Hi friends - This one is for the Canadians - eh? :-) published today in The Breach. See original here.

To be clear the AI bubble may or may not burst this week.. but I think it’s pretty locked in. It is a bubble. It will burst in the near future. Government’s number one job on all things AI should now be to protect society from AI fragility not to keep heightening that fragility.

Nov 18 2025

While Canadian politicians are still caught up in their AI love affair, panic is rippling through the industry—and the AI bubble may be about to burst, with massively damaging consequences for Canada.

Billionaires are bailing on AI stocks, and even tech giants are preparing for a correction that could send a colossal tremor through markets and public pension funds.

Over the weekend, it was revealed that Peter Thiel, the billionaire founder of software firm Palantir, dumped all of his personal investments in Nvidia, the U.S. chipmaker whose products make generative AI possible.

By betting on that company’s sale of chips to Google, Meta, Amazon and OpenAI, investors have pumped up Nvidia to the extent that it is single-handedly responsible for over a quarter of all gains in the stock market over the past three years.

Nvidia is now the world’s first-ever company to be valued at $5 trillion—a staggering overvaluation that many have predicted will be “corrected,” perhaps dramatically.

The news that Thiel, who has been called the “dark lord of Silicon valley,” decided against keeping any Nvidia stock in his own portfolio (it had made up nearly 40 per cent) may become one of the factors hastening that now inevitable correction.

Thiel’s exit happened only a few days after Japanese giant Softbank publicly withdrew its entire $5.83 billion investment in Nvidia. It also comes a couple of weeks after charismatic investor Michael Burry (known for making $800 million by betting against the housing market during the 2008 recession, inspiring the film The Big Short) placed his own billion-dollar “short” against both Nvidia and Palantir. He then shut up shop to wait out the coming crash.

Deutsche Bank, which is apparently also exploring shorting AI stocks, has warned that without the oodles of money currently being spent by AI companies to build data centres filled with pricey Nvidia chips, the U.S. economy would already be in deep recession.

It turns out that just a few tech giants splashing out on data centres, electricity, and chips are now outspending the entire American consumer population.

Unfortunately for us ordinary Canadians, who don’t have multi-billion dollar hedge funds to play shorting games with, the prospect of an enormous implosion of AI stocks might prove profoundly consequential.

Minister of Artificial Intelligence and Digital Innovation Evan Solomon with Prime Minister Mark Carney. Credit: Evan Solomon/X

Ottawa is doubling-down instead of pulling back

At the end of September the Canadian Pension Plan Investment Board (CPPIB), to which all working Canadians outside Quebec are subscribed, held $8 billion of Nvidia stock as its largest single equity holding. In fact, its eight largest stock holdings were all AI stocks. At $33 billion of combined value, these holdings amount to a quarter of the value of the entire pension fund.

Quebec’s main pension fund, La Caisse de dépôt et placement du Québec (CDPQ), also reported Nvidia as its largest holding at the end of September. Almost all of the “Maple Eight” big pension funds also lead with Nvidia and other AI stocks as their largest holdings, or they are loaded up on index funds that are in turn overloaded with Nvidia and other AI stocks.

What happens then to the financial security of ordinary people if the value of these stocks collapses overnight? According to Deutsche Bank, there is “no playbook” to deal with the potential fallout from an implosion in AI stocks. The Bank of Canada, meanwhile, says it doesn’t know what the wider impact of an AI correction would be.

This isn’t good enough. Until federal and provincial governments can clearly explain what an AI crash would mean for 40-plus million Canadians, public servants and pension managers should already be moving to follow Thiel, Softbank, and Burry, acting now to shield the rest of us from the increasingly likely collapse of this bubble, whether it hits soon or years from now.

Unfortunately, the Liberal government, captivated as it is by AI hype, is moving in the opposite direction. In the recent federal budget, Prime Minister Mark Carney poured a billion dollars into expanding Canada’s AI infrastructure and pushing government departments and everyone else to go “all in” on generative AI.

And that doesn’t include the additional funding funneled into fossil and nuclear energy projects to power AI data centres, or the money earmarked for extracting the so-called critical minerals needed to build the chips and transmission lines that feed them.

Generative AI is incredibly energy-intensive. The data centres that power AI applications consume massive amounts of electricity, water, and minerals, fuelling environmental strain and local resource pressures. Credit: Wikimedia Commons/BalticServers.com

Our Big Tech Prime Minister

The public may know Carney as a former banker. What’s far less obvious is that he’s also a former Big Tech executive, and still very much acts the part. As vice-chair of Brookfield Asset Management, he helped position the company as a top global investor in AI data centres. Brookfield has only doubled down since, unveiling tens of billions of dollars in new AI infrastructure investments on top of what it already holds.

The voracious appetite of data centres for energy, water, and minerals is sparking a global backlash from communities and environmental groups, and it’s already driving economic hardship even before the AI bubble threatens our pensions. Because most new data centres are being built near major cities, with many drawing hundreds of megawatts of power, utilities are scrambling to expand generation capacity while offering lucrative deals to the operators.

The result is rising electricity costs for households, as ordinary consumers effectively subsidize some of the world’s richest tech companies. As Daily Kos notes, when it comes to affordability, “everyone hates data centres.” Canadians in Alberta, Quebec, the Greater Toronto Area, and B.C.—the regions seeing the most aggressive build-out—should be especially alert.

Among his tech roles, Carney also sat on the board of tech payments giant Stripe, a firm whose leadership is close to and funded by Thiel. Both Thiel and Stripe founder Patrick Collison profess an ideological creed known as “state capacity libertarianism,” which argues for governments to take on the financial and infrastructure costs of emerging technologies so that venture capitalists can be less encumbered to speculate for private gain. In other words, socializing risk and privatizing gain.

Over the past few weeks alone, several AI leaders, including OpenAI CEO Sam Altman, began hinting that they expect governments to step in and bail out AI companies if and when the bubble bursts. After all, the U.S. government did exactly that in March of 2023 when Thiel’s last big withdrawal of funds (from Silicon Valley Bank) precipitated a series of bank collapses.

With so much pension money and other public funds riding on AI stocks Carney could also decide to bail out AI giants and data centre builders with public funds, rather than take a precautionary tact and pull out investments now from over-bloated AI firms.

Shielding Canadians from the AI crash

If the government rides in to save AI companies, this would miss the bigger picture and only punish working Canadians. All the blind faith and sometimes childish excitement that Carney and other political leaders (such as Minister of Artificial Intelligence and Digital Innovation, Evan Solomon) for generative AI stems not only from the eye-popping, yet illusory, financial gains, but also the crass misunderstanding that generative AI is somehow “intelligent” and thereby socially valuable

The financial tremors shaking the foundation of the AI bubble is a five alarm fire bell ringing for Canada’s economic stability and the security of ordinary Canadians. By uncritically embracing the hype, our policymakers are sleepwalking into a dangerous state of AI fragility—a vulnerability that extends far beyond our collective pension funds to the core of our economy, environment, and public services.

Ignoring this looming financial threat and the already catastrophic strain on energy, water, and social trust is no longer an option. The first order of the day must be an urgent national mobilization to assess, insulate, and protect our country from the now-towering likelihood of a collapse, however soon or long it takes.

To move from denial to defence, the government must immediately introduce and fight for legislation that insulates our pensions, public funds, and public services from this systemic risk, convening the Bank of Canada and pension managers to prepare for an AI-driven economic crash.

Concurrently, the government must start a national assessment, launching a comprehensive evaluation across the five key areas of fragility: finance, economy, environment, democracy, and psychological well-being. Ottawa should also immediately implement a spending and infrastructure pause, halting the approval of new AI data centres and stopping new departmental spending on AI until the assessment’s recommendations are delivered.

The stakes are too high for cautious delay. Our officials must stop acting as cheerleaders for Big Tech’s speculative frenzy and take swift, protective action to shield the financial security of more than 40 million Canadians from the bursting of this speculative bubble.

‘Hoodwinked in the Hothouse: Resist False solutions to Climate Change’ is a near legendary zine in Climate Justice circles. Published in multiple languages and distributed to grassroots communities throughout the world, ‘Hoodwinked’ lays out just the essential facts on what technofixes and neoliberal financial schemes are false solutions that perpetuate systems of extraction and exploitation . The booklet helps to refocus activism towards real solutions for climate justice.

The fourth edition of ‘Hoodwinked’ is being finalized right now. As the global climate summit gets underway in Belem, Brazil, the Hoodwinked Collective have decided to pre-release the chapter on ‘AI as a false climate solution’ for movements to already print and use. I was honoured to be asked to write this short chapter for such a venerable and important publication. You can see or download the designed version of this new chapter here on the Hoodwinked website or just read the text below.

Please do share and use this short primer for popular education and activism. I also encourage everyone to read the full ‘Hoodwinked’ zine (currently available as its third edition but the fourth is imminent), order copies or donate to the collective to support this important resource.

Enjoy!

ps. For a refresher on different varieties of ‘false solutions’ you may also enjoy this article from two years ago.

AI as a False Climate Solution

written by Jim Thomas

In a world where students and scammers employ chatbots to cheat and deceive, it should be little surprise that greenwashers are selling artificial intelligence (AI) as a way to fake climate action. The world’s richest tech giants, such as Google, Meta, Microsoft and NVIDIA, are selling an “AI for climate” fantasy that prophesies their AI services concocting magical panaceas for the climate crisis. In reality, a massive speculative AI boom is fueling a huge increase in emissions, extraction and multiple harms across society.

What is AI?

Unlike the sci-fi version, today’s so-called “artificial intelligence” is not really “intelligent.” Computers analyze trillions of bits of data to produce responses a user may wish to see in an elaborate guessing game. Generative AI models (e.g., ChatGPT) calculate outputs that assemble “new” pictures, text or video using statistical trends gleaned from human-made writings and art (complete with built-in errors known as “hallucinations”). Trillions of dollars are being invested to build out AI infrastructure across the entire economy.

The Artificial Promise of “AI for Climate”

Can a chatbot save the climate? No, but Big Tech are creatively inventing scenarios in which AI might seem to shave the edge off climate emissions, including:

• Air transport: Using AI to redirect the path of airplanes to prevent contrail formation and reduce fuel use.^[1]

• Industrial processes: Using AI to redesign manufacturing to use less energy or lighten materials.^[2]

• Digital agriculture: AI-directed industrial farming to increase storage of CO2 in soils or reduce fertilizer use.^[3]

• Novel proteins: AI-designed genetic engineering for new “alt-proteins” to replace meat or as enzymes for “greener” industrial chemistry.^[4]

• Materials: AI-designed materials for sequestering CO2 or replacing fossil-based materials.^[5]

Like AI itself, these “solutions” are mostly error-riddled hallucinations.

1. Massive energy use: Digital technology has become an outsized user of energy with emissions higher than the aviation sector,^[6] and those emissions are being supersized by AI computation. A typical AI data center consumes as much electricity as 100,000 households.^[7] Data centers currently consume 1-2% of global energy – and this is expected to rise to 3-4% by 2030.^[8] The electricity used by data centers runs computers and cooling fans but an additional matching amount is required to send data across networks and power devices. This energy demand is largely met by fossil fuels, extending the life of coal and gas plants. The AI boom is also justifying new investment in nuclear power. Microsoft recently signed a deal to restart the notorious Three Mile Island nuclear plant to help power its data centers.^[9] Tech oligarchs are also investing in small modular nuclear reactors to help power AI.^[10]

2. Water, minerals, pollution, noise: Data centers each require between 300,000 and 4 million gallons of water per day to cool overheating computers.^[11] Even more water is used in electricity production. This deprives farmers and poor communities of essential water supplies, especially in Global South countries. The millions of racks of AI computers are constructed from mined minerals including silicon, gold, cobalt, nickel, copper and rare earths. Increased demand for these is driving land grabs, violent conflict and human rights abuse on Indigenous and peasant territories as well as additional climate emissions. The same components later become hazardous electronic waste. Data centers continuously run diesel generators causing significant local pollution and noise.

3. Doubling down on harmful industries: Many of the “AI for climate” schemes are about making already harmful industries more efficient and viable. Far from reducing harm, bringing efficiencies into such industries only encourages their growth. While AI boosters promise emissions reductions, the same companies are selling AI as a way to push up oil and gas production, expand industrial agriculture and intensify mineral extraction.

4. Oligarchs, militaries and surveillance: Imagined “AI for good” applications are dwarfed by the dangers of enabling corporate and military elites to control populations, workers and the economy. AI originated as a weapon of war and has become a defining feature of such conflicts as those in Gaza and Ukraine.^[12] Its use depends upon surveillance, data collection, and theft of creative labor. Algorithmic bias and discrimination has been extensively documented as AI systems exacerbate racial, gender and other injustices.^[13] In authoritarian countries (such as the U.S.) tech CEOs are now aligned with MAGA-style politics, enlisting AI to help dismantle social services, increase surveillance, imprison migrants and crush workers.

For More Information

Organizations:

• AI Now

• Beyond Fossil Fuels

• ETC Group

• Green Screen Coalition

• MediaJustice

• Securing Indigenous Peoples’ Rights in the Green Economy (SIRGE) Coalition

Blogs and Podcasts:

• Blood in the Machine

• Scan the Horizon

• Tech Won’t Save Us

[1] Elkins, C., & Sanekommu, D. (2023, August 8.) How AI is helping airlines mitigate the climate impacts of contrails. The Keyword. https://blog.google/technology/ai/ai-airlines-contrails-climate-change/

[2] Mehta, M. (2024, June 27). How manufacturing with AI can drive a sustainable future. World Economic Forum. https://www.weforum.org/stories/2024/06/how-manufacturing-with-ai-can-drive-a-sustainable-future/

[3] Rowe, J. (2025, January 6). Delivering regenerative agriculture through digitalization and AI. World Economic Forum. https://www.weforum.org/stories/2025/01/delivering-regenerative-agriculture-through-digitalization-and-ai/

[4] Graham, J. (2024, November 18). Q&A: Bezos Earth Fund CEO on how AI could help climate and nature. Context. https://www.context.news/nature/q-and-a-bezos-earth-fund-ceo-on-how-ai-could-help-climate-and-nature#

[5] Prest, J. (Host). (n.d.). Shaping the future of material design with AI (No. 2) [Video podcast episode]. In Energy Futures Podcast. Energy Futures Lab. https://www.imperial.ac.uk/energy-futures-lab/energy-futures-podcast/episode-2—shaping-the-future-of-material-design-with-ai/

[6] Lavi, H. (2025, July 17). Measuring greenhouse gas emissions in data centers: The environmental impact of cloud computing. Climatiq. https://www.climatiq.io/blog/measure-greenhouse-gas-emissions-carbon-data-centres-cloud-computing

[7] IEA. (2025). Energy and AI. IEA. https://www.iea.org/reports/energy-and-ai

[8] Goldman Sachs. (2024, May 14). AI is poised to drive 160% increase in data center power demand. https://www.goldmansachs.com/insights/articles/AI-poised-to-drive-160-increase-in-power-demand

[9] Sherman, N. (2024, September 20). Microsoft chooses infamous nuclear site for AI power. BBC. https://www.bbc.com/news/articles/cx25v2d7zexo

[10] See for example Oklo Inc backed by OpenAI founder Sam Altman.

[11] Mann, T. (2025, January 4). How datacenters use water – and why kicking the habit is nearly impossible. The Register. https://www.theregister.com/2025/01/04/how_datacenters_use_water/

[12] Férey, A., & de Roucy-Rochegonde, L. From Ukraine to Gaza: Military uses of artificial intelligence. Ifri.

[13] UNESCO, & IRCA. (2024). Challenging systematic prejudices: An investigation into bias against women and girls in large language models. https://unesdoc.unesco.org/ark:/48223/pf0000388971

You may have heard this one before.

In a far away land a bit like this one, there was an Emperor who didn’t feel quite as confident or clever as he liked to project.

(After all he was a human being - which doesn’t fit easily with being an “Emperor”)

One day some tinker-tailors turned up in the Emperor’s court. Actually there were rather a lot of them - more like tinker-tailor-lobbyists. They wore relaxed casual suits and sneakers and they seemed pretty hip.

They were associated with a new kind of tech called ‘generative clevertech’. They claimed their ‘clevertech’ was really the cleverest tech that there has ever been and that if you used clevertech right you too could be incredibly clever.

They showed the Emperor some of the whizzy things generative clevertech could do: - it could write speeches, tell some funny jokes, organise the court a bit better, replace the Lord Chancellor and even help the treasury staff by making budgets a little easier to make.

The Emperor played around with a few clevertech apps. He laughed at the jokes. He found it was actually comfortable to wear sneakers. He began to feel a little bit cleverer.

“it’s good eh?” said one of the tinker-tailor-lobbyists. “Imagine if you let it help you run the whole country? I mean.. it IS that clever. You could even ask it how to run the country in a clever way. It probably has some clever ideas”

That seemed like a clever idea indeed, so the Emperor asked the clevertech app and it had a few suggestions: It suggested the Emperor could get the Health Minister to use clevertech to run all the hospitals more efficiently. He could ask the Agriculture Minister to use it to tell farmers how to be more clever in growing food. Heck, you could even tell the schools to let all the kids each have a bit of clevertech so that the whole country becomes really clever and that would help the economy… somehow.

“Oh and your defence department” said one of the tinker-tailor-lobbyists looking over his shoulder. “They are really going to need clevertech to be cleverer than your enemies because unfortunately ..sorry to tell you this.. but your enemies are already using clevertech to work out how to undermine and attack your country”

“Wait!” said the Emperor, now quite alarmed. “They are? That’s serious - I’m glad you warned me. This isn’t just a cool thing that tells clever jokes.. This is actually pretty important”.

“I’d say” said the tinker-tailor-lobbyist suddenly looking a little less casual and leaning in with a worried look. “The thing is, if your enemies use it the wrong way this clevertech could be pretty nasty. It could destroy everything. It kinda needs a clever guy like you to use it better and quicker and cleverer than the bad guys - you know?, both to do the good things and for stopping the really bad uses of clevertech. “

The Emperor got it immediately. This mattered. He was glad he’d met these cool tinker-tailor-lobbyist dudes, They might have just saved everything. “Ok,” said the Emperor - feeling more certain and important than he’d ever felt, “We are going all in on this clevertech thing. I mean ALL IN!!”. He even made up an acronym for ‘All In’: A.I. That felt pretty clever.

First he asked the clevertech app to help him rearrange the budgets so he had the money to buy clevertech. The clevertech app suggested that he stop giving money to foreign aid. “You are going to need to buy a lot of clevertech to get up to speed here” explained the clevertech tinker-tailor-lobbyists “ which will be a little pricey. But hey! consider it a security expense. In fact you might want to give a bit more money to the military too so the generals can be first to buy lots of clevertech”

Buying clevertech for the treasury, the health department, the education department, the king’s wardrobe, the energy minister and the cooks all came next. “Phew, This is a lot to organize.“ thought the Emperor. Luckily the tinker-tailor-lobbyists always seemed to be at hand with good ideas to help him out “Appoint an ‘All In” minister to do this work.” they suggested. “He doesn’t need to be too clever himself because he can just follow instructions from the clevertech app.”

There was an out-of-work journalist hanging around the court. He had sold the Emperor some paintings in the past that he quite liked. “How about you?” asked the Emperor tapping the journalist to be his new ‘All In’ (AI) Minister. “Cool” said the journalist, hurriedly combing his hair and wondering if he had any casual suits to wear.

The Emperor and his new AI minister were soon in full flow of organizing what they grandly called the ‘Clevertech All In (AI) Revolution’ for the court and country. Everyone in the court realized they quickly had to get on board (“All in”, right?) with this new obsession by the Emperor . After all he was the boss and it was the cleverest tech there had ever been and they didn’t want to look stupid.

Several courtiers got hold of clevertech apps and asked the apps to come up with ideas for how they could use clevertech to deal with labour costs, sell more things and to organise the court arts concert that evening. They brought each of these things to the Emperor to show him they were being clever and “All In” too. Each time someone did so it became more and more obvious to the Emperor that the court and the country really needed clevertech - like lots of it - especially for that whole thing about keeping ahead of their enemies.

One of the tinker-tailor-lobbyists had explained to the Emperor and the court in general that the country needed to amp up things even more to reach a state of “general cleverness” before an enemy country managed that first. “We hear they are even working on ‘super-cleverness’ explained that tinker-tailor-lobbyist in awed tones. “We need to get to super-cleverness first” expressed the Emperor, feeling resolute and clever.

“Er.. thing is..” said one of the tinker-tailer-lobbyists looking a bit sheepish, “With all this extra AI clevertech activity happening here, now we are sort of running out of clevertech. We are going to need a whole lot more clevertech factories full of expensive clevertech computers running full time. We’ve got people who will pay for them and sell them- clever bankers and all that - but we might need subsidies and land. And oh there’s getting hold of the energy, the water and the metals. Clevertech is built out of energy and water and metals, don’t you know?

“Oh.. I didn’t know that.” thought the Emperor. And then, realizing he was supposed to look clever, said “Yes, of course no problem. Go ahead and build lots more clevertech factories across my empire. Dig out more metals! Pump more water! Amp up the energy production! - Oil! Coal! Gas! Nuclear! whatever it takes. This is serious. We need to stay cleverer and ahead of our enemies. All In!”

Outside the court, in the streets of the city, the ordinary citizens couldn’t help but notice all this frantic ‘All In’ clevertech activity suddenly going on. The kids at school were being sent home with clevertech apps. The hospitals were laying off doctors and nurses so that they could have clevertech do the doctoring and nursing instead. The border police had suddenly got very good at finding foreigners who looked different and rounding them up . The clevertech tinker-tailor-lobbyists had also been fanning out from court to talk with the bosses in the factories and the shops. So suddenly workers found that they too were being expected to do whatever a clevertech app said they should be doing.

Beyond the city, rivers were being redirected to cool down the new clevertech factories that were being built at the edge of town. These huge factories were very noisy and billowed pollution from the energy turbines - because clevertech, it turned out, needed a lot of energy and all they had on hand was smoky, noisy coal, oil and gas.

A few people discovered there were some other types of clevertech factories where poor people were being badly paid to tell the clever tech exactly what to say in the first place.

“Wait - you mean it’s not actually clever by itself?!” people asked incredulously.

It turned out the clevertech needed to keep being told what to say and not to say and what to do and draw. The artists found out that clevertech was stealing their pictures and pretending it had made them itself. The musicians found the same thing was happening with their music, the farmers found the same thing with their seeds.

The further you got away from the court into the far reaches of the kingdom the clearer it was that clevertech was mostly just a network of copying machines organized to steal other peoples words, ideas , songs, seeds and labour and present it as if it was some sort of new clever thing to the court.

In one of the villages where the air was smoky from the clevertech factories , where the water had dried up because it was all going to clevertech factories and what rivers were left had been polluted by a spill from the now frantic metal mines …. in that village a young girl had had enough.

She saw how the clevertech apps they sent home from school were not in fact making her friends any cleverer - just sort of lazier and finding it harder to think for themselves. She had watched helplessly how one of her friends had become horribly addicted to asking the clevertech apps questions again and again until he got really depressed and committed suicide. Ugh!

She saw that about a third of the time the clevertech apps were just sort of making things up and getting their facts wrong. She didn’t quite understand how these apps that took away her friend and spouted lies were of any help or fun or meaningful or hopeful to her life whatsoever.

“This is bullshit” said the young girl exasperated. “This isn’t clevertech - it’s stupid tech: dumb-and-stupid overhyped bullshit tech! I’m gonna go to the court and tell that foolish Emperor with his ‘All In’ slogan to just.. stop it”.

She grabbed an old bed sheet and some paint and painted up a banner. It read “Clevertech is Bullshit!”.

It wasn’t polite or eloquent or nuanced or clever. But it was true.

Setting out, carrying her banner, she left her home. She took a breath of air, found a trickle of clean water to fill her bottle and set off for the court holding her banner high. As she walked people looked up from their clevertech apps.

Some were puzzled but a lot smiled. They looked at the smoky air and the dried up stream and the depressed kids and some of them shook their heads and turned off their apps. Some of them even grabbed their own banners and joined in beside her: “It’s not clevertech, it’s stupidtech” , “It’s not clever if I can’t breathe”, “All In? We’re out!” “The Emperor has no clue!”.

Back at court the Emperor and his AI Minister were beginning to sweat. They had used up all the gold coins in the treasury buying clevertech and setting up new clevertech factories. They now had nothing to pay for all the hospital costs of people who were having health problems or were hungry and dying from thirst because the water had been diverted away from agriculture and poorer communities.

Word had got to the court about the marching army of angry ‘stupidtech’ activists who were coming. The generals said their clevertech apps suggested that the clever thing to do was to mow them all down with machine guns before they arrived in the city. The Emperor didn’t feel good about this clever idea so he was dragging his feet on approving it.

In the city people were getting restless because many didn’t have jobs anymore or they had been fired and re-hired for less money to correct the clevertech when it went wrong or said offensive things. . Some of the banks had collapsed because they lent out too much money for building clevertech factories. The tinker-tailor-lobbyists meanwhile seemed to have all become scarce. Word was that they had built a few bunkers in New Zealand where they were having a huge blowout party - or hiding away - nobody quite knew.

“Have we reached general cleverness yet?” asked the AI Minister sounding very alarmed. “If we haven’t we are going to need a lot more money and energy and water and metals for a lot more clevertech. Especially the energy.. the power grid is really overloading and..,,”

Suddenly all the lights in the court went out.

All the clevertech apps suddenly stopped working.

Everything was in darkness and confusion and somewhere outside in the distance was the faint sound of people marching - of people chanting and getting closer and closer.

The Emperor listened in the darkness, in the sudden unusual quiet, to the singsong chanting of the voices as they got closer. He could make out sentences:

“Unplug the empire! Turn off the apps! We want our minds and communities back!”

“its not clever! its not cool!. Clevertech’s for chumps and fools.”

And he realized.. he really was.

.———————————

If you enjoyed this fable please pass it on and share it far and wide. We all need to start calling bullshit on the toxic claims of the ‘Empire of AI’.

Generative AI is artificial but it ain’t intelligent.

• You may also enjoy another re-purposed tech fairytale that I wrote a few years ago called Jack and The Cloud Giant - Also available as a short audio story.

Hi friends, I’m posting here excerpts (and a video) from a long interview I recently gave to Lynn Fries of GPE NewsDocs in the aftermath of the recent Cali CBD Cop16.

Lynn is a former financial professional and an experienced journalist and producer whose online channel, GPE NewsDocs, is a great place to find reflective, thoughtful long-form exploration of Global Political Economy news and analysis. She interviews various folks there who are tracking the financialization of the natural world, the power of corporations, the abuses of agribusiness and more.

Lynn had already shared on her channel a webinar on ‘Black Box Biotech’ (generative biology) hosted by the African Center for Biodiversity (see here and here). I’m very appreciative to Lynn for this chance to dig deeper into the implications of the discussions and outcome from the biodiversity convention’s negotiations. In this interview we explore the links between financialization of nature and new genetic technologies, artificial intelligence, synthetic biology, generative biology, biosafety and biopiracy.

The full interview is just over an hour long and is available online (with a full transcript) here: https://gpenewsdocs.com/big-finance-big-tech-ai-titans-ride-next-wave-of-colonization-at-cop16/

But for those who don’t want to watch or listen to an entire hour of conversation I’ve made some excerpts below to browse through and give you some sense of the discussion.

-----

How Financialisation of Nature is being tied to risky new technologies

“We’re seeing a sort of a new agenda at the COP at the CBD which has really come across from the climate COP. But is also where tech companies, finance companies are seeing a new opportunity here.

They want to turn biodiversity into new financial markets. They’re thinking that they can set up biodiversity markets and biodiversity credits. Just like we have carbon markets and carbon credits.

In order to achieve that, they’re bringing in new technologies, new monitoring technologies, new genetic engineering technologies. And so in some ways the CBD is now becoming a marketplace for quite cutting edge digital and genetic engineering technologies as a way to manage the problems of the environment and biodiversity collapse.”

On creating a new bioeconomy:

“One of the things that’s very clearly on display in Cali at the COP was this hope by governments, including big South governments such as Argentina, Brazil, Paraguay, that they would be able to create a new bio-economy. That by using genetic engineering, artificial intelligence, synthetic biology, they would create a new high tech economy that would let them sort of leapfrog ahead.

And the promise is there as well from financiers, from big philanthropists like the Bezos Earth Fund, that the way to get beyond the current carbon economy, the fossil fuel economy, is to create a high tech economy based on new technologies, genetic engineering, and artificial intelligence.

And so this was on view. This attempt to create a sort of high tech, financialized version of nature and the economy around nature was part of what’s going on”

On how and why big tech is driving generative biology:

“It is large tech companies such as Microsoft, Google, NVIDIA, Alibaba, and Salesforce. They’re the ones who are now setting up these platforms which will design proteins, which will design viruses that will design RNA, which will design organisms.

And then partnering with chemical companies, with pharmaceutical companies, with food companies. And they certainly don’t have any history on carefully managing bio safety risks.

What they do have a history of is very successfully creating monopolies, very successfully moving ahead of government regulation and and getting their their technologies out there into commercial use before any kind of controls can be put in place.

So, that’s worrying to see literally the world’s most powerful, well capitalized companies jumping on this bandwagon.

In part, I think they’re doing it because their AI platforms, whether that’s ChatGPT or Gemini and so forth, aren’t delivering much. They’ve spent billions of dollars, in fact almost trillions of dollars, building out AI platforms that have really just created a few chatbots.

And the financiers are saying what are we getting for our money? So they need to be able to show that ultimately they’re going to get drugs. They’re going to get foods. They’re going to get new materials. They’re going to be able to create energy solutions.

So that’s why they’re moving into this. But they’re doing so at a pace and with a lack of accountability, that’s really quite scary.”

On how AI/synthetic biology convergence may overwhelm regulatory capacity:

“One of the brakes on new genetic engineering organisms impacting biodiversity has been that it’s slow at the moment. It’s been slow to create a new genetically engineered organism to get it out into the environment.

And that’s what’s changed with synthetic biology and now with artificial intelligence. It’s now increasingly quick and easy to generate new genetic codes that somewhat seem to work. To transfer them into living organisms, whether that’s bacteria or into viruses or into other organisms such as plants and animals.

And increasingly, there’s a focus on putting them into organisms that are going to be in nature, whether that’s insects or bacteria and so forth.

So the danger is that with the use of artificial intelligence to design increasingly novel organisms, you’re going to see many more synthetic organisms being released. Certainly more than biosafety regulators are able to easily regulate.

And also, we’re seeing a big focus on creating new proteins that you can genetically engineer. Novel proteins, whether for food or for materials or for drugs that never previously were possible. Those too, there’s a concern coming that those could over overwhelm regulators or just be produced without regulation.

So we’re sort of at a tipping point if not already soon, where we’re going to see a volume of new entities whether that’s proteins, whether that’s organisms or viruses being produced – for the marketplace, for environmental release and honestly also for other uses – that the biosafety regulations aren’t really capable or have the capacity to deal with.”

On how synthetic biology and AI enable the wrong type of food system transformation:

“ I suppose in the face of all of the many threats to biodiversity, the climate and so forth, there are different routes you could go for food and agriculture and sustainable development. One is to really support systems such as agroecology where you’re supporting communities on the land to use their own knowledge and their own techniques appropriate to their own place.

Where synthetic biology, artificial intelligence, and new technologies fit is a very different route. It’s a sort of high tech control vision. That in face of all the threats to the biosphere and to biodiversity, states and companies will create a sort of high tech control and command of food production, of biodiversity, of carbon capture and so forth.

And pushes the power and agency of communitiesout of the way. Their land becomes a resource for genetic information that feeds the AI systems. It also becomes necessary to control their lands and territories in order to grow enough food in an industrial food chain.

And their knowledge so far as it’s relevant has been hoovered up by artificial intelligence and used to generate commercial products that will be sold back to them.

So I honestly think where this heads is that the knowledge of communities of indigenous people, of farmers, of fisher folk gets more and more marginalized if you build your food systems, your health systems, your environmental systems around these high tech fixes”.

On the Cali Fund on digital sequence information (DSI):

“This fund is around what was called digital sequence information - that is the digital version of the genetic sequences. The DNA sequences fund is now going to be called the Cali Fund.

There were continual day and night negotiations for two weeks to try and get this Cali Fund established. And it was established. And industry not only has to pay into this, but thinks they’re going to get money out of this.

They hope to set it up so that they can use that money to grab more DNA, for example. or to train people in using genetic engineering. So it’s a two edged sword.”

On the fundamental “deal” behind Access and Benefit Sharing (ABS):

“It’s true that the kind of the fundamental bargain that was agreed at the beginning of the Convention, the U. S. and other industrial countries, were saying: Well, we hope we can make a market in this stuff. That by agreeing a benefit sharing arrangement for genetic resources, DNA, and so forth will become part of a market.

And that is now what’s on full display. This idea that the South should hoover up as much of its genetic resources as possible. And in Cali we had a number of companies there who were offering to do what’s called eDNA scans. They were offering communities that they would just be basically sampling again and again environmental DNA.

And that in turn will be going to a fund where you’ll get a little bit of money off the back. And all of that will run these artificial intelligence platforms that will create new drugs, new plastics, new materials, new foods. Which will benefit, frankly, largely the North.

That’s now the full game. It’s how can nature become a source of commercial opportunity for banks, for tech companies, for Northern investors. And the South will provide the underlying resources, will provide the genes, the DNA and the biodiversity, and get a smallbit of money in a fund, such as the Cali Fund, off the back of that.

So, that’s sort of the restatement of what was originally struck as a deal 25 years ago, 25 to 30 years ago. Now it’s quite naked that this is about trying to create a different economy. Potentially a post fossil fuel economy is how it’s presented, an economy that’s supposedly about nature based solutions. It’s a green economy.

All of these things were being said very loudly by, for example, the Bezos Earth Fund, which is, of course, the largesse of Jeff Bezos who’s one of the major investors in this.

And some of the large South governments – Brazil, Argentina, and others – are happy to go along with this vision in the hope that they might build high tech sectors along the way.”

On AI and generative biology as a new form of colonialism:

“Many of the groups who are at the convention meeting in Cali, indigenous groups, civil society groups, women’s groups, were saying quite loudly that what’s going on is a new wave of colonialism.

That through things like biodiversity markets and new technologies, the same power players, whether that’s financial players or tech players or large Northern industrial countries are trying to grab power over territories, over life and even over people’s culture.

Colonialism always comes (whether it’s with gunboats or with debt) with a narrative that what’s on the territory is worthless. Whether that’s human lives or foods or now genes and biomass. That all of this stuff isn’t really worth anything. And it should be handed over in exchange for trinkets. In this case, new technologies, little bits of money in a fund.

That in exchange for those trinkets the South should now hand over its biodiversity. Or should put large areas of its biodiversity in sort of fenced off spaces that will be controlled by Northern Conservation NGOs, which is the other thing that’s going on.

I think now that the technology is there to sequence, to take codes, from every living organism and put it into artificial intelligence models and generate new products in the North, they’re sort of offering little bits of money (trinkets of money if you like into this fund, the Cali Fund) and promises that the South might get some of this technology through technology transfer in order to grab as much as possible of this genetic resources, of this biodiversity.

And that that becomes the underlying resource for Artificial Intelligence companies like Google or Microsoft or Nvidia or large pharmaceutical companies, whether that’s Pfizer or Johnson and Johnson or agribusiness companies like Corteva and Bayer.

The North wants to make sure that they have unfettered access to as much genetic resources and biological diversity as possible to build out this different economy.

So it is, the same story that we see again and again. You come for territory, you come for human bodies, you come for food and commodities, and now you come for genetic commodities, the next phase of colonial exploitation.”

On whether the Cali Fund and AI breaks the link with Access and Benefit Sharing?

“LF: You said the Cali Fund breaks the connection between those who looked after genetic resources and those who are exploiting those resources. Talk about the role played by what you call black box biotech in undermining the previous CBD agenda to avoid biopiracy, so the appropriation of biological resources or traditional knowledge without proper compensation or consent.

JT: Yeah. So, originally, the way in which the Convention on Biological Diversity set up the question of what they call access and benefit sharing over genetic resources was they asked for a Memorandum of Understanding.

That if you take a seed or a sample from one place, from one community and you’re going to carry it across the world and give a biotech company, then you have to have a sort of paper trail. And a Memorandum of Understanding of where that specific DNA sequencing came from and went to such that benefits could go back. It was creating a paper trail.

This has become harder and harder to track as you have large databases where you’re not moving digital material. It’s all being uploaded digitally into very large databases that are held by the U. S. government or the Japanese government.

And then other companies will come in and scrape off of that. But you still could, absolutely could, track where the data they’re taking comes from and where it ends up. This is all possible.

Where it becomes even more complicated, however, is when you start to introduce artificial intelligence platforms. So the artificial intelligence platforms that are now coming out for designing genetic material, so called generative biology, what they do is they scrape all the DNA data from all the databases.

They use it to train an artificial intelligence model. That model has millions, sometimes billions of different variables. And then you ask a question of it and it generates a brand new novel, supposedly, piece of DNA or a brand new novel piece of protein, a sequence for protein.

And what the companies will often say is because this artificial intelligence process is so tremendously complex – the many variables and the weights within the model – it effectively becomes a black box.

You can’t just track a line between the data that comes in and the new novel data that comes out, the so-called synthetic data.

And therefore, the idea that you’re going to be able to say that this invented piece of DNA comes from these other pieces of DNA that were taken from, you know, the South Pacific or from North Africa, it begins to break down within that model.

Now, that then becomes an argument for this sort of general fund. Which then says: okay, if we can’t trace it within the model, then we’ll have a general fund. Anyone who uses this will pay into that fund. And that fund will pay to indigenous people and to farmers and so forth.

So that’s the way in which this black box nature of artificial intelligence accelerates what’s happening here.

Interestingly enough, in Cali I met with some of the artificial intelligence companies who were there and were lobbying. And they said that they think they can trace. They believe that they actually do trace. And so it may be that the black box can be circumvented.

It may be that you could request that an artificial intelligence company building one of these models has to be able to trace. And that’s more work for them, but that would somewhat enforce justice.

It would somewhat ensure that if you’re designing a new genetic sequence, you have to prove where it comes from. This is what’s known as explainable AI.

So, it may not be entirely impossible, but this is exactly the sort of thing that needs to be looked into.

…

if a company shows that they made an agreement and that they promised that they were going to give some kind of benefit back to a community, that’s legal biopiracy. And peasant communities, indigenous communities have said this is an unfair system to begin with.

You know, that if somebody breaks into your house steals your television set and on the way out says: It’s alright. I’ll give you a benefit. That’s not necessarily something you’ve agreed to. It’s something that you kind of have to deal with.

And that’s how many communities feel. Often, DNA was taken from them. Samples were taken from them. Often it was taken decades or even centuries ago. Collecting for botanical gardens, for example, without them understanding or agreeing to have the many ways in which it could be used.

And now they’re being told: It’s alright; we’ll give you some benefit. You’ll make some money out of it. But they have lost sort of sovereign rights and control over the use of the resources that they’ve looked after. That they’ve developed.

So this is why it’s such a contentious and highly emotional topic especially for indigenous and peasant communities. This is about the very resources that their lives and cultures depend upon.”

On axing precaution – how countries choose to cover their eyes to impacts:

“The same countries who really want to financialize biodiversity; who really want to take advantage of this new Cali fund; who want to have these new technologies transferred to them were blocking the opportunity to assess or do horizon scanning or monitor these technologies.

Effectively asking that the Convention stops; sort of covers its eyes with the impacts and just takes the money to develop the technology. And that’s what the industry wants.

The industry wants this Convention not to be a critical reflective space to properly oversee and regulate biotechnology but to be a promotional space where monies can be gathered together to build the biotechnology industry and the promise of techno fixes.

…

It’s a sort of very basic common sense request that if you’re going to put lots of money and energy and political time into promoting new technologies, you also need to understand how they’re going to impact people; how they’re going to impact nature; how they’re going to impact economies. And industry doesn’t want to have those discussions.”

On when technology transfer becomes technology dumping:

“Technology transfer has become an increasing demand in the Convention on Biodiversity. And it’s supported very much by African and Latin American and Asian countries who hope that if new emerging technologies such as synthetic biology, artificial intelligence are transferred to their economy, then these will help boost their economy.

The problem in technology transfer is which technologies get transferred and how do we make sure they’re not technology dumping? That very often when a technology is failing in the North, such as for example, incineration, then it gets transferred to the South. And the South has to deal with its impacts.

Sometimes the transfer of technology is a way of opening up new markets that are then tied to having to use the expertise and technologies of Northern companies. And that would just drive countries deeper into debt.

Or create agricultural systems or other systems that aren’t appropriate to their own culture.

That’s why technology transfer has to be tied with technology assessment. You have to see whether the technologies are appropriate to the cultures and the environments and economies into which they’re being transferred.

And that they don’t come with strings. That they don’t come with dependencies. That’s a very real risk.

Unfortunately, at the Convention on Biodiversity, as elsewhere, Southern countries are dealing with cripplingly high loads of debt. And they’re being promised easy ways out.

That if you just jump onto the biotech bandwagon, if you just jump onto the bandwagon of digitalization and artificial intelligence, this will get you out of the economic straits that the countries are in.

Of course, it doesn’t. It’s potentially a trick. It gets them deeper into those economic straits.

The underlying problem here are the historical debts that they’ve been forced into and structurally adjusted into. And that’s actually the thing that needs to be dealt with.

Not giving them new techno fixes that may not work and may tie them more to all sorts of obligations.”

On why technology assessment is essential:

“Every society, every culture uses technologies and develops technologies that are appropriate to their needs. And so long as that community is able to exercise control over those technologies and develop them to fit their culture, then those those technologies are helpful and useful.

The danger I think we have now is that we have technologies that are being determined and driven and imposed not at the level of communities, not at the level of specific cultures but by corporate strategies that fit the bottom line.

We need to therefore have processes to determine which are the appropriate technologies. That’swhy technology assessment has become such a major rallying call for movements and civil society.

Unless we can begin to exercise discrimination over which technologies move forward, which technologies are appropriate to communities and the rights of communities and ensure that there’s a choice made on the right technologies to move forward through assessment you’re going to have the technologies of the elites of those who can push their technologies onto society then reshape society.

So the bigger call here is about agency and democratic accountability in development of new technologies and assessment is one of the first steps towards that.”

It’s the wee hours of 2nd November 2024 in Cali, Colombia. In a large UN negotiating hall   Colombian Environment Minister Susana Muhamed  has slammed down the gavel on a decision that should send a jolt through the AI policy world.  The decision, while seemingly about paying for genetic data, sets a significant wider precedent for how AI firms can be held accountable for stealing training data without consent or recompense.

Here is the context: After years of stand-offs and diplomatic wrangling, the United Nation’s Convention on Biological Diversity (UN CBD) this month adopted a landmark decision on something called “DSI”. DSI stands for  Digital Sequence Information. It refers to digital versions of biological ‘codes’ such as DNA, RNA or the amino acids in proteins. These biological “codes” are routinely collected, stored and processed in digital form and have become the raw commodity powering the global $1.5 trillion dollar biotech industry. Originally sequenced from plants, animals, bacteria and viruses extracted  from territories (plus human medical samples) the sheer volume of ‘DSI’ now stacking up in servers and databases rivals much of the text and images of the internet - and may come to surpass it.

At the recent sixteenth Conference of the Parties (COP16) of the UN CBD governments agreed to a mechanism intended to ensure that benefits arising from industrializing digital sequence information are not monopolized by private industry . The CBD’s previously agreed Nagoya Protocol requires any company using genetic resources (for example a pharmaceutical company) to pay back benefits to   original ‘provider’ communities. This was to avoid ‘biopiracy’ - the colonial theft of biological material. States wrestled for fifteen years with how to also operationalize this obligation in the case of digital genetic resources. Under the new decision adopted this month corporate users of DSI  are expected to pay some part of their earnings into a one-stop common fund as compensation. This fund, administered by the UN, will be known as the “Cali Fund” and It should be structured to first compensate the indigenous peoples, local communities, peasants and others whose DNA, seeds and genetic heritage  was originally taken. Specifically the DSI agreement, made between 196 governments, specifies that if a company is big enough (above certain size thresholds) and uses DSI in its business then it “should” contribute either 0.1% of its overall annual sales or 1% of its corporate profits into the Cali Fund.

In the biodiversity world this agreement is a big deal.  International press hailed the Cali Fund as a breakthrough move that in effect ‘taxes’ big pharmaceutical, agbiotech, cosmetics and other firms for their previously unaccountable use of DSI. While the decision isn’t as strong as South countries had hoped (companies ‘should’ pay into the fund rather than ‘shall’ pay) hope is running high that this mechanism may somehow bring in billions of much needed dollars for impoverished indigenous communities, conservation and biodiversity. In the run-up to COP16 major pharma companies such as AstraZeneca were so alarmed by this proposal that they threatened they would  close down manufacturing locations or cut jobs if such a measure was passed - but pass it did. Yet all the analysis and news reports celebrating this milestone decision, missed an important reality: While big pharma players like AstraZeneca are indeed highly exposed to the Cali Fund, over time it will likely be Big Tech (especially Artificial Intelligence - AI - giants)   who may be most on the hook to pay.

This is because of a little appreciated  set of  AI developments referred to as “generative biology”. I’ve written about generative biology already here and here (I was also recently awarded an ‘AI and Market Power Fellowship’ from the European AI and Society Fund to dig deeper into how  developments in Generative Biology shift power - see here). The term ‘generative biology’ describes the use of ‘generative’ AI platforms (similar to Open AI’s ChatGPT 4 ) trained not on digital text or pictures but on billions of pieces of digital genetic data or protein data. The core idea behind “GenBio” is for AI to ‘generate’ novel DNA, RNA or protein codes  - to invent new genomes, viruses , enzymes, RNA vaccines  or more. Whereas generative AI chatbots are trained on billions of text fragments in order to to create novel text so the ‘gen-bots’ of generative biology rely on hundreds of millions of digital genomes for their training sets - or in other words: DSI.

And not just a little DSI. Generative biology uses a LOT of DSI. When the tech titans advertise their new ‘Gen Bio’ tools they casually boast things like this:

“We took all the DNA data that is available - both DNA and RNA data, for viruses and bacteria that are known to us - about a hundred and ten million such genomes. We learnt a language model over that and then we can now ask it to generate new genomes”  

That is from Anima Anandkumar, Chief AI scientist of NVIDIA corporation. For that boast that they “took” all the DNA data read it as ‘scraped from public databases” . NVIDIA is currently the world’s third largest company by market cap. Its 12 month sales revenue  to July 2024 was just over  $96 billion US dollars while its 2024 gross profit was just over $44billion. So by my reckoning NVIDIA is now on the hook to pay into the new Cali Fund somewhere between $96 million US dollars (0.1 percent of sales) and $440 million US dollars (1 percent of profit). They will be expected to keep paying at this level on an annual basis.

And it is not just NVIDIA. In the report ‘Black Box Biotechnology’ I named a few more big AI giants now offering commercial generative biology tools trained on DSI. These include Alphabet (Google), Microsoft, Open AI, Alibaba, Salesforce and Amazon.

Just these ‘Big Six’’ AI giants ( NVIDIA included) should be collectively on the hook for paying between $1.5 billion US Dollars to $3.74 billion US dollars into the Cali Fund - depending whether they peg their contributions to sales or profits. (see chart below)

And to be clear there is little legal doubt that AI companies are in scope for paying into the fund. Enclosure A of the DSI agreement from COP 16 lists sectors considered to be users of DSI and therefore expected to  make payments. Alongside pharma, cosmetics and the biotech sector is listed:

So there it is:  On November 2nd countries came to a clear UN agreement that AI companies using DSI ‘should’ pay billions of dollars to compensate for their use of digital genetic data.

So what can we make of this?

1. This should impact more than genetic data - it provides a clearly agreed global precedent and framework for the principle that AI firms should pay back to  providers for taking training data.

Most of the negotiators in the room were from the world of biodiversity and genetic resources policy. In this policy milieu it has long been established that a commercial player using genetic resources (seeds, genes etc) taken from elsewhere should pay back some sort of compensation to the original stewards and ‘providers’. This is what is known as the “Access and Benefit Sharing” (ABS) arrangements of the CBD. and has to be done based on Prior Informed Consent (PIC) and under Mutually Agreed Terms (MAT). The ABS approach is also reflected in the FAO Seed Treaty, the currently negotiating WHO Pandemic Treaty and the recent ‘High Seas’ agreement on Biodiversity Beyond National Jurisdiction (BBNJ). From the perspective of ABS regimes it doesn’t really matter whether you are taking a single genetic sequence from a soil sample or if you are hoovering up millions of genetic sequences to train a commercial AI model. The principle is clear. It is a simple matter of accountability and justice.

Yet in the world of Artificial Intelligence policy the question  of whether an AI firm should pay society and/or individuals  for the data used to train models is a raging political fight. The fact is that all of the large commercial generative AI models such as ChatGPT, Meta’s Llama or Google’s Gemini have already hoovered up petabytes of text, image, sound etc from the open internet without prior informed consent or mutually agreed terms. Ignoring copyright and other protections they have used that “taken” data to train their models. Open AI’s current $157 billion dollar valuation as a company is based entirely on brazenly stealing other people’s text, pictures, video and sound files to train models while not compensating a penny back. Indeed Sam Altman (CEO of OpenAI) has bluntly told parliaments that without being able to freely rip off copyrighted works as training data his company would not be able to make money.

Artists, authors, journalists, musicians and others have launched one after another lawsuit against AI firms for their blatant theft of other people’s materials. As the attorney for the Authors Guild , Rachel Geman, has noted, without stealing copyrighted work OpenAI "would have a vastly different commercial product.” As such, the company's "decision to copy authors' works, done without offering any choices or providing any compensation, threatens the role and livelihood of writers as a whole.”  The defence by AI firms against the demand to compensate for stolen training data amounts to a weak assertion that generative AI firms, despite being the world’s richest and best capitalized companies, are acting in some sort of public interest and therefore their use of training data should be covered by ‘fair use’ doctrine. For an entirely unconvincing roundup of AI titans’ lame attempts to excuse their massive theft of training data see this roundup by the Verge.

But the DSI decision by UN CBD isn’t equivocal or even hee-hawing on this question. Instead, it starts from a clear cut and agreed assumption that theft has happened and that the use of digital sequence Information without benefits (here as training data) is wrong and “should” be compensated in line with the ABS doctrine. The decision then attempts to construct a streamlined mechanism by which firms can meet that obligation - even if the final wording doesn’t quite go the full mile in insisting. In so doing and in explicitly naming AI, the Cali Fund may be the first formal instrument anywhere to signal clearly and unequivocally that corporate use of training data (here genetic data)  places an obligation to pay compensation back to original providers. For genetic data governments have settled the principle of this in line with existing ABS regimes.  Now for consistency,the ABS principles of PIC and MAT should also be applied  beyond digital genetic data to all data that might be causally stolen and used for training AI platforms. Those advocating for justice in thE face of AI should be holding up and waving around the COP16 DSI agreement for everyone to see. It’s a clear policy precedent for how all training data might be handled and it’s signed and agreed by almost every government on earth.

2. A global mandate- well sort of. Dealing with USA.

Almost every government. As noted above the CBD (and therefore the Cali Fund) was established with almost universal agreement and membership. But the ‘almost’ is not insignificant. The only 2 countries who are not party to the CBD are the Vatican and the USA., Honestly, nobody expects the Vatican to become a tax haven for AI corporations sheltering from their obligations to the Cali Fund but of course the USA - especially post-election - is another story. Generative biology leaders NVIDIA, Alphabet, Microsoft, Salesforce and Amazon are all headquartered in the US and even the Biden administration had signalled they weren’t going to request payment into the Cali fund from their domestic companies. For the Musk (sorry.. Trump)  administration that attitude is likely a ‘hell no’ - unless Elon wants to exact a certain kind of twisted revenge on his AI competitors (note: X’s Grok AI doesn’t appear to have DSI/Generative Biology interests - yet).

However the fact that major AI firms are headquartered in USA shouldn’t prevent other national and regional governments  from going the extra mile and requiring them to pay into the Cali fund for DSI use. NVIDIA after all sells its services worldwide as does Microsoft, Amazon, Salesforce etc. Google/Alphabet’s generative biology division (called Isomorphic Labs)  is actually based in London UK (and the UK chaired the DSI negotiations and drafted the final text). Alibaba of course is firmly Chinese and China are the most recent presidency of the CBD itself. Nor should governments’ be shy to attempt to tax American AI firms when they have a UN agreement to back them up. Much of the underlying genetic data is taken from outside the USA from countries of the global south and that may be enough of a trigger for them to institute laws compelling US companies to pay.  The European Union’s Competition DG has come to various decisions aimed at fining Microsoft, Google and other big tech monopolists and so could also insist that such firms pay into a DSI fund. Indeed the EU’s Corporate Sustainability Due Diligence Directive (“CS3D”) already expressly refers to the Convention on Biological Diversity as one of the instruments companies should demonstrate compliance with and the EU has already issued a press release on the 5th November stating that “large technology companies that benefit from genetic data are encouraged to contribute to the Cali Fund.”. It’s a start.  In a very parallel example Canada has struck an agreement with Google to pay an annual $100 million dollars into a fund for compensating Canadian journalists and news outlets for the way that Google (including its Gemini AI) draws on Canadian news content in its services.

3. Public vs private data.

There is one odd wrinkle in the Cali DSI agreement - firms are effectively being taxed for utilizing genetic data that is “in the public domain” (such as published on large genomic databases such as GenBank). AI companies who instead draw on or build private DSI databases might be presumed to have established their own DSI arrangements under ABS rules and so it is not clear if they too need to pay into the Cali fund. Presumably they have to now show they are establishing PIC and MAT for taking genetic data privately. Basecamp Research, a UK based generative biology company, claims that its AI models are trained on ten times as much digital sequence information as is held in all the public DSI databases. Instead of scraping public databases (or as well as??) Basecamp strikes private deals with communities to sample and sequence environments on their behalf, including some sort of repayment of benefits under ABS agreements. Indeed, as generative biology leaders claim that ever bigger datasets are needed to be more accurate in genomic design the need for training datasets is driving a new private bioprospecting boom - which Basecamp are at the forefront of. In Cali I was surprised to meet local community organizations  already subcontracting their services to Basecamp Research under private compensation agreements. This is part of a much bigger trend underway in which digital firms are trying to gain rights to all sorts of data generated from datafying indigenous and local territories - whether to be used for genetic engineering or for generating biodiversity credits (that can be sold to ecologically damaging enterprises to ‘offset’ their impact).  The power balance here feels very asymmetrical. The terms under which communities are now signing away social and ecological data to private AI firms needs to be urgently investigated.

4. What is and isn’t DSI - a widening scope.

Sitting through days and days of detailed negotiations in Cali one notable aspect is that the UN CBD had not authoratively placed a definition on what does and doesn’t count as DSI. Everyone agrees that DNA codes and RNA codes count as DSI while others firmly include the use of digital protein codes (eg. to generate novel proteins) . But in the side events, booths and talks beyond the DSI negotiating rooms it was very clear that tech companies are now extracting all kinds of other relevant biological information from biodiversity in digital form. Soil data, species diversity data, moisture data and even continuous recording of wildlife sounds and video might all be considered forms of ‘digital sequence information’ associated with genetic resources and of high interest as training data for AI systems. Large language AI models are already being trained to try to decode and maybe speak animal language, to monitor and maybe alter soils or to model and meddle in population structures. The principles of who is compensated when biologically derived data develops an economic value should be extended beyond simply digital DNA codes to also whatever future commercial uses of AI for environmental, ecological and biological intervention are coming down the pike.

5. Getting back to traceability and accountability.

If there is one big downside with the Cali DSI decision it’s that it risks breaking the original ABS obligation to pay benefits back to specific communities from whom genetic resources were originally taken. By paying into a general fund DSI users (including AI firms) might feel excused from the obligation to identify and compensate exactly those they wronged in the first place. There is also concern that monies will instead be channelled to broad unconnected beneficiaries like big green conservation NGO’s or even to support genetic engineering and sequencing projects.

What is needed therefore are initiatives and obligations to ensure that funds are channeled accountably and correctly back to the actual DSI “provider” communities. Interestingly, big data analysis and AI tools may have some role in helping identify where specific DSI came from. AI firms could be required to establish technical traceability of the DSI they use and the fund could also be empowered to support developing and implementing technologies and approaches that analyze DSI and achieve traceability back to “providers” - to ensure actual communities are accountably compensated.

What next?

Although its been 15 years in coming, the decision to establish the Cali Fund is really only the starting gun on the establishment of a much larger infrastructure and politics around DSI including how it will be integrated and used by Artificial Intelligence giants. Just as decisions on carbon accounting in the climate convention created a thicket of carbon credit and carbon offsetting markets and players, we can expect this new DSI decision will give rise to an entire universe of DSI associated counters, assessors, analysts, services, consultants, lawyers and policy pontificators.  Already legal and lobbying firms are offering their services to corporate players to see if they can shield them or mitigate for the new obligations. In cash-rich AI firms such legal and lobbying firms see rich clients to service.

Early in 2025 ‘relevant organizations’ will be invited to “submit views” on the operationalizing of the COP16 decision - that will be an important moment for public interest AI policy groups, civil society and South governments to insist that AI titans be firmly kept ‘in scope; for their massive use of DSI as training data . Following this, studies will be commissioned and then in late 2025 or early 2026 there will a meeting of the CBD’s “Subsidiary Body on Implementation” to formulate recommendations to further tease out the modalities of the Cali Fund. Such detailed modalities should be adopted at COP17 in Armenia in late 2026.  How the CBD finally rolls out payments to the Cali fund relevant to AI genetic training data could help set a larger direction on how AI Titans are treated for all of their appropriation and theft of other people’s data - not just their genes.

Held in the world capital for salsa dancing, the COP was a three-week extravaganza in sweltering heat . “Cali es caliente” we all muttered as we sweated through something between an eco-jamboree, a trade fair and yes, serious diplomatic negotiations. For outsiders to ‘COP culture’, curious to understand how biotech and tech-related agendas played out in Cali, here’s a rough guide to what, sort of, just happened.

Firstly, for the outsiders but also for newcomers (most of the 16,000 people who descended on the so-called “blue zone” where the formal negotiations across the two weeks of the conference take place, were CBD newbies) it’s helpful to recognise that there was not one, but effectively two different ‘spirits’ occupying and animating the Cali COP during this season of spooks and spectres.

CBD Classic  

First, there’s ‘good old-fashioned COP’ – the spirit of CBDs past if you like. This spirit embodies the story, values, priorities, agendas and programmes that many of us CBD old-timers know too well.

The original UN CBD, which has been meeting for three decades, came out of the 1992 Rio Earth Summit and was crafted at a time when a few well-informed environmental diplomats were worried about the new threat of GMO crops reducing biodiversity (as well as the bio-piracy of seeds, breeds and indigenous cultures). As a result, the ‘CBD classic’ agenda has the precautionary approach baked into its DNA – along with a sensible preoccupation of scanning for new threats and emerging issues, agreeing on oversight guidelines, supporting biodiverse indigenous cultures and so on.

The CBD classic agenda insists on principles of equity and addressing socio-economic aspects of our ecological crisis. It’s why we have the Cartagena Protocol on Biosafety(that mandates risk assessment for GMOs), the Nagoya Protocol which (rather weakly) tries to address biopiracy and the Nagoya – Kuala Lumpur Supplementary Protocol on Liability and Redress which (even more weakly) states that harm from GMOs should carry liability.

CBD classic mode is where we have seen past moratoria agreed on terminator seeds and geoengineering, risk assessment guidelines, strong statements against GM trees and gene drives and other half-decent multilateral decisions, backed historically by on-the-ground protests.

This legacy agenda trundles on, not only in the biotech items but also in the 8(j) working group, where indigenous communities defend their needs and interests, in areas such as forest protection and marine biodiversity protection and climate change. Since the ‘CBD classic’ never had many goodies on offer for big business, the CBD was previously treated as a policy backwater compared to say the business-friendly climate COPs – even earning the moniker ‘The NGOs’ COP’. Civil society with its moratoria and worthy calls for justice, agroecology, economic and cultural rights and so on had some voice there.

CBD 4.0

But there was a different kind of COP going on in Cali; a Davos-style neoliberal eco-trade fair mixed with norm-setting committees for enabling emerging biodiversity markets and next-generation high-tech gadgets.

‘CBD 4.0’ sums up a new spirit that didn’t just apparate into being around the 2022 Montreal “Summit for Nature” with its high profile KunMing Montreal Global Biodiversity Framework (KMGBF), but was certainly kicked into overdrive at that moment.

From Montreal onwards a gentrifying new crowd of younger, better funded ‘green’ NGOs, financiers and philanthropists seemingly ‘discovered’ the CBD as if moving into a run-down but pleasant neighbourhood they hadn’t noticed before. They condensed around a biodiversity financialisation agenda of ‘nature positive’ biodiversity offsets, 30×30 conservation targets, debt for nature swaps and shiny new digital and genomic technologies (or “innovative solutions” as some prefer to tag them).

The CBD 4.0 agenda brings welcome youth energy but also attracts types who are very interested in numbers – particularly how much money is being pledged for ‘Nature’, which is now a measurable thing like ‘Carbon’.

‘Nature’, under CBD 4.0, is written in bright colours on NGO displays featuring jaguars and indigenous people. The theory of change for CBD 4.0 evangelists is that increasing donations – and something called ‘ambition’ for saving ‘Nature’ – will also get us closer to saving ‘Climate’ via so-called ‘nature-based solutions’. The metric for tracking this salvation is the promised billions of dollars for ‘Conservation’ (mostly fencing off lands) including cash trickling into a new biodiversity fund.

CBD 4.0 believers also hope that a small tax on digital genomic sequences will create a ready windfall for ‘Green Growth’ and ‘Nature’ too. Along the way, new lucrative markets and tech startups in biodiversity monitoring, ecosystem restoration and ‘nature positive’ technologies (managed by energy-gobbling AI) are bubbling up – offering jobs to eager young eco-wonks and, potentially, ‘biodiversity finance’ for business.

This neoliberal crowd, many funded at one remove by Bezos, Gates or similar, are here to talk tech transfer, capacity building for development, targets and financing. Their side events are much more upbeat and excited than the angry attacks on capitalism from the CBD classic crowd.

Conflict and Contrast

So where does global policy on biotech, genetic engineering, synthetic biology and new technologies fit in? In these topics at COP16 we mostly saw a clear struggle at work between the CBD classic agenda and the new CBD 4.0 approach.

For a start, there’s the very classic Cartagena Protocol. With its focus on precaution, risk and regulation, industry and the Davos-esque CBD 4.0 crowd are left cold by it and would rather shuffle this protocol offstage. An intersessional process to establish risk assessment guidelines for gene drives (self-spreading gene-edited organisms) was successfully hijacked by industry scientists who rewrote the original biosafety advice for the protocol along more streamlined (less precautionary) lines.

Those nations who take their orders from biotech and agribusiness interests (a group that goes by the acronym CANJAB – Canada, Australia, New Zealand, Japan, Argentina and Brazil – although UK is in there too) celebrated this weakening of precaution at COP16 and then set up a constrained expert group tasked to suggest other potential ideas for guidance and prevent actual work for another two years. A prior agreement to commission guidelines on risk assessment for GM fish was shelved.

At the same time a more precautionary effort from the Cartagena ‘compliance committee’ launched an attempt to get the Parties to publicly acknowledge that gene-edited crops are, in fact, legally regarded as ‘living modified organisms’ under the protocol and shouldn’t be exempted from regulation. CANJAB weren’t going to allow that (since deregulating gene-edited crops is a major focus for them). Instead, they secured compromise text that put off any further discussion on this inconvenient point for another two years (by which time national and regional gene editing deregulation strategies may have already happened).

DNA – Who owns it, who pays for it?

But it’s in the two remaining big ‘biotech’ topics at COP16 where we saw the struggle between the CBD classic approach and a newer CBD 4.0  agenda really playing out.

Consider DSI (digital sequence information) – that is CBD jargon for digital versions of DNA code stored in their millions in cloud databases or used to train commercial AI models. The CBD classic approach to this issue was called ABS (Access and Benefit Sharing). It had some problems, but at least it was primarily motivated by biopiracy concerns. The Nagoya Protocol insists that when companies bring genetic material (e.g. seeds or DNA) across borders for commercial use they must also agree to pay a benefit to the original stewards.

However, when genes and sequences started being digitised and sent by email instead, that arrangement became outdated – so a new mechanism was needed. Enter CBD 4.0.

Rather than design a way to ensure that digital DNA sequences were tracked to ensure ABS compliance, the neoliberal answer has been to set up a big (probably voluntary) multilateral fund into which digital sequence users (such as pharma, AI and biotech companies) can donate some pennies. That fund will either go to indigenous communities or, in some hybrid manner, be added to the headline ‘money for nature’ figures that CBD 4.0 evangelists are so busy trying to count.

The money might even go towards technology transfer or training in biotech as a ‘non-monetary benefit’.

In Cali, a contact group of hundreds of delegates sat every day for hours – and sometimes all day and night long – to hammer out the details of this new multilateral fund and mechanism. Everybody seems excited that maybe there will be some money in this fund and, in this excitement, the negotiations strayed far from questions of justice or access and benefit sharing to questions of who can avoid filling the fund and who can get in line to get money from it.

Scanning, but not seeing, the horizon

In the agenda item on synthetic biology the conflict has been fiercest.

In the CBD ‘synthetic biology ‘ describes all the new developments in genetic engineering – such as synthetic organisms, gene-editing, gene drives, RNAI sprays and more. Synthetic biology (or synbio) has been a CBD classic topic for 15 years, addressing precaution, regulation and oversight. For all of that time, industry has been trying to kill it off by saying it doesn’t meet technical criteria as a ‘new and emerging issue’ (it does).

Parties established a ground-breaking Horizon-scanning, Assessment and Monitoring process at COP15 in Montreal intended to pinpoint new issues and threats and elevate them for assessment and policy action.

This process was a novel substantiation of the precautionary principle and industry fought tooth and nail to stop it from being established (and lost). Once established, a multidisciplinary expert group worked tirelessly over two years to design the process in detail and then carried out a first round of horizon-scanning and assessment concluding that the CBD needed to look more closely at five areas including artificial intelligence and self-spreading viral vaccines for wildlife.

Rather than implement these recommendations, CANJAB plus the UK entirely denigrated and sidelined the work of this expert group and forced a pivot in the negotiations to the CBD 4.0 agenda.

By introducing a ‘thematic action plan” on capacity building and tech transfer, CANJAB plus the UK crafted an industry promotion package for synthetic biology, positioning biotech as the source of shiny ‘innovative solutions’ (technofixes) that could be matched to the targets of the KMGBF and thereby made eligible for funding.

With eyes squarely on the new funding pots of DSI and the Global Biodiversity Framework Fund (GBF) – which rolled out of the implementation of the KMGBF – the CANJAB crowd brought African and other South governments onside with a general promise of capacity-building monies and the eventual transfer of new synbio technologies for their economies.

Horizon-scanning, assessment and monitoring meanwhile was within a hair’s breadth of the chopping block and CANJAB negotiators even pushed for the formal ‘dis-establishment’ of the Horizon-scanning, Assessment and Monitoring process – an unheard of move.

Luckily, Europe and some African and Central American countries (e.g. Egypt, Guatemala) insisted on protecting remnants of the CBD Classic agenda of precaution and oversight.

As a compromise, another more technical expert group will be convened to once again repeat the exercise of horizon scanning and, again, recommending items for assessment. While the process has been saved – and useful knowledge will likely be generated as a result – there can be no illusion: CANJAB plus the UK will continue to block actual decisions or assessments from here on, every 2 years – all while expanding the synbio industry promotion package.

As they do so, it will increasingly harmonise with bigger economic currents already visible in the ‘blue zone’.

Trick or treat?

While the negotiations were engaging, what was really eyepopping was the speculative technologies and radical financialisation on display in the side events and exhibition booths. A pumped-up presentation by XPRIZE Rainforest (a 5-year $10m ‘competition’ to enhance understanding of rainforest ecosystems) showcased macho explorer teams with swarms of drones, robots, genomic probes, acoustic samplers and facial recognition cameras deploying full-spectrum real-time AI monitoring and surveillance of indigenous territories in order to secure new biodiversity credits for financial markets.

Gene drive developers pitched engineered rats and snails as new apps in their expanding gene-tech library. Private companies offered to pay communities to scoop up continual soil, water and air samples for genomic sequencing to feed their ‘generative biology’ platforms so they can sell novel AI-generated proteins to Proctor & Gamble .

As Halloween came closer, a ghoulish parade of tech bros, startups, banks, trade groups and other corporate types wearing ‘Nature’ masks continued to offer up ecosystem restoration, biodiversity offsets, e-DNA and more to dazed country delegates straying deep into unfamiliar techno-utopian territory.

Thankfully at least some, channelling ancestral spirits of CBD classic, still had the will – and the capacity – to ask whether these shiny new Halloween treats that were on offer were, in fact, something altogether more tricky.

• NB: This article was first published at the website of A Bigger Conversation - a great website opening up the dialogue on food, farming and technology. I strongly folks to go and look around all their writings - see

  https://abiggerconversation.org

This briefing addresses ‘generative’ artificial intelligence (AI) tools, better known for text chatbots such as ChatGPT, which are now being applied to generate novel digital sequences for genetically modified organisms (GMOs) and proteins. 

These models, developed by large digital technology firms and trained on vast quantities of digital DNA or protein sequences, find patterns and apply them to create novel digital sequences. This new industry, dubbed ‘generative biology’ by its advocates, is accompanied by promises that AI ‘Biodesign’ tools can deliver an array of technofixes for a more sustainable world. 

These promises echo the speculations made for previous cycles of GMOs and first-generation AI systems, with each falling short of initial commercial hype as new problems emerged. The briefing hopefully will assist Parties to the CBD and other stakeholders in separating fact from fiction. 

However, beyond the hype and whether or not reliable bioproducts ever emerge, very worryingly, the field of generative biology represents a bold grab on the world’s digital sequence information on genetic resources. Already, we are witnessing significant investment flowing to these developments and powerful digital players pumping the hype cycle to generate fascination, hope, and investment in generative biology. With legitimacy granted by Silicon Valley funders, AI firms will likely attempt to significantly change governance conditions for modern biotechnology – claiming the CBD’s core approach of defending precaution and equity is now outmoded in an age of AI. 

The briefing requests Parties to the CBD to redouble their ground-truthing effort of horizon scanning, technology assessment, and monitoring by establishing a sensible process to understand the implications of SynBio’s integration with AI. In so doing, CBD Parties must examine, and strengthen, the Convention’s oversight arrangements for biotechnology in the face of a rapidly changing technological landscape.

French and Spanish translations of the briefing will be published shortly.

Click here to read the briefing.

On 12 September, at 14:00 Central African Time/ Central European Summer Time (UTC+2), the ACB, TWN and ETC Group will host a webinar to discuss these issues. There will be interpretation between English, French and Spanish. To read the programme and register to attend, please click here. 

Happy new year. I hope folks found rest and reasons for hope in the turning of the calendar.  I’m going to continue occasionally writing here at scanthehorizon.org through 2024 while paying close attention to the controversy over whether this platform, Substack, is crossing an ethical line profiting from hate speech  (For now Substack  makes no money from ScantheHorizon so I don’t feel too complicit).

This longer post below is the first in what will likely be a two-part essay on how the worlds of artificial life (Synthetic Biology) and artificial Intelligence are rapidly merging. Its a topic I’ve been trying to puzzle out as a member of the UN’s Multidisciplinary Ad Hoc Technical Expert Group (mAHTEG) on Synthetic Biology. I’ll explain a bit about both Syn Bio and AI below but I’ve come to see that there are broadly 3 ways in which developments in these fields are converging - into what my friend Pat Mooney snappily calls “DNAI” - and I think the significances of that convergence are huge.

Firstly, There’s the way that Artificial Intelligence tools (such as generative AI) are being used to design synthetic organisms  or synthetic biological  ‘parts’ such as DNA and proteins . including so-called ‘alt-proteins’. I call this ‘When AI bots do genetic engineering’ - and thats the ‘part 1’  essay shared below.

Secondly , Synthetic Biology is being increasingly used to design organisms as a component of bigger cyber-physical systems driven by AI and algorithmic decsion-making. The example I will write about from digital agriculture is what I call ‘robot-ready crops’ . These are crops genetically engineered to emit different signals that can be interpreted by AI systems  to direct autonomous agricultural machinery.

Thirdly, We are also seeing synthetically engineered biological organisms and parts become the substrate for AI computation itself - so called ‘biocomputing’.  Instead of using silicon chips to compute, DNA or other cellular processes can carry out computation which is promised as a ‘low energy’ computing option. One arresting example from the past year is the emergence of organoid computing where lab-grown brain cells are now being cultured as AI computers.

I’ll deal with the second and third areas in a post yet to come.

The big questions of course cross all three areas: eg. what does this mean for nature?, for our economies and peoples livelihoods?, for safety?, governance? for who we are in the world and how we relate to each other? etc etc..

Part 1.  When AI Bots do Genetic Engineering:  Text-to-organism platforms, the engineered protein explosion and AI biopiracy.

“Limits are now being breached. We are approaching an inflection point with the arrival of these higher order technologies, the most profound in history. The coming wave of technology is built primarily on two general-purpose technologies capable of operating at the grandest and most granular levels alike: artificial intelligence and synthetic biology”  

– Mustafa Suleyman Co-founder of DeepMind (now owned by Google) and Inflection AI, The Coming Wave -Technology, Power and the 21^st century’s Greatest Dilemma, p55 

I was at a supper recently with a graphic designer explaining how she uses generative AI tools (like Midjourney or Dall-E ) to  design images. Many readers are probably  familiar with these  text-to-art AI generators and I mistakenly assumed generative AI tools had become pretty commonplace.  But  her explanation of typing a descriptive text prompt into a programme to auto-magically generate a full AI image amazed my fellow dinner guests (civil society activists) as if  the whole ChatGPT hoopla hadn’t happened yet. I found myself wondering how they would  react if I shared what I’d just been researching about  text-to-organism software. I thought better of that, kept quiet and sipped my soup. I think they might have freaked them out a bit. It freaks me out a bit.

“Text-to-organism” is my shorthand for an imagined system where you type (in text) the characteristics you want from a genetically engineered organism and then an AI agent custom-designs the DNA for you, maybe even printing out the modified DNA ready to be engineered into a living organism.

Unlike Midjourney or ChatGPT, text-to-organism capability doesn’t exist yet - but its not as far off as you might assume. Along with other developments along the rapidly fusing frontiers of  artificial life and artificial intelligence, developments that  three years ago would have been classed in the  ‘Science Fiction/Fantasy’ section  are now marked up as ‘Business/Venture Capital’ . They will  soon enough come round to dinner table conversation (and urgent action points on policy memos).

Speaking of policy-making, I am part of the UN’s Multidisciplinary Ad Hoc Technical Expert Group  (mAHTEG) on Synthetic Biology - under the Biodiversity Convention . We are a  small group charged by governments with ‘horizon-scanning, assessment and monitoring’ of new developments in cutting-edge genetic engineering (or ‘synthetic biology’). I was glad when the mAHTEG took up this topic of AI-synthetic biology integration. . However, when I started to look more carefully I was  astonished by how far the field had moved . In particular, it appears that the developments in so-called ‘generative AI’ - the generation of new texts, pictures, videos etc exemplified by Open Ai’s ChatGPT - is now bringing along a parallel shift in how life can be redesigned, manipulated and engineered.  It has also brought a jolt of additional capital , corporate enthusiasm and hype into the already overheated commercial space known as ’Syn Bio’. The biggest corporate  players in the AI revolution , many with no institutional background in biology , are  now eagerly starting to play  around with the littlest parts of living things . As I recently wrote to my fellow mAHTEG members ”The current speed, investments and shifts in technical and corporate developments in this arena should have us all sitting up in our seats with great attention and focus to address this topic.“

‘Synthetic biology’ (Syn Bio) is better known in tech, investment and science policy circles.  It is  presented as  the building or “programming” of artificial lifeforms (a sort of “GMO 2.0” or “extreme genetic engineering”). As a field ‘Syn Bio’  was founded two decades ago upon the use of large sets of genomic data (big data) and new tools with which genetic engineers could rationally redesign biological parts, organisms and systems. Using AI to re-design genetics was always there in some form. Synthetic biology firms such as Amyris Biotechnologies, Zymergen and  Gingko Bioworks ( only the last one still exists) have used forms of artificial intelligence, for over a decade - leveraging algorithms to sort through genomic data and select viable DNA sequences as part of their genome-design processes. However in the past year the release of massive  AI ‘foundation models’ (such as Open AI’s ‘Chat GPT’ , Google’s ‘Bard’,  Meta’s ‘LLAMA’ or Stability AI’s ‘Stable Diffusion’ ) has not only effected a historic switch in the AI space. It is also now driving a parallel switch in the Syn Bio space. This switch is described as a move from “discriminative AI to generative AI” and in biotech it accompanies a switch from  genomic analysis to a more generative synthetic biology itself. 

To put it roughly, whereas the ‘discriminative AI’ of the past decade was about sorting differences between data to identify clusters and outliers  (high throughput automated analysis), “Generative AI” directly generates novel forms  or rearrangements of that data (high throughput automated de-novo design). Today’s  ‘large language models’ (LLM’s) such as ChatGPT respond to a human request or “prompt” by predicting what data elements will most likely satisfy the user’s request . The AI then automatically stitches together  texts, images or videos that appear to us as entirely new and creative but are actually just a series of predictive guesses. Generative AI programmes output these novel forms into the world as synthetic images, synthetic text, synthetic video etc

 The mechanism by which generative AI works may  look like a bit like magic, but really  its just statistics. First these systems undertake massive computational “machine learning”  or “training”. This sorts and  maps patterns of relationship between  elements within billions of digitally ingested texts and images to create algorithmic rules. eg the training may record how much more often the word ‘pizza’ is associated with the word ‘cheese’’ rather than the word ’elephant’ - and make a rule out of that.   AI experts then  “fine tune” these general models of  relationships on more specialized training datasets (to refine the rules). The  system then generates outputs coherent with the algorithmic rules that the trained model has deduced.  eg by choosing the most likely thing statistically (eg the most likely next  word in a paragraph).  This creates seemingly new variations of text or image that satisfy the users request for  novel but coherent “synthetic media” .

if we believe the self-reporting of Open AI and others  then generative AI tools represent the fastest  commercial take-up  of a new technology in history. For this reason they are attracting astronomic sums of speculative money. At a time when investment money is tighter than it used to be, the high expectation of commercial returns is cracking the whip , driving the direction, nature and speed of development in AI. These tools arriving so fast into commercial use,  accompanied by magical claims and urgent need for financial returns,  stretch and break our governance mechanisms across many domains. 

The next big tech  investment? – a rush to AI-enabled SynBIo. 

Having seemingly mastered synthetic text, image, sound and video generation , the leading AI firms (which include the world’s very largest and richest companies by market cap) are now focusing on exploiting  other commercially valuable forms of ‘language’ that they might train their ‘large language models’  on.  In particular there is incredible excitement for AI to master the biological ‘languages’ of genomics and  proteinomics (and by extension the field of synthetic biology).  Having spectacularly  ‘disrupted’ graphic design, script writing, advertising, movies, the legal profession, journalism and more, now the ‘life’ industries (agriculture, food, conservation, healthcare) as well as biological production of materials is firmly in the crosshairs for AI firms looking for their next ‘killer app’ or payout. This is seen as a next multi-trillion dollar commercial frontier for the generative AI revolution  to ‘disrupt’ and deliver big investor payouts.

The Senior Director of AI Research at   NVIDIA (the leading producer of AI chips) , Anima Anandkumar, describes one example of how an AI corporate leader is making this switch to applying generative AI capabilities to synthetic biology . She describes NVIDIA’s new “GenSLM” large language model this way: 

“Rather than generating english language or any other natural language, why not think about the language of the genomes. You know, we took all the DNA data that is available - both DNA and RNA data, for virus and bacteria that are known to us - about a hundred and ten million such genomes. We learnt a language model over that and then we can now ask it to generate new genomes”  (source: NVIDIA - The AI Podcast - Anima Anandkumar on Using Generative AI to Tackle Global Challenges – Ep203 - 

) 

If NVIDIA’s airy  talk about just ’ taking’ all that DNA data sounds  like a big colonial grab (biopiracy) - thats because it is. I’ll come back to that.

As a demonstration of GenSLM, Nvidia recently ‘fine tuned’ their genomic language model (based on those 110 million ‘taken’ genomes) on a further dataset of  1.5 million COVID viral sequences in order to generate DNA sequences for novel coronavirus variants . They did this, so they said, in order to aid in pandemic prediction and vaccine design – although what they didn’t acknowledge out loud is that this also has dual use security concerns. If you or I decided to generate blueprints for infectious strains of biological  viruses on your computer expect security agencies  to very quickly come knocking at your door. Anyway i’ll come back to that too.

NVIDIA proudly announced that amongst the synthetic variants of COVID that GenSLM generated digitally there were plus for strains that closely match actual recent new biological variants that have emerged in nature since  the original  training set. That is: they correctly predicted how Covid might mutate. This matters: It helps with surveillance efforts and vaccine design. But this work is not just about Covid.  NVIDIA emphasizes that their underlying genomic large language model (GenSLM) could now be fine-tuned to create de-novo genomes of other viruses or bacteria, enabling new syn bio microbes to be automatically generated much as ChatGPT generates texts. Once they have those new sequences the DNA can be either ‘printed out ‘on a DNA synthesizer or tweaked in a living organism using CRISPR-type gene editing tools. Its a reasonably trivial step these days to go from designing a new genome blueprint on a computer to implementing it in biological wetware - the hard part is getting the design right.  In effect NVIDIA has created a first-pass ‘ChatGPT for virus and microbe design’.

It should be noted that NVIDIA – a traditional chipmaker and the worlds 6^th largest corporation by market cap - was not previously seen as a life sciences company. As such it may not have the right in-house instincts for dealing with microbiological risk or the complex politics of engineering biology. Even biotech companies aren’t too good at this - witness Bayer (formerly Monsanto’s) trail of global protests, lawsuits, genetic pollution  etc.

NVIDIA is far from the only trillion-dollar silicon tech giant  applying generative AI  to Syn Bio organisms and parts. Meta (Facebook), Microsoft (who now control Open AI) , Alphabet (Google) and Stability AI are all investing heavily in developing generative AI tools for synthetic biology. The first 3 of these, like NVIDIA,  are also among the 7 richest corporations in the world. The established corporate giants of the biotech world (eg Bayer, Syngenta, Corteva) are also using this generative AI approach or contracting with smaller firms that employ it on their behalf. One recent report by a UK AI think tank, The Ada Lovelace Institute, suggests that the market for AI-driven genomics technologies could reach more than £19.5 billion (about $25 billion USD)  by 2030, up from just half a billion in 2021.  – but that prediction may already be out of date given the pace of developments.

The visceral impact of  merely-digital generative Ai is already being felt across many other economic sectors (eg. entertainment, law, education, advertising). However biotech and AI leaders  are touting that applying generative AI to biology  is going to be a much more explosive act of “disruption” than what we have seen to date  –  even predicting  a ‘splitting the atom’ moment for AI.(see below) . In his recent bestseller book “The Coming Wave’ the co-founder of Deep Mind (now owned by Google)  Mustafa Suleyman talks up the current coming together of generative AI with Syn Bio as the most significant “superwave” that technologists have ever seen. Another AI-Syn bio proslytiser is Jason Kelly, CEO of Syn Bio ‘unicorn’ Gingko Bioworks. His firm recently inked a five year partnership with Google to train large language models for synthetic biology. He describes the exceptional commercial opportunity of applying generative AI to Syn Bio like this: 

“Here’s why ‘Bio’ is particularly interesting for folks who are interested in AI: The idea of a foundation model plus fine tuning with specialized data - people in AI understand that. Let’s try that with one of the categories of English - lets say ‘legal’ .  That thing has to compete with a lawyer at Robeson Grey trained for 15 years, taught by other humans, writing contracts designed to be understood by human brains in English (a language that coevolved with our brains). That also gives us leverage from how our brains work  - and so we are asking these computer brains - neural nets - to compete with us on our turf. it’s a pretty high bar that its got to compete with.

Now lets go over into biology. I remind you it runs on code (sequential letters) feels a lot like language - but it ain’t our language, we did not invent it , we do not speak it. we do not read it, or write it and so I feel like these computer brains are going to kick our ass a lot faster in this domain than they do in English ... if you are looking to understand where Ai is really going to flip the script - not be a low level Clay Christensen disruption (which is what’s happening in English) -  but rather be  like splitting the atom: its Bio.” 

(Jason Kelly speaking on ‘No Priors’ Podcast ep34 – see 12:50 at

) 

Black box biology?

In short: Kelly is pointing out that AI bots will be more at ease with navigating the logic of  engineering biology than human brains are and so increasingly we human brains will struggle to  understand what the AI bots for genetic engineering are doing. Reassuring, eh? One of the foundational issues in AI ethics is already something called ‘the black box problem’. Effectively this describes how AI systems evolve complex sets of rules for themselves that are non-obvious nor able to be easily understood by humans . However these self-evolved  rules then cause AI systems to make decsions  that have real world impacts. The black box problem becomes thorny when for example an AI trained self-driving car fails to recognise a bicycle and drives into it or  decides to accelerate  into the side of a truck. Because the decsion-making process of an Ai is a ‘black box’ to us humans we can’t understand why it made that potentially fatal decision and so its almost impossible to guard against.

Related to this is the phenomenom of AI ‘hallucinations’ by generative AI systems. Large language models such as ChatGPT routinely incorporate elements in their output that appear compelling but are factually inaccurate or bizarre: Living people are described as deceased, dates are given wrongly, generative AI images of people develop additional body parts or mangled unreadable signage is created etc. While such hallucinations and black box failures can be problematic enough in the 2 dimensional and electronic domains of text, image, video or sound, they could be highly problematic if incorporated into genomic design of four dimensional living organisms or of active biological proteins released into the body or the biosphere.

Genetic engineers already commonly face problems of unexpected and emergent effects from small genomic changes (even as small as a single base pair). If an AI-designed genome was to begin to behave unpredictably or have significant side effects it may be impossible to understand why those changes have happened or to locate the cause until long after the organism or protein had entered the biosphere. It is not clear how biosafety assessment of AI-designed organisms or proteins can proceed when the system is not even able to explain its own design rationale in ways that we can comprehend. In response to the wicked problems of the AI black box , the European Union is now prioritizing development of ‘Explainable AI’.  Governments  may also wish to insist that any organism, protein or other biological components designed through generative Ai must be able to provide strong human-understandable explanations of its design decisions. 

Generating novel nano-machines - Making AI ‘speak protein’

To go back to Gingko Bioworks, their AI collaboration with Google is initially focusing on using generative AI to design proteins - drawing on their in-house codebase of 2 billion protein sequences.  Gingko’s Jason  Kelly explains that “The new idea is “Can I make a foundation model that … speaks ‘protein’ just like GPT4 speaks english?” (source).

It’s worth focusing a moment on why ‘proteins’ matter as a commercial target. While that word popularly evokes a type of food ingredient (meat, dairy, beans etc), a protein is actually a very particular kind of biological form: - a chain of amino acids, folded into a 3d structure which in turn often has bioactive properties. High school biology reminds us of the so-called ‘central dogma’ of genetics where the DNA in the cell is translated into particular RNA stands which code the order by which  amino acids link in long chains (polypeptides) . These chains then fold into intricate proteins: nano-sized structures which carry out life processes. These include enzymes and catalysts to enable, speed up or slow down key biochemical reactions as well as those proteins that build up into living and non-living materials. Proteins have been described as nature’s nano-machines - carrying out much of  the work of the living world down at the molecular scale. How the amino acids are coded by DNA and RNA and then folded into different structures determines how these machines are ‘programmed’ . So being able to ‘speak protein’ is about having the language to programme  biological nano-machines.

For years the crux of research into proteins was about trying to understand how  amino acid sequences fold and unfold themselves into shapes with unique and important biological and structural roles. One by one protein scientists tried to predict how a given linear amino acid sequence would then fold into a particular 3d shape with specific physical and biological properties. It was slow-going work.  Then in 2018 Artificial Intelligence firm Deep Mind (owned by Google) seemingly solved the protein-folding puzzle. Deep Mind’s Alphafold programme was an AI model trained on over 170,000 proteins from a public repository of protein sequences and structures and it seemed to successfully predict the shape of a folded protein from its initial linear code almost every time. In 2022 Deep Mind released a codebase of protein folding predictions containing almost every protein known to science - almost 200 million different structures. The alphafold breakthrough is often held up as one of the strongest examples of an AI ‘success’ in science.

But working with only known proteins is not enough. Following the success of Alphafold several of the first wave of generative Ai models in Synthetic Biology are focusing on generating entirely new proteins never before seen in nature (“generative protein design”) as well altering and ‘optimising’ existing natural proteins. 

There are in fact now several  generative AI tools for protein engineering  with names like ProtGPT2, ProteinDT and Chroma  - but there are also a number of startups (beyond Gingko)  focused entirely on using generative AI for creating  a range of novel proteins for commercial markets including enzymes, catalysts, food ingredients, pharmaceuticals,  biomaterials, coatings, gene therapy and more. In another example of how AI is bringing unusual tech entrants into Syn Bio, global cloud data company Salesforce has developed ProGEN: This is yet another AI large language model for generating novel proteins. This model was trained by feeding the amino acid sequences of 280 million different proteins into a machine learning model.  Salesforce then fine-tuned the model by priming it with 56,000 sequences from just one class of protein: lysozymes – in order to generate  functional novel lysozymes (used, amongst other things,  for food ingredients). A report on this work in Science Daily emphasises just how huge the protein design space is for novel variation just within this one class of proteins: 

“With proteins, the design choices were almost limitless. Lysozymes are small as proteins go, with up to about 300 amino acids. But with 20 possible amino acids, there are an enormous number (20 to the power of 300)  possible combinations. That's greater than taking all the humans who lived throughout time, multiplied by the number of grains of sand on Earth, multiplied by the number of atoms in the universe. Given the limitless possibilities, it's remarkable that the model can so easily generate working enzymes.” 

As noted, Lysozymes are an example of food ingredients (salesforce began with egg proteins) and using AI to design novel synthetic ‘alt proteins’ for processed food markets fits perfectly with foodtech ambitions by investors who claim that high tech artificial proteins can be a profitable ‘climate fix’ (by displacing livestock proteins) or a biodiversity fix (by replacing environmentally damaging protein harvesting of palm oil or fish oil) . There are numerous problems with these simplistic green claims but it’s worth noting that one of the factors really driving these new “alt protein markets” is the commercial opportunity opened up by such protein engineering tools - including AI. Alt-protein enthusiasts such as George Monbiot of the Guardian like to refer to this sort of synthetic bioproduction as “precision fermentation”. The “precision” bit of that buzz term is mostly just hype but it also increasingly refers to AI-driven design.

Food ingredients are just one slice of the future engineered protein market that companies like Salesforce or Gingko are chasing. (Gingko has its own Alt-protein food ingredients company called Motif Foodworks) . Syn Bio companies are also developing engineered alt-proteins as coatings, sweeteners, pesticides, packaging etc – including several uses that will involve environmental release or human and animal  ingestion of these novel protein entities. 

Planetary boundaries for novel entities 

By itself this new AI-powered ability to generate a wider range of novel proteins ever faster for industrial use should be regarded as a potentially significant industrial shift in production patterns. It’s a shift that may have huge impacts on health, economies and on biodiversity in the longer term once a greater variety of engineered proteins make it into the market, our bodies and the biosphere. This should place a high requirement on monitoring, assessment and reporting as well the need to develop systems of recall, clean up and liability should problems emerge. None of that infrastructure is being put in place right now.   A historical point of  comparison might be the advent of synthetic chemistry techniques and establishment of the accompanying petrochemical-based synthetic chemical industry in the late 19^th and early 20^th century that flowed from the new techniques to ‘crack’ hydrocarbons . The generation of a range of commercially valuable novel chemical molecules before proper oversight and regulation were in place led to rapid  dispersal  of  many  thousands of different synthetic chemicals into the biosphere long before meaningful toxics laws were framed. Many of these synthetic chemicals  are now subject to complicated and difficult  global efforts at clean-up or mitigation (or attempts at industrial replacement) because of the unexpected biological and health effects of synthetic compounds interacting with the natural world.  It is estimated that there are currently between 140,000- 350,000 different types of manufactured chemicals being released to the biosphere at approximately 220 billion tonnes per year and that the USA alone adds approx. 15000 new synthetic chemicals to the inventory every year. Most of these are new-to-nature  and many are toxic at some concentration. In early 2022 scientists reported that humans had breached the safe ‘planetary boundary’ for novel chemical entities in the biosphere.  

Now consider the prospect of unleashing a new generative synthetic protein industry, undergirded with massive speculative capital intended to artificially generate an array of never before seen  proteins for quick venture capital returns. Once again this industry is gunning to realise profits in the marketplace ahead of deliberate international discussion and rule-setting. This should raise significant red flags. That this unleashing is supercharged with the current investment hype on AI is doubly worrying.  Recall that proteins have been described as intricate nanomachines whose interactions govern most life processes at the molecular level.  Synthetic proteins as a class of complex molecules may therefore be more likely to be  biologically active (and disruptive) than simple synthetic chemical compounds – indeed they may be deliberately designed for industrial reasons to speed up, slow down, transform or otherwise alter molecular biological processes at the basis of life - thereby  requiring more complex safety assessment . Observers have noted for example that synthetically engineered proteins appear to be more stable than naturally evolved proteins,  – which may raise comparisons with the persistence problems of certain classes of synthetic chemicals. (eg POPs). 

It was from the enormous challenge of trying to deal with the negative effects of unassessed, poorly understood synthetic chemicals that the Precautionary Principle was  first established in environmental governance. This principle roughly states that its is appropriate and prudent to take early action to prevent, regulate and control an emerging threat  even before we have all the data to conclude on its exact  nature . The precautionary approach is enshrined in the preamble to the convention on Biological Diversity  as well as in the first objective (article 1)  of the  Cartagena Protocol on Biosafety and is also principle 15 of the Rio convention that emerged from the 1992 Earth Summit. The precautionary principle was crafted exactly to try to prevent highly disruptive technological developments running into widespread application ahead of proper oversight and governance. This time we have the chance to apply it before the number of novel protein entities entering the biosphere starts to mimic the toxic trajectory of synthetic chemicals. If synthetic engineered proteins become a rapidly expanding, structurally diverse and widely distributed class of novel synthetic entities they will enter the biosphere and require new forms of biosafety assessment and oversight. Left unchecked this would further worsen the overreach of the planetary boundary on novel entities adding both novel protein entities and novel genome-engineered entities to the biospheric load..

“Text to Protein” may mean greater distribution, dual use, livelihood impacts.

Even more concerning is that the industrial generation of de novo proteins through AI-generated syn bio may more quickly become widely distributed, automated and difficult to manage. The spread of the industrial chemistry industry was somewhat slowed by the need for large expensive production facilities. This wider distribution of synbio entities could come as a result of new protein engineering Ai tools. Just as Chat GPT swiftly enabled millions of  ordinary users with just a web browser  to enter natural language text descriptions in order to generate  synthetic media,  so new foundation models are being developed for  natural language “text-to protein” discovery. In a system like ProteinDT which describes itself as a ‘text-guided protein design framework’ a user can write in natural language (such as English) the broad characteristics that they want to see in a synthetic protein (eg high thermal stability or luminescence). The generative AI model will then generate multiple viable synthetic protein sequences. These can be selected and created out of synthetic RNA strands (eg expressed by an engineered microbe or in a cell free system) . The equipment  to turn these designs into reality is in itself is becoming more distributed. 

This distributed “text-to protein” model could make oversight even more difficult. For example one paper on text-to-protein generation acknowledges that “Although text-based protein design has many potential positive applications in agriculture, bioengineering, and therapeutics, it can be considered a dual-use technology. Much like generative models for small molecules (Urbina et al., 2022), ProteinDT could be applied to generate toxic or otherwise harmful protein sequences. Even though acting on these designs would require a wet lab, synthesizing custom amino acid sequences is typically straightforward compared to synthesizing novel small molecules.”  The paper further notes that the authors own model allows generation of venomous and dangerous proteins and that “Future efforts to expand the training dataset and modeling improvements could increase the dual-use risk. “ . 

 Its not necessary to biosynthesize snake venom for the products of generative protein engineering to harm people’s well being. Synthetic biology firms such as Gingko argue that by allowing rapid deign of novel materials it will be possible to replace existing petrochemical-based production with fast and lighter biological production methods. Replacing petroleum-derived chemicals may indeed be one outcome, but it will only be one amongst many commercial drivers of the technology. Other commercial entities may attempt to replace valuable natural products currently grown by small farmers or to displace forest or marine-derived commodities – changing land and ocean use patterns and impacting the livelihoods of farmers and fisherfolk.

We have already seen that the first commercial targets for synthetic biology production have been exactly these high value natural flavours, fragrances, cosmetic ingredients, oils, spices and textiles that are grown , gathered and stewarded by small farmers and indigenous peoples. While i worked for ETC Group my former colleagues and I spent years cataloguing examples of synthetic biology firms who were biosynthesizing (or as some now say “precision fermenting”) natural products. these included vanilla, Stevia, silk, saffron, artemisinin, coconut oil, orange oil and many more important, culturally sensitive commodities that the worlds most vulnerable people depend upon for livelihoods and culture. Here is a report we published  in 2016 that highlights 13 case studies of natural products that could be disrupted by synthetic bioproduction - which may also have serious impacts on biodiversity conservation since small farmers and indigenous communities are often the basis of in-situ community conservation efforts. We found that of the 200 to 250 different botanical crops used by the food and fragrance industry, 95% of that comes from   small-scale farmers and agricultural workers mostly in the global South. In total an estimated 20 million small-scale farmers and agricultural workers depend on botanical crops grown for natural flavors and fragrances. (This is a low estimate.) Flavor & fragrance industry trade groups themselves acknowledge that these botanicals are “highly important in terms of their socio-economic impact on rural populations and may also have important environmental benefits within agricultural systems.”

Back in 2016 ,when I was researching these syn bio applications, I was increasingly noticing that the disruption to traditional natural products was not simply coming from the vats of fermented microbes that synthesized alternatives compounds. It was also coming from new bioengineered enzymes that could convert one low value product into something else with a high value. For example a key target of synthetic biology companies at the time was trying to coax genetically engineered yeast, fed on sugar, to produce large quantities of sweetener compounds called Rebausides found in Stevia.  Most commercially harvested stevia is high in a rebauside called Reb A which gives natural Stevia its characteristic metallic taste as a sweetener ingredient. However much smaller quantities exist of Reb D and Reb M which are far sweeter but exist only in small quantities in natural stevia leaf. While some companies such as Cargill and Evolva   were actively engineering yeast to produce quantities of Reb D and Reb M in fermentation vats (directly challenging botanical stevia) others were producing a genetically engineered enzyme protein that could convert the large quantities of Reb A  already grown on fields into Reb M. On the face of it this enzymatic “bioconversion” might seem a good thing: Stevia farmers could keep growing stevia leaf but consumers would have a sweeter product at the end. That was certainly the argument that leading Stevia company Pure Circle were making about the engineered enzyme approach.

But the same enzymatic approach can also be used to design engineered enzymes and proteins which bioconvert quite unrelated feedstocks into high value ingredients. For example Ambrosia Bio, a synthetic biology company is working with Gingko Bioworks and its AI-protein design tools to create engineered enzymes which convert low cost sugars and starches into allulose, a low calorie sweetener to compete with stevia usually found in small quantities in figs, raisins, jackfruit and maple syrup  Allulose has the special property of not spiking blood glucose and insulin levels while still tasting very much like sugar. One analyst report provocatively claims that the Allulose industry is likely to be “the fastest growing industry in 2031”. In fact Ambrosia Bio is just copying what most allulose producers are now already doing - including sugar giant Tate and Lyle . They also  use genetically engineered enzymes to convert corn and wheat starch into this new low cal super ingredient. In another reality allulose might have been an opportunity for raisin, fig, jackfruit and maple producers to build new low cal sweetener markets.  Instead syn bio protein production means it has been captured entirely by big ag and biotech interests.

AI Biopiracy

But the loss of such economic opportunities is not just about innate AI and genomic cleverness. It is also predicated on a grand theft of genomic data. Remember those hundreds of millions of genomes and millions of protein sequences  that NVIDIA, Salesforce, Gingko and others trained their large language models on? They weren’t  collected by NVIDIA or Salesforce through mutually agreed use agreements with farmers, peasants, patients and so on, They are the result of decades of underhand bioprospecting and biopiracy - taking samples and sequencing genomic material from many thousands of locations and communities and uploading those digital sequences into databases.

Biological materials are both the common heritage of humankind and they  come from specific communities who have stewarded, developed, protected, bred and co-evolved with biological diversity and who may be seen to have inherent rights and relationships to how those genetic resources are used. For the past quarter century indigenous peoples movements, farmers and south governments have waged ongoing battles through the corridors of the Convention on Biological Diversity, the Food and Agriculture Organisation, The World Intellectual Property Organisation, the World Health Organization  and other bodies to get their inherent rights in genomic material recognized and to staunch the theft of genetic material by biotech interests. Its a battle that has birthed a global ‘Seed Treaty’ on plant genetic resources and a Nagoya Protocol on Access and Benefit Sharing of genetic resources. In the past few years the existence of large digital genomic databases has elevated that fight to a north vs south battle about fair and equitable governance of Digital Sequence Information (or DSI) on genetic resources - ie the very digital DNA sequences that AI titans are now basing their business plans on.

Because its a fair bet that at no point when DNA sequences were taken from southern communities by bioprospectors or university biotech researchers did the local community  get asked how they felt abut their genetic heritage being transformed into digital code, uploaded to large language models and used by generative Ai agents to construct synthetic alternatives that could be sold for private profit. That act of free prior and informed consent simply never happened.. Yet it is the common resources taken from  those communities which are now being used to power the expected mega profits of AI tech companies. The emergence of automated AI driven genetic engineering as an industrial opportunity is the culmination of why biotech corporations and industrial states have fought so hard against giving communities rights to their own traditional biological resources.

Incredibly, There is an almost exact parallel to the legal and moral fights which are no breaking out around commercial  AI art, text and video being based on stolen , unpaid for labour of real artists, authors , actors and others . Whan Dall-E or MidJourney “generates” a new piece of AI artwork - what it really does is remix elements from images of millions of existing artworks that have been ingested into the training data. Yet no attribution or commercial repayment is made to those whose artistic labour is leveraged nor is there even  acknowledgement of the original artists whose work is stolen, trained upon, remixed and incorporated into the new AI “artwork”.  The same is true  for synthetically constructed texts which remix the actual written work of real authors and journalists.

Several lawsuits have now been launched by artists, graphic designers, authors and others, while Hollywood actors, scriptwriters and animators staged a successful strike to force hollywood studios to recognise there inherent rights in their works and not have unfairly  it stripped away by AI processes. There are now online tools such as haveibeentrained.com which lets artists search major AI datasets to find out if their works are included  within them and insist on having them removed. However no similar process exists for communities to explore DNA datasets to see if their stolen genetic material is being used by generative AI to create corporate Synbio organisms, protein, and other commercial  products.  The existing processes within the Convention on Biological Diversity and The Seed Treaty urgently need to reckon with the implication of generative AI for the already bitter politics of Digital Sequence Information - in order to defend the rights and economies of the worlds poorest and most vulnerable people.  For example this month a process is getting underway under the biodiversity convention to create  multilateral mechanism for benefit-sharing from the use of digital sequence information (DSI) on genetic resources. Unless that mechanism addresses the aggregation and  use of digital DNA by generative AI models (and makes the DNAI companies  and the AI protein-generators pay) it will have completely missed the point.

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For all the daily welter of stories about AI , media reporting and analysis on AI/Syn Bio convergence is still fairly thin. Where it happens, its usually trade and investment press that focuses on the economic promises and hopes of startup founders and their clever algorithmic science.. Ultimately however most folks are looking in the wrong place.   Its elsewhere, in the peasant  fields of the global south or even the agricultural producing regions of the nort, that the real impact will be felt from the convergence of artificial life with artificial intelligence. If it gets to the point where synthetic biology firms or their industrial clients can credibly type ‘Design me a protein like silk’ into a text-to-protein command line as easily as graphic designers can now  type ‘paint me a picture of silk ’ into  midjourney then the worlds production economies may shift -  harmfully and even further away from justice.

This week I am in New Orleans - glad to be  speaking at the ‘Systems Change’ conference of Friends of the Earth International - the world’s largest grassroots environmental federation. I’ve been asked to give a quick overview of ‘False solutions in the food system’.  Since my ‘podium time’ is limited, I thought it would be helpful to provide a much expanded version of that talk (including slides) here at ScanTheHorizon.org. If you find it useful, please do share it. I also want to acknowledge and flag this publication by the ‘Hoodwinked in the Hothouse’ collective whose work I am partly building on here 😊

They say the road to hell is paved with false solutions… or something like that.

Sometimes it’s hard to recognize these bad ideas at first.  Therefore, I was asked to present a kind of “spotters guide” to false solutions in the food system.

Ideally, I would have liked to offer a full ‘taxonomy’ or a ‘here be dragons’ map of the terrain flagging exactly which ‘false solutions’ food activists (and others) should be watching to sidestep on our way to a better food future.

But there are just too many false fixes out there (and they are proliferating) so I concluded it may be more useful to step up a level and instead offer some general ways to think about this phenomenon of “false solutions”.

Perhaps using these insights folks can draw their own maps, charts, and taxonomies for spotting false solutions in the wild - or at least in the issues and regions in which they work.

Troubling solutionism

First a word on solutions.

Even after 30 years hanging around NGO ‘solutions campaigning’ (or maybe because of it) I still get uncomfortable when I hear the word ‘solution’ tossed into a campaign or activism strategy. That word evokes for me something between the bland optimism of a 1950’s corporate brochure (I think of General Electric’s: “Progress is our most important product”) and the marching jackboots towards “The final solution”. I recognise that in the 2020s, amidst the gathering anxiety of the polycrisis,  ‘solution’ is a comforting word to many with its sense of certainty. But I feel that is illusory. Our polycrisis moment isn’t really going to resolve like that.

Today’s common conception of a “solution” is as an ‘explanation or answer’ that ‘solves’ or ‘resolves’  a stated problem. We are told that ‘fermented’ protein will “solve” the climate emissions problem of livestock, that GMOs will ‘solve’ hunger or that digital farming will “solve” the excesses of industrial agriculture.

 Solutions like these promise a particular kind of dopamine hit. Think how a “solution” is that rewarding pay-off you get at the end of wrestling with a math problem. This kind of solution unwraps “the correct answer” to a stated puzzle. Getting there requires applying a certain type of technocratic cleverness to the problem at hand. It also requires the problem be first stated in very defined and countable, even engineerable, terms. In these days of Artificial Intelligence that sometimes means defining the problem as narrowly as a mathematical algorithm and then feeding it into a model full of data.

So climate change gets rendered as a puzzle of reducing carbon dioxide (or CO2 equivalents) in the atmosphere. Hunger gets rendered as a puzzle of managing access to adequate food calories. Biodiversity a puzzle of protecting ‘wild’ acres. This reductive approach substitutes for, plays down and often invizibilizes the very factors that create, complexify and entrench climate change, biodiversity loss and hunger in the first place: history, power relations, cultural forces, legal arrangements, economic oppression, patriarchy, racism and empire.  Rendering climate change, biodiversity loss or hunger as simply “puzzles to be solved” leads us towards cheap, quick answers or ‘hacks’: fixes of various types.

Benjamin Bratton in his great TED talk provocatively called ‘What’s wrong with TED Talks’ names this as a matter of mistaken metaphors:

” Problems are not puzzles to be solved,” he says. “That metaphor assumes that all the necessary pieces are already on the table, they just need to be re-arranged and re-programmed. Its not true. Innovation defined as moving the pieces around and adding more processing power is not some Big Idea that will disrupt a broken status quo: that precisely is the broken status quo.  If we really want transformation, we have to slog through the hard stuff (history, economics, philosophy, art, ambiguities, contradictions).  Bracketing it off to the side to focus just on technology, or just on innovation, actually prevents transformation”.

I say ‘Amen’ to that.

It may be better to stop referring at all to  climate change, biodiversity collapse or hunger as ‘problems’ (as in math problems) a but instead name them as something more like ‘situations’ or  ‘troubles’ - with all of the layers of complexity and ambiguities those words invoke.  Donna Haraway talks of ‘Staying with the Trouble’ in how we approach our interconnected crises. Like Bratton in the quote above, her phrase helps remind us that trying to “fix “our crises as though we are mending a broken engine is to fundamentally misunderstand the kind of trouble we are in.

It’s to fail to go a radical analysis (literally “to the root” ) of the troubles at hand.  Sure, you can mend the engine of a car (or even make it electric) and feel good about that but cars wil still tear apart our communities and road injuries will still remain the 8th leading cause of death globally. What sort of fix is that?

Evgeny Morozov defines something called  “Solutionism” in his excellent book “To Save Everything Click Here”.  Solutionism, he says “refers to an unhealthy preoccupation with sexy, monumental and narrow-minded solutions - the kind of stuff that wows audiences at TED conferences - to problems that are extremely complex, fluid and contentious. They are the kinds of problems that, on careful examination, do not have to be defined in the singular and all-encompassing ways that ‘solutionists’ have defined them…  Solutionism presumes rather than investigates the problem it is trying to solve, reaching for the answer before the questions have been fully asked”

So solutions and solutionism move too quickly to render a situation as a ‘solvable’ problem - usually in too narrow technical terms and without addressing root causes.

That is not going to dig us out of our current polycrisis hole.

Solutions as “solvent”

Interestingly the original Latin origin for the word “solution”  provides a more useful concept. it goes back to the notion of a chemical ‘solution’  or “solvent”.

This sort of ‘solution’ means ”a loosening or unfastening," - like loosening a knot or loosening the chemical bonds.

Loosening bonds turns matter in a fixed state into a more fluid state - making change possible.  When Silicon Valley talks about ‘disruptive solutions’ (so-called “moving fast and breaking things”) it is often talking about applying technologies as a solvent to loosen up commercial opportunities. We can also loosen up situations, introduce flexibility to make change possible.

But, as the toxicity warnings on chemical solvents attest, a solvent can be strong stuff and must be used with caution. What else do you loosen and break up when you apply ‘disruptive solutions’ as a solvent (communities, rights, ecosystems)?? And what about deliberate solvent abuse? (I’ll come to that…)

Five Forms of False

Having destabilized (loosened?) the word ‘solution’ let me also prod a bit at that other word: ‘False’.

I’ve come to appreciate that ‘false ’can mean different things to different people. I think it’s helpful to parse out these various meanings of ‘false’ when we talk of ‘false solutions’.

1.         FALSE RESULT  - This is  False as in “fraud”.  It’s the most straightforward type of false to contest without questioning solutionism per se. This is the sort of ‘false’ that points out that a thing simply doesn’t deliver what it says on the tin – that it fails on its own criteria.

This sort of false solution is abundant in the many types of proposals that make carbon dioxide-reducing claims. These may fail to count properly or may not account for the full context or life-cycle of an activity.  This kind of false often gets activists into highly technical fights about dweeby details (such as carbon maths or hunger calories).

An example is the case of biofuels which it was claimed were ‘carbon neutral’ energy sources since the act of growing plants supposedly re-sequestered the very atmospheric CO2 that came from burning the fuels. It took quite an effort for civil society to point out that the land use change and soil disturbance associated with growing new biofuel crops was leading to a massive additional pulse of atmospheric CO2 emissions - not to mention all the uncounted CO2 from the energy it takes to grow, collect, process and transport biomass. The same thing is happening now with Seaweed.

2.         FALSE PROPHET - This is when a solution is false as in ”harmful in other ways”. It’s what many environmental justice movements who fight against false solutions have uppermost in mind.

A proposed solution may promises to make one metric better actually makes a whole lot of other metrics worse - such as worsening human rights situations, increasing resource extraction and escalating community conflicts.

 For an icon of ‘false prophet’ solutions, I‘ve used a picture of Joseph Stalin here on my slide    But since we are talking about food I might have better used an image of Norman Borlaug whose “Green Revolution’ is the grand-daddy of modern false solutionism in the food system. Borlaug’s particular prescription of breeding high-input dwarf varieties of monoculture crops involved a mass unleashing of poisons and artificial fertilizers, a switch to commodity-driven monocultures and degradation of community agriculture models throughout Latin America, South East Asia and the Pacific.

Fights against  ‘false prophet’ type solutions often involve showing the unacceptable “side effects” and also how the visions and understandings driving these activities are fundamentally  incompatible with ecology and justice. Often it takes fighting for recognition that other metrics (social, cultural, spiritual) also matter.

3.         FALSE FLAG - This refers to false as in “misdirection” - i.e. when a so-called ‘solution’ is touted in order to usefully distract from better alternatives. If a particular “solution” seems at first to be either bizarre or overly spectacular it may be that those proposing it have no strong interest in actually bringing it into widespread use but are rather using the spectacle of the idea as a token to distract and move policy and public attention elsewhere.

A great example of false flag solutionism (from the transport sector) is Elon Musk’s ’Hyperloop’ - a proposal to bore a pressurized tunnel from San Francisco to Los Angeles through which individual pods containing private cars could be accelerated at high speed - like firing a nail gun. Musk even has a private company called ‘The Boring Company’ supposedly working on hyperloop development. However, from the beginning, it has been apparent that the Hyperloop proposal was floated by Musk in order to distract and take political energy and investment away from a particular proposed high-speed public rail project in California. Musk, who controls the Tesla car company, is openly opposed to investment in public transport and wants to ensure future transport developments center private car ownership.

In the food domain, development’s such as The Impossible Burger, lab-grown meat and other alt-protein proposals have more than a whiff of ‘distraction’ and false flag solutionism about them. Hence the rabid enthusiasm for alt-protein by food processers, fast food companies and even meat companies, some of whom have now reinvented themselves as ’protein companies’.  By ginning up a discussion on switching animal-derived proteins for GMO and monoculture ingredients they centre ultraprocessed industrial fast food culture as a vision of a sustainable future, distracting attention away from movements for agroecology, whole foods, local production and traditional diets.

Fights against this sort of false solution often emphasize the way a so-called solution comes wrapped in unproven hype and exaggerated claims.

4.         FALSE IMPRESSION - This refers to false as in a “Trojan Horse”. That is when the so-called solution is not really designed for the purpose it appears to be but is actually fulfilling another primary purpose altogether - usually profit-making or ‘disruption’.

Digitalisation is a common area for these sorts of ‘trojan horse’ false fixes. Consider the idea of digital farming where a farmer subscribes to an artificial intelligence platform such as Bayer’s ‘Climate Fieldview ‘.  The farmer believes they are hiring an incredibly smart AI which, after analyzing all their farm data, is giving them helpful personalized prescriptions on how to farm smarter. It may even reward them with carbon payments if they follow the prescriptions. 

 In reality, what is happening is that a multinational tech company is cheaply gathering large amounts of data from them that it can combine with other data from hundreds of thousands of other farmers to derive commercially valuable insights that it can sell and leverage. . Bayer is also better able to understand and nudge the farmer towards adopting farming practices that better fit its own products - upselling them its own seeds, sprays, fertilizers and engineered microbes. Thirdly Bayer establishes itself as an intermediary between desperate carbon markets looking for sequestration schemes and farmers, accruing carbon credits and taking a rent along the way. The farmer thinks they are using the tech company’s AI, but the tech company is really using the farmer (and their data and their soils) many different ways to realise extra profits and new markets.

Even more cynically a ‘solution’ may be cobbled together or promoted to push up a company’s ESG (Environmental and Social Governance) ratings for investors or to bring in environmental subsidies, tax credits and grants into a company. The Tesla car company aggressively claims carbon credits on research and production then sells them for billions of dollars. In a similar way meat companies who can show they are developing Alt Protein get a smoother ride from FAIRR’s ESG ratings and hope one day to get carbon credits too.  It doesn’t matter whether electric engines or alt proteins actually help the planet - they are demonstrably already helping companies with their bottom line.

 Fights against this sort of false solutionism often involve investigations and revelations to expose and take apart myths.

5.         FALSE NATURE  - This is false as in ” fake” or inauthentic . This reflects a value that many ordinary folks feel that better, safer, more ecologically just food systems are more likely to be rooted in intact natural ecosystems and   traditional and indigenous relationships with the natural world. Genetic engineering, synthetic biology, alt-proteins , synthetic agrochemicals and geoengineering schemes are examples of ‘false nature’.

 The problem is not simply that technologies like these alter nature – after all many technologies alter nature. Rather it is the depth of intervention, the level of fakery, and the fake confidence and hubris about the resilience of natural systems to survive being meddled in. Concerns also attend enclosure attempts by corporate entities to gain mastery and  legal ownership over natural systems  for their own private ends.

 In fights over ‘false nature’, movements often point to novel risks, hubris, uncertainties and the need for precaution.

If I had a hammer…

In a way there is a sixth sort of false fakery - reflecting the maxim that “when all you have is a hammer, all problems begin to look like nails”. Too often technologists, financiers and PR folks go looking for a good news story to justify ongoing industrial investment in a particular technology and so dream up speculative “solution” stories.

Nanotechnologists looking for ways to use of nanoparticles in the food industry developed nano-thin coatings for fruits that would prevent them ripening during long distance transport.  Realizing this work could get ‘development dollars’ , they subsequently argued this was a technology to help small farmers sell more into global food markets. Rather than start with a root analysis of small farmer’s problems, this solution story starts with the technology itself and is then backcast towards an ad hoc justification -  a solutionist hammer looking for a problem nail to justify it.   

A Food Sovereignty Talisman

To focus on root causes and discern solutionist fantasies, I am reminded of Mahatma Gandhi’s famous talisman - a heuristic he offered to help judge whether a proposed solution is the right one:

“Recall the face of the poorest and the weakest man [person] whom you may have seen,” he exhorted “and ask yourself, if the step you contemplate is going to be of any use to him [or them]. Will he [they] gain anything by it?

Will it restore him [them] to a control over his [their] own life and destiny?

 In other words, will it lead to swaraj [freedom] for the hungry and spiritually starving millions?”

That question over restoring control to the poorest and weakest and hungriest is especially crucial. I suggest we could develop an additional ‘food sovereignty talisman’ to complement Gandhi’s talisman. To my mind it would go something like this:

“Who is ultimately going to benefit from this supposed “solution”?

Will it strengthen and restore Food Sovereignty to small farmers, growers, gatherers, herders, fisherfolk, indigenous people and their traditional ways? 

Which food system does it best fit with?: The diverse peasant food web that feeds two thirds of humanity or the corporate Industrial food chain that feeds only a third?

Go on Try it. It helps. Some solutions sound benign enough until you try to imagine them in the context of rural peasant economies in the global South at which point they just sound ludicrous or irrelevant since they are built for the wrong food system. Will blockchain carbon credit cypto-dollars or Burger King’s bioengineered Impossible whopper burger strengthen the food sovereignty of indigenous peasant communities? it’s a pretty far stretch to make that case.

Ultimately the folks who are best placed to answer the Food Sovereignty talisman are not technologists or policymakers (or even well-meaning campaigners) but the communities themselves. That is why the best way to identify and  fightback against false solutions in the food system is to support participatory processes that let those most affected assess the false fixes themselves - based on their own knowledge, experience, wisdom and practice.

The “Food System Transformation” Agenda

One good question is “Who is coming up with all these false solutions anyway?” We are now definitely seeing an uptick in false solution promotion, and I see it linked to rising rhetoric about something called the “food systems transformation” agenda.

On the surface ‘food system transformation’ sounds like a good thing that we all might want to get behind. After all most everyone wants to get away from climate-harming, biodiversity-trashing industrial food systems. Yet this specific language, while also used in a muddled way by some  food movements, is not actually coming from them.

There is in fact a fairly consistent narrative now about what “food systems transformation’ means that is most comprehensively described, projected and costed by agribusiness-friendly trade groups such as the World Economic Forum, The FOLU (Food and Land Use ) coalition or the World Business Council on Sustainable Development. It’s a narrative loyally amplified by certain large green groups - such as WWF or WRI bankrolled by philanthrocapitalist funders such as the Gates Foundation and the Bezos Earth Fund.

Here for example is the triangle of ‘ten critical transitions’ that is promoted by The Food and Land Use Coalition (FOLU) in its 2019 ‘Growing Better” report. FOLU argues that governments and investors need to urgently channel $350 billion dollars extra per year towards these ten transitions to transform food systems towards ‘sustainability’.

Again, at a casual glance the ten transitions look benign enough:  who doesn’t want Healthy diets, Stronger Rural Livelihoods and Protecting and Restoring Nature? but when one begins to look into how the $350 billion dollars should be spent it becomes apparent that this triangle is a fundraising portfolio stuffed to the brim with false solutions - from ‘Nature-Based Solutions’ such as REDD+ to genome editing and bioengineered alt proteins, not to mention a whole suite of corporate “digital solutions”.

FOLU is in fact just arguing for governments and financiers to plough money into exactly the sort of things big agribusiness corporations want to do anyway. And that’s not surprising when you realise who FOLU are. Hosted by the so-called “systems change corporation” Systemiq – itself a for-profit half-billion-dollar creature of the city of London financial elite - FOLU’s exact membership is a little hard to pin down. However, when it sends letters to governments they are co-signed by the CEO’s of Unilever, Nestle, Syngenta, PepsiCo, Yara, DSM, Olam, Rabobank and more. These guys don’t have the Food Sovereignty Talisman at front of mind.

The two-headed hydra of false solutionism

Which brings me to my last slide:  - What exactly are the false solutions that corporations like Yara, Unilver, Syngenta or tech billionaires like Gates and Bezos would like us to fixate on? As I mentioned before there is a wide and dizzying array of proposals out there but i think we can broadly divide them into two approaches - what I have called the “Two headed hydra of false fixes “. That is: technology fixes as the one head and financialisation of the food system as the other. It’s a hydra because ultimately these two vipers are joined on to the same body and some schemes have elements of both. In the neoliberal economy of 2023, it should be no surprise to see ‘tech;’ and ‘finance’ as the dominant solutionist themes since ‘finance’ and ‘tech’ are literally the two best capitalized, most powerful industries on the planet. Capturing the 15 trillion-dollar industrial food system is fully in their strategic plans for the coming decades.

On technofixes I’ve used a categorization scheme that we developed in the Long Food Movement report when we examined technological trends. In that scenario-building exercise about global food systems to 2045 we clustered new technological developments as being Digital, Automated, Molecular or Nature-based manipulations.

 Of course many bleed across all four categories - for example InnerPlant are genetically engineering crops using synthetic biology to emit fluorescence signals to automated digital farming equipment so that farm machinery can automatically  adjust their operations to better manage carbon cycles and earn carbon credits– thats D – A- M and N  as well as finacialisation all in one technological ‘solution’. Several so-called ‘solutions’ bleed between tech and finance in this way- such as the use of blockchains, smart contracts and cryptomarkets to mint digital carbon credits based on use of digital farming platforms – eg See Nori’s proposed NoriToken .

On the financialisation side of the solutionist hydra we see the world of so-called ‘nature based solutions’ that is now rapidly inflating as the bubble of biodiversity finance and climate finance is being consistently applied to food systems. Luckily Kirtana Chandresekeran of Friends of the Earth international is going to talk more about nature-based solutions and financialisation

so on that note I will hand over to Kirtana. :-)

Postscript: Pathways to System Change

For those who are interested, Friends of the Earth International has recently published an excellent report on why and how to pursue a Systems Change approach (including in supporting food sovereignty). It explicitly calls out the challenge of False Solutions and calls on changemakers to be Honest, Realistic, Collective, and Inspirational. Notice they put honesty first – that may not be as reassuring as promising “solutions” but I think that’s the right way to stay with (and get through)  the trouble.

That report can be found online here:  Pathways to System Change: Transforming a world in crisis into a sustainable and just future

Note to readers:  My apologies that this is a considerably  longer essay than I usually write on this substack. I guess I had a lot to say on this one. I will try to get back to shorter pieces next time!

Folks from the UK (and activists from beyond) have likely heard of George Monbiot. He’s a  left-wing eco-intellectual opinion leader with a weekly column in Britain’s Guardian newspaper. I recall a newsletter writer in Oxford who used to regularly refer to George  as ‘the Greatest Living Englishman’ . He may  well be right - Personally I quite like the guy.

I mean personally as in I do know George a little bit. For some years we lived on the same street in Oxford and I used to be invited around to supper occasionally  since we were two of the only people in our social set who ate meat. That’s ironic because George  has since become a firebrand vegan evangelical of the most  eager kind. Indeed George’s latest book - Regenesis: Feeding The World Without Devouring the Planet. - presents livestock production as one of the worst things. (I appreciate his direction of travel , but not all the specifics ). Animated by his strong conclusions about livestock George nowadays argues for replacing farming  (yes all of it- especially where animals are part of the farming mix,) with high tech industrial food production. Specifically he advocates for harvesting protein from genetically engineered  bacteria grown in  bioreactors. He calls this ‘precision fermentation’ - a recent PR term for  ‘synthetic biology’ (itself a hype term for ‘genetic engineering’).  Its the sort of sci-fi corporate technofix I have fought against my entire career.

George first  flagged his bullish backing for biotech-brewed bacterial banquets by way of a slightly bizarre and bombastic episode.  In early 2020 he barrelled into the Oxford Real Farming Conference (a Uk agroecology gathering) to confront the assembled small farmer crowd. Accusing them of being as outdated as typewriters, he informed them that they would soon be crushed under the wheel of inevitable progress as “precision fermentation” relegated  farming work to the sidelines of history - and none too soon. It was not a good way to open a constructive conversation, nor was  subsequently caricaturing his audience  as a braying pack of  backwards-looking, pitchfork-wielding  reactionaries unable to hear the new gospel. In focusing his considerable powers of intellect and communication towards addressing problems of our food system George appeared to have decided to open his crusade by picking a fight not with agribusiness but with the food sovereignty movement. He regards their celebration of peasant and small farmer production  as an impediment to the one true way.   Claiming that bucolic visions  of farming life  hide an ecological hellscape, George provocatively declared that “The greatest threat to life on earth is Poetry” . To slay the dragon of a harmful food system, the Greatest Living Englishman decided to sidestep the peasants as allies. He instead rode alone into the food battle  behind  a sword and shield of science, statistics and shiny technologies.

Shortly after that Real Farming Conference confrontation I met up with George for lunch; I was passing through Oxford and thought it a good opportunity to explore in a friendly way  our  disagreements over the role of synthetic biology . But  I was surprised to encounter George in full battle mode:  armour on, hackles raised, self-righteous. Despairing of political progress on climate he told me that he felt that there was now no choice but to gamble on technological fixes. He accused me of not really grasping how dire the climate crisis was. He told me he was offended that I was eating an avocado. I was thrown off by the hostility. I had sought out George because I had always been impressed by him as someone who did disagreement well: kindly, gracefully, with humanity. I was particularly a fan of an exchange of letters  between George and musician/activist Theo Simon in which they debated nuclear power. I thought of it as a model of how activists could publically air differences respectfully  while agreeing the real enemy remained systemic.

Zoom forward to this month - a whole pandemic later - and George has recently published a 5500 word essay on food and agriculture. Again  the armour is on and crusading George is riding forth  .  Entitled ‘The Cruel Fantasies of Well Fed People” George boasts on X/twitter that he is proud of writing this new piece. I confess by contrast that I feel pretty  nauseated by it.

The target and reason for the essay  is to battle a key critic of Regenesis (George’s ‘Food’ book) British agrarian writer Chris Smaje. Like many in the UK food sovereignty and agroecology movement Chris,  an admirer of Monbiot’s earlier work,  had been sideswiped by George’s turn to advocating high-tech industrial food. In the tradition of radical pamphleteering Chris then published “Saying No to a Farm-Free Future” as a  direct response to Monbiot’s Regenesis. At the heart of Chris’s critique is naming the inappropriateness and infeasibility of trying to move global food production away from the land towards  electrically powered hydrogen and biotech vats . He frames this in the context of the already large energy requirements needed to manage a green energy transition.  Chris advocates instead for a return to small-scale rural food economies as the better path forward though the jaws of the climate and biodiversity crises.  When I read “Saying No to a Farm-Free future” (i recommend it -  its a slim volume)  I was relieved that Chris had remained consistently gracious and respectful towards George throughout (like Theo had on nuclear power) - even while  taking apart his arguments. In these days of twitter cancelling,  online trolls and mirror-world conspiracist culture war i think  it very much matters how we conduct our disagreements.

There is a popular online meme  that focuses on what makes a productive disagreement versus a degraded spat . This triangle diagram called ‘Grahams hierarchy of disagreement’ is based on a short essay by Paul Graham called ‘How to Disagree’. It encourages people to move away from unhelpful tactics such as name calling, ad hominem attacks and overly focusing on tone, towards more substantial engagement and refutation of actual arguments. At the pinnacle of a good robust disagreement, claims Paul Graham, is engaging with and refuting your opponents central point.  It’s a helpful metric for assessing how well a writer has engaged their opponent or just thrown up mud and theatrics.

I feel that George’s article “The Cruel Fantasies of Well-Fed People” doesn’t do well when measured against Graham’s hierarchy of disagreement. George spends a lot of rhetorical huff and puff questioning Chris’s morality - trying to present Chris as a callous “well fed” ideologue leading an unnamed movement  who "treats billions of people as disposable" and  'promotes what appears to be a recipe for mass starvation' or 'formula for mass death’, Chris is  described by George  as peddling ” the great cruelty- common to colonialism, capitalism, communism, Nazism, neoliberalism". And so another  online disagreement meme becomes relevant  here: namely “Godwins law” known as  ‘the rule of Nazi analogies’ which states that as an online discussion grows longer (regardless of topic or scope), the probability of a comparison to Nazis increases. .Personally I feel that , Nazi name calling aside, accusing anyone of causing mass starvation is a pretty serious accusation - best kept for accusing actually powerful people: eg. the states and corporations  bombing civilians, forcibly grabbing land in central Africa or inflicting crippling debt and austerity measures. Accusing a rather unknown and mild-mannered ruralist writer  of clearing the way for the third horseman of the apocalypse  is a pretty low blow. Coming from a  journalist with a big public platform  it comes off petty, defensive, even defamatory. But thats all about tone - low on the triangle.

Back to what makes a good disagreement, it is notable that  Georges 5500 word defence is absolutely silent on one of Chris Smaje’s two central points:  - that brewing bacterial protein for the masses is energetically infeasible and unwise in the context of the necessary green energy transition.   George reserves some of his most sneering huff and puffery to accusing Chris and other food sovereignty advocates of being unable to deal in hard numbers: “If there is one habit that incites fury more reliably than any others” he writes. “ it is to put numbers on the problem. Hectares, yields, nutrients, calories, inputs, outputs, costs, emissions, hunger, death: any form of quantification is as welcome in this arena as a tambourine in a Bach sonata.”  

Yet Chris absolutely DOES put numbers on a problem that George studiously avoids acknowledging - the problem of wishfully thinking that we can bacterially brew our way to food security. In his book and then more fully online Chris carefully lays out the maths for why George’s calculations about the energy input to produce bacterial foodstuff is off by a  factor of four. He shows that every kilo of bacterial protein will require at least 65 Kwh of energy - twice the daily energy use of an average US household. This is electricity use which in aggregate would then have to be added on top of  expected additional  electricity demand for electric vehicles, electric heating of our homes, running an ever-ballooning internet, cloud and AI infrastructure and much more - all from clean energy sources without damaging mining and extraction for the infrastructure build out. Food would seem to be  be an unnecessary use of additional electricity generation since for now agroecological  land-based food production  doesn’t require electricity at all . Incredibly food  really does grow on trees.

Besides the electricity use challenges I can suggest other reasons why feeding the world with synthetic biology protein may prove unfeasible or problematic  -  Some I did mention in passing to George but he either didn’t grasp them or felt too confident that the tech-boys will find a clever way around them Anyway here are three - i admit I haven’t put numbers to them:

1. Nutrients - In Regenesis and elsewhere George likes to present his  bacterial banquet as a free ride on thin air, surf and sunshine. Hydrogen producing bacteria, in his  version, consume only oxygen and water and transform this into magical edible bacterial biomass to offset land-based farming. It’s not that simple though. All living things use DNA and RNA - nitrogenous nucleic acids  which arrange themselves on a phosphate backbone .Therefore bacterial protein production, like land-based food growing, requires ongoing nutrient addition of phosphate and nitrogen as well as other elements - which must be acquired, mined, carried etc  with  additional energy and biodiversity costs. Of course industrial agriculture already requires large amounts of these artificially acquired  inputs  but not agroecological farming which draws nutrients from soil and animal wastes . In seeking to decouple food production from soils and animal agriculture  George is implicitly arguing for a greater take of phosphate and nitrogen through artificial and extractive means (eg phosphate mining).

2. Contamination -  So-called ‘precision fermentation’  is not a virginal industry - its been around well over a decade as “synthetic biology”. Companies such as Gingko Bioworks, Amyris Biotech. LS9 and Solazyme (the last 2 no longer exist ) promised that they would engineer bacteria to produce biofuels, plastics, food ingredients, textiles and more. Despite billions of dollars of investor funds and plenty of ‘steel in the ground’ the industry hasn’t succeeded in creating an endless flow of bulk biofuels, food oils or industrial chemicals. Instead most of their profits are from smaller niche markets - flavours, fragrances, biopharmaceuticals - that are high value but small volume. Several of the Syn Bio startups who launched promising high volume bulk production either failed or switched to these more niche compounds. One reason  is contamination. Wild bacteria and yeasts are a constant foe of fermentation processes. Once they get into a fermenter wild type microbes can change and spoil the production and if you contaminate a batch of bacterial protein  or  speciality  engineered foodstuff  that may have safety issues. For commercial survival  Syn Bio companies pivoted to products that could be profitable in small tightly controlled volumes. The bacterial food future that George dreams of  is not such a small managable niche. it will pose a significant technical and feasibility  challenge to scale up bacterial protein without  the problem of contamination - and even harder to do so without exorbitant financial and energetic investment in ever multiplying numbers of steel tanks.  For this reason at least one company in Canada wants to do away with steel tanks for bacteria and use genetically engineered flies as bioreactors instead - I kid you not.

3. Chlorine, fossils, platinum -George’s preferred technology is brewing hydrogen-eating bacteria. The hydrogen for this has to come from somewhere and the two most likely sources are processing fossil fuels  or splitting water with electricity. George unsurprisingly prefers the latter route even though in the real world most hydrogen is fossil-derived. Another common means of commercial hydrogen production is the chloralkali process which produces the toxic chemical chlorine with hydrogen as a byproduct. Basing the food supply on a process that manufactures excess chlorine may be asking for trouble but the chemical industry would love to have more chlorine produced under such a green smokescreen.  For now hydrogen production requires  a catalyst such as platinum,  with problematic mining impacts. Catalysts based on nanoparticles may be possible but bring their own novel toxicity challenges.

None of these challenges are the killer argument against handing our food system to biotech breweries - but as you start to add them up then feasibility and desirability falls quickly away. More significantly sourcing food in biotech  factories requires a reorganization of the food system to be highly centralized, arranged into corporate-mediated value chains flowing from industrial processing facilities. To my mind that is exactly the corporate industrial food chain model at the root of so many of our current problems. We don’t want the food system concentrated in the hands of less and bigger corporations. Such a concentrated food system  is unfair,  extractive, easy to monopolize and  very vulnerable to external shocks  - which we are going to see more of in our unfolding century of crisis. Consider which food system is more likely to fall over in the face of climate catastrophe, dictatorship  or cyberattack: - a handful of large electrically dependent food brewers  or a distributed network of millions of small farms and local food relationships  spread across diverse landscapes?

Which brings us to Chris’s other central premise in ‘Saying No to a Farm-free Future’ - the one that George does attempt a partial response to. Chris argues that the way to organise food to survive in the face of climate crisis is to withdraw away from the corporate controlled industrial agrifood chain  and attempt instead  to put power back into the distributed local ‘food web’ of small growers, local markets and peasant-type production . This ‘food web’ may sound  ‘backwards’ to modernist global north sensibilities of someone like George but it is what still characterizes much of  the food systems of the global South. It is also better suited to our times of crisis and challenge. Strengthening food webs is not a “one stop” bold  breakthrough. Rather its a distributed social process of ‘muddling through’ together  in diverse and different ways that are at best  agroecological and collective, culturally and ecologically tailored to different geographies. The food web (or ‘agrarian localism’ as Chris terms it) can’t be summed up in one shiny totemic widget. It doesn’t fit  a formulaic  “stop this, go that” campaign binary (“stop eating meet , go plant-based”).   Leaning into the complexities of  local agroecological diverse food webs is maddeningly  unsellable as a soundbite.  George presents agrarian localism as a ‘withdrawal’  but its more in the gesture of “staying with the trouble” - a phrase feminist scholar Donna Harraway so brilliantly coined to dismiss  big, male, over simplistic technocratic solutionists who claim to have the ‘one big answer’ to our global polycrisis. (sound familiar?). Staying with the trouble and leaning into food webs means embracing a messy politics of relationship, nuance, context, complexity and co-learning. It means a single clever journalist sitting in Oxford can’t dream up a cracking saviour formula all by himself in the space of a 2 year book project. . its why (and how) we build movements - to figure this stuff out collectively. So relax - take off the armour - make friends.

I think that understanding the difference between the food chain and the food web offers the opportunity to unpack assumptions that lie beneath Georges rather crude and emotionally loaded attack on Chris Smaje as a hunger-monger . Doing such unpacking  destabilizes  Georges core arguments in Regenesis. Its ultimately about that insistence on numbers.

As I understand it, at the heart of George’s arguments in Regenesis (and now restated in his “Cruel Fantasies of Well Fed People”  essay) is a contention  that the titanic of our current food system is on a crash collision with the iceberg of global heating which will sink  much of our food supply in a conflagration of heat stress, drought, flood and rising climate catastrophes.  In broad strokes I think George is right up to this point. George, who claims to have read over 5000 academic papers in writing Regenesis, lays out the numbers that describe this catastrophe in more detail and shows that if we are to launch his preferred lifeboat  of “rewilding” nature sufficiently  to rapidly recapture  Co2 then the land and resource footprint of the food production enterprise  is going to have to radically shrink. Using numbers  from FAO, often by way of neoliberal website Our World in Data (who he seems to quote approvingly at every turn). George sketches a picture of a food supply  struggling to keep up with a growing global population and climate pressures. In his neo-malthusian telling, understanding rising hunger and managing food futures within this situation is a simple matter of maths: Take the 9.5 billion tonnes of primary crop production reported in the commercial global market,  minus both  the billion or so tonnes fed to livestock and the amount of grain and oils diverted to biofuels and then divide the remainder by 8.1 billion people and see if there are enough calories per person to be fed -  while  acknowledging the distributional effects of high prices. Given that he wants to launch rewilding lifeboats, this approach is why George is so keen to remove livestock from the picture - if land is not being wasted for livestock protein, it could be rewilded without impacting hunger. That improves the overall maths - feeding more people on less land.

In the latest essay George goes further. he stake out an additional view that within this equation monoculture industrial agriculture, while exacting a heavy toll on the planet and people, is nonetheless the tool that has kept more and more folks alive and staved off famine - so we mess with it at our peril. Noting the lower percentage of populations dying  in acute famines George claims that the lower levels of hunger as a percentage of global population stem from two key features of the industrial agrifood chain: the growing of large amounts of grain in certain ‘breadbaskets’ and the success of  long distance food value chains to move food (grains anyway) from those breadbaskets to the rest of us. George acknowledges that the industrial monoculture of those breadbasket regions is problematic but says we have to acknowledge that system is whats keeping hunger from the door..  Its what I’ve heard called the ‘abusive husband’ argument for agribusiness - as long as this horrible abusive system still puts food on the table we shouldn’t walk away from the relationship or go to the police. We should be grudgingly grateful.  It’s premised on the idea that There Is No Alternative (TINA).

Specifically George claims: “Much of the world’s food is grown in vast, lightly-habited lands (US plains, Canadian prairies, Russian steppes etc) and shipped to tight, densely-populated places.”

I think George is dead wrong here. Wrong because what he perceives  as ‘much of the worlds food’ is not ‘much of the worlds food” at all - at least not much of the food the world actually eats. In all of his writing about food and agriculture  George systematically invisibilizes  two thirds of the food that actually feeds people   There isn’t one unified food system to talk of - there are two (or maybe many..). The formal statistics and data that George holds up for his assertions actually count only the food in one of these systems - the industrial food  ‘chain’. The chain is the commercially  structured food system that organizes its activities along an economic value chain. It is relevant and important but its a woefully incomplete picture of the food system.

While he was writing Regenesis I pointed out to George that 70% of the food that people actually eat is grown in another system -  by small producers in peasant and local food webs - in gardens, urban agriculture,  small plots and through fishing and gathering. This 70% figure is a common statistic first calculated by Pat Mooney of ETC Group. George replied that this just wasn’t credible  pointing to a  paper by Vincent Ricciardi et al which models food flows and concludes that small farmers actually only produce 30% of the food supply. Ricciardi’s article was in fact written as a direct response to ETC’s 70% statistic.  It was an arresting article and on the face of it seemed to be backed up by another paper from FAO authors that also came to a similar-sounding “30%” statistic. Yet when my colleagues and I dug into these two papers they began to fall apart under scrutiny. I knew Ricciardi’s  thesis supervisor personally (Navin Ramankutty at University of British Columbia - oddly, like George, another former neighbour of mine)  and when we talked in person Navin acknowledged that some of our critique was right and the paper shouldn’t be overly-relied on. The bigger problem, we both agreed, was that we didn’t have all the right data about informal provisioning to reach reliable conclusions.

In the end I helped draft  a critique of the two “30%” studies which ETC Group and others  published here: https://etcgroup.org/content/backgrounder-small-scale-farmers-and-peasants-still-feed-world  under the title ‘Small Scale farmers and Peasants still feed the world’. The reasons we  defend and double down on broad message of  the 70% claim is spelled out  in that paper but basically both papers had serious methodological flaws. The Ricciardi paper was biased heavily towards over emphasizing   European food systems. Both papers drew only on formal FAO and national statistics about commercial production which systematically invisibilized the many other ways that people grow, gather , access and provision food outside what is counted in the marketplace- especially in the global South where peasants and small producers are most numerous.  Researching and writing that response helped clarify for me that we still don’t have much hard data about the real food systems that actually  feed people and keep them from hunger. Not acknowledging this other system  of the ‘web’ leads to  careless invisibilizing of billions of people and mountains of food. Until the invisible is made visible  and the uncertainty acknowledged, basing policy proposals on the partial statistics of the industrial food chain may create deadly decisions about our food system.

So when George writes that “Much of the world’s food” is grown in a few breadbasket regions and shipped to cities ,He is making an unsupported assumption looking at very partial evidence.  By contrast he should consider the implications of  this  2015 study:   McDonald et al (2015 ) Rethinking Agricultural Trade Relationships in an Era of Globalization. This  shows that  (after non-food uses are accounted) only enough food calories were traded across borders to feed 1.7 billion people. Thats less than a quarter (23%) of the global population that year  (2015 global pop was 7.3 billion).   Indeed 23% likely overstates the nutritional role of the international food  trade  since it assumes that all of those calories equally reach consumers. The FAO calculates that 17% of calories in food value chains are in fact wasted at the grocery or household level . A further 17% of food are ‘over-fed’ to the same people leading to obesity and metabolic disorders. So the real number of calories traded across borders that actually feed people may be somewhere  closer to around 15 percent (around one sixth) of the global nutritional needs ( equivalent to feeding 1.1 billion people). Much of that flow (according to trade flow analysis) heads to the global north.  The remaining 85 percent of people (mostly in the South) were presumably fed  ‘locally’ from territorial  food webs including territorial markets - not kept alive by long global food chains  of grain as George presumes.  Here’s what the FAO says about the relative importance of long food chains vs territorial markets in food security “The available research and information on markets that are embedded into local, national and regional food systems confirms that they are crucial not only for ensuring market access to smallholder farmers, but also for food security and nutrition, since only 10–12 percent of all agricultural products are traded on the international market (FAO, 2015).

George cites a study in Nature to back up his contention that long food chains are needed to feed people. The study  looks at 6 key crops (so called ‘staple’ foods) and concludes that  only “11-28 percent  of the global population can fulfill their demand for specific crops within a 100km radius”.  Weirdly  George claims this study shows that “Most of the places where large numbers of people live do not have sufficient fertile land nearby to support them” It doesn’t show that at all. Once again, like the Ricciardi paper, the study works only with data available from the traded commercial food system, invisibilizing much of the food web. More significantly it doesn’t actually speak to food security at all - as George misleadingly represents it -  rather to food preferences. It notices that people like to buy rice or tropical root crops in locations far away from their key growing regions and shows that those far away regions need to be supplied by long chains to meet the multiple market preferences of people in those locales. It’s the sort of study that food traders would use to structure their marketing and logistics activities but its not about combatting hunger. To answer that question the study would have had to ask if there was sufficient nutritious food available (not just 6 select commercial value chains) for all populations within a 100km area - including through counting subsistence and non-market sources. It didn’t attempt that.

Since George mostly drew his conclusions  on the food system from reading (but seemingly not questioning) 5000 academic food studies its not hard to see where his invisibilizing bias against the 70% comes from. Academics need reliable comprehensive numbers to write reviewable articles and the easiest numbers to hand  are about the food chain and food trade.. Indeed the Food and Agriculture Organisation and others such as the Civil Society and Indigenous Peoples Mechanism (CSIPM) are right now trying hard to point to the importance of these previously invisibilized and undercounted  territorial markets and territorial food systems of the food web since accounting for them changes the overall global food picture. in a  recent FAO report on Mapping Territorial Food Markets it is noted that “ A number of studies show that the majority of fruits and vegetables in low-income countries (LICs) are still purchased through territorial markets. Territorial markets are not only key retail outlets for fruits and vegetables, but also for animal source foods and staple foods. These trends indicate the relevance of these market outlets on a macro-level. However, data concerning the availability of the different food groups and characteristics of food retailers and consumers in territorial markets are seldom considered in national data collection systems.”. The same report reiterates that “the markets in which smallholder farmers most commonly operate are systematically neglected, often due to pervasive gaps in information about these markets.”

There are case studies that do start to look at this question of the territorial web versus the industrial chain . For example FAO reports the following: “Karg and Drechsel (2018) studied the dependence of urban centres on their “hinterland” over several seasons for more than 50 commodities in several cities in West Africa. According to an analysis of more than 40 000 records of food flows in two cities, about half of basic urban food needs were met by farming within a distance of 100 km. Extending to 300 km, 80 to 90 percent of all food items were sourced for Tamale, Ghana and 60 to 80 percent for Ouagadougou, Burkina Faso. In comparison, an average processed food item found in shops and supermarkets travelled 3700 km before reaching local shelves.”

The same statistical biases may unfortunately also creep into  some of the abstracted data on hunger that George cites. I want to be clear that I have absolutely no doubt that hunger is a very real, terrible and growing force in our food system today and tackling hunger should be at the core of how we address the multiple challenges we face - indeed it is at the heart of the food sovereignty approach which demands food be treated as a human right not a commodity. Many people see and feel hunger firsthand in both urban and rural settings. However the metric by which George refers to hunger in his essay, “Prevalence of Global Undernourishment”  draws on estimates of the total caloric supply per country taken from FAO’s food balance sheets that in turn do not account for availability of subsistence and non-commercial food sources. So for a real accurate picture on hunger  that metric has to supplemented with other types of information such as household surveys  and more qualitative on the ground reporting. The data we have only gets us so far.

What this means in practice is that not only do we not have any good statistics on how most people in the world get their food, we also don’t have a very full statistical picture of who is and isnt starving once non-commercial and subsistence territorial food systems are  acknowledged. With numbers alone its hard to discern what is causing or softening hunger. George argues in his essay that the reduced bite of famine events is thanks to the increased trade in food across borders moving calories quickly to where they are needed . This makes some sense in the specific  case of short term emergency food aid but its a stretch to generalize that the long chain food trade is responsible for generally rounding down hunger between  these acute events. The reduced bite of famine may even  be thanks to stronger territorial food economies and concerted decade-on-decade action to support small famers and local food markets. When famine hits many emergency relief organizations now prioritize buying local food first and redistributing that (to protect territorial systems) -not moving grains from the other side of the world.  Equally it was instructive that through Covid governments and movements alike moved to strengthen local food economies and territorial co-operation in face of the clear vulnerability created by dependence on long food chains.

Worryingly  I notice in George’s writing on food  that while  he forcibly presents his assumptions as fact, and demands that food sovereignty folks back up their views with numbers (that just may not exist) he then doesn’t present much compelling evidence to support his own big counter-assertions. An example of this appears in his “Cruel fantasies” article where George argues the need for long food chains (and bacterial production?) specifically to feed cities where over 50 percent of the global population now live. George asserts that  long chains are needed because  “Cities can grow only a tiny fraction of their food,”  pointing out that urban areas occupy only 1% of the planet’s land, and this land is in high demand for other uses.

But do cities grow “only a tiny fraction of their food”? Again I think George is wrongly bringing his narrow experience and assumptions from a UK context into misrepresenting global food systems more broadly. In the global South newly urbanized peasants living in favelas, townships and large towns routinely continue to raise animals  such as chickens and pigs and grow food in back gardens and urban spaces. Much of what are considered ‘urban areas’ are actually big towns and sprawl contiguous with peri-urban space and peri-urban growing is also important to how a city subsists. Cities are not just the downtown core he is imagining. There are back gardens, wastelands etc.

While he was writing Regenesis George took issue with an aspirational target ETC Group and IPES food had made in our Long Food Movement report . We imagined it could be  possible that by 2045 up to  25% of the world’s small livestock and fruit and vegetable consumption could be supplied by urban farms and households”. George wanted sources for the feasibility of  this  target and so my colleague Pat Mooney, who has continuously studied food systems and fought hunger since George was in nursery school, passed on a few relevant references. This included a 1996 report that 15-20 per cent of global food output is grown in cities (Smit et al 1996) which was updated in 2001 here and another that estimated eight hundred million people were practising urban agriculture (UNDP, 1996). Another OECD study Pat referenced reported that in the US in 2005, over 56% of GDP from farming, forestry, and fishing was produced in “metropolitan areas”. A further example was a study from Detroit that found that if all the empty urban land in that city was assigned to vegetable growing  “even amateur gardeners should be able to produce twice as much fresh vegetables as the city can eat.”. The point was to show that this stretch goal of 25% by 2045 was not unfeasible.

Georges response to these examples was to attack them as “ridiculous”, out-dated  and likely based on unhelpful classifications of what constitutes a ‘metropolitan area’. Interestingly looking back at our correspondence I see that George also attacked me for passing on these references  along the lines that he now portrays Chris - as a fantasist hunger-monger. Specifically he wrote  “I guess the hard maths of where food can and cannot be produced explains the handwaving about urban agriculture: if you can’t grow it you must invoke an urban miracle. But I would urge you to recognise just how dangerous this position is. It’s a prescription for mass starvation. “. He then wrote in Regenesis that he had asked an “old friend” (presumably me)  for hard data on the potential for urban agriculture and that I had answered him amicably  but couldn’t provide the evidence to back up our claims. Which isn’t quite accurate. ( Although i was amicable ;-)

Yet what strikes me now is that George never presented any data either - not then in our email exchange nor in Regenesis nor in his recent attack on Chris. He just presumed and asserted that urban growing is a non-starter for food security and glossed over the key point that I had tried to make that cities in the South are filled with resourceful peasants who know very well how to eke food out of marginal situations. As a result he invisibilizes these urban peasants  and their ingenuity yet again (even while he celebrates and imagines the ingenuity of urban biotech brewers) . Instead in Regenesis he spends several paragraphs  dwelling on the energetic infeasibility of corporate high tech ‘vertical farms (I agree with him) and making some non-sequitur jokes about cannabis grow-ops - again rooting his analysis firmly in his limited northern/UK experience yet drawing unsupported global generalizations . Its one thing to dismiss a set of studies from 25 years ago as not up to date but he fails to explain why they are wrong  today nor does he   provide any counter-data, recent or otherwise  while  just forcibly reasserting his own assumptions. This is exactly the sort of behaviour George  accuses others of:  Big claims, no numbers. The difference is that he additionally  accuses those who differ from his assumptions of enabling ‘mass starvation’. Ouch.

Actually some newer datapoints supports the ‘urban miracle’ that George can’t imagine: The US Department fo Agriculture (not known for its romantic food sovereignty bias) reports that “Around 15% of the world's food is now grown in urban areas. City and suburban agriculture take the form of backyard, roof-top and balcony gardening, community gardening in vacant lots and parks, roadside urban fringe  agriculture and livestock grazing in open space." The FAO has also published updated work on urban and peri-urban agriculture. A recent FAO survey reports that “the latest data indicate a global farm area of more than 60 million ha within urban agglomerations” and that “it is likely that well over one billion people in urban and peri-urban areas are growing food or are engaged in other agricultural activities”. Do we have comprehensive numbers now on urban growing?, No, Do George or his neoliberal pals at “Our World in Data” have those  those numbers either . Probably No. The conclusion when there is lack of data shouldn’t be to look away or dismiss it as ridiculous. The better conclusion is that some of the most important things we need to know about food systems we just can’t conclude  yet with numbers and so we have to  look to other kinds of evidence - experience, case studies , farmer knowledge, maybe even culture and poetry (!)  . Unfortunately the shield and sword of statistics, science and  quantification might not be much use by itself.

And maybe the peasants who have been in the countryside for centuries have even learned a thing or two about how to keep dragons at bay and  how to get through difficulty and hardship without waiting for the greatest living englishman to come along and tell them what is what.

Coda.:  This is a  far long enough essay but I wanted to end on a more personal note. As I mention George has been kind enough to feed me at his home in the past - sometimes with his own delicious food. He is part of a small group of friends who planted and grew a food orchard that he writes about in Regenesis - and I’d always thought that was a wonderful act of hope and long term vision in these times. This summer my family and I were lucky enough to also stay on Chris Smaje’s smallholding in Somerset England and eat some of his delicious food too and I learned what a  modest, self-effacing, principled person he is. But looking around his holding, where he also grows food for a community veg box and offers allotment  plots for locals to grow their own food what i was most astonished by was a  wide 20 year old swathe of woodland. Chris and his  family had planted  this from scratch. Full of nuts, berries, fruits etc they had transformed  empty fields into an agroforested oasis. I find anyone who plants trees and makes long term plans to increase food production and biodiversity in the landscape very inspiring -  whether it’s George’s Oxford urban orchard or Chris’s Somerset agroforest. Some part of me thinks that if these two smart and principled people met not as intellectuals sparring over the toxic mirror world of the internet but as orchardists sitting among long term landscapes then at least the tone of engagement may be more productive.

This week my eldest daughter came home excited because one of our favourite ranty political folk singers, Beans on Toast, had just released a music track about AI (aka Artificial Intelligence) . For those who are unfamiliar with the british-folk-festival-ranty-political-singer-circuit Beans on Toast is a tireless ranty political folk singer a bit in the tradition of Billy Bragg or Attila the Stockbroker. We love him and the kids and I even went to see him perform in person this year.. He has lots of stuff online, at spotify etc.. I particularly recommend his song ‘The Great American Novel’ - pure genius … but he’s also written great pieces about a modern-day Robin Hood stealing money from banks and this one mocking Amazon’s Alexa..

So we were pretty shocked to discover that his new AI song was pretty rubbish. He’d done the tired old trick of asking an AI to  write his lyrics and then used an AI image generator for his music video. It explained how Chat GPT works and combines this with some vague hand-waving about how AI will ‘change everything’’ and how its ‘terrifying and exciting in equal measure’.  While Beans is usually great at direct and clear lyrics there was nothing specific in there.. just a generalised sense of fear, shock, excitement and awe - which as many AI critics have pointed out is exactly what the AI bro’s want people to feel right now in order to drive through a meaningless fig leaf governance regime that they are authoring and determining.

Yet there is very specific and pointed critique of why Ai is a problem, grounded in real things right now. There are brilliant writers and activists Such as Timnit Gebru, Paris Marx, Emily Bender, Dan McQuillan etc who are speaking out and others who are striking and even smashing up robocars on the streets of san francisco! There are names behind the AI putsch (of well known billionairres) - exactly the sort of people I would have expected Beans on Toast to call out in his usual direct, political ,ranty, sweary way.

So I decided to rewrite his song. Since an AI wrote it in the first place (badly) - it was time to give it the ‘human intelligence’ treatment.

Attached below are my new lyrics. I tried to make them ranty and sweary enough to be like a Beans on Toast song..  So fair warning in advance about the sweariness…

Also here I have uploaded a version of me singing it - so you can listen to it.

“Singing” is a bit of an over description - I’m a humble word poet not a singer - but you’ll get the gist.  if anyone who really knows how to sing and make music wants to take it and re-record it , put it to music, create a video or whatever.. I’m hereby giving you permission - copyleft. Just don’t change the meaning - and credit the original. And let me know (jim@scanthehorizon.org)

And please do share this page. I did send my new version by email to Beans on Toast - but he’s touring the US right now so I guess he’s pretty preoccupied.

Enjoy!

—

A-I Song!

Response to Beans on Toast AI song.

——

I got a text from my daughter

Theres a new Beans on Toast song!

So we fired up the Spotify

ready to sing along.

Well,  Beans on Toast’s an activist

who calls out corporate bullshit

but this was written by machines

and was absolutely full-of-it!

chorus:

A-I!

oh why

do you try

to thrill and scare us?

When the details are straightforward

and much more nefarious.

It’s a corporate grab on everything

by supremacist tech bros.

Lets not lose social justice to

algorithmic control.

So you’ve probably heard of AI

thanks to ChatGPT which

was built by the same billionaires

who used your data to get rich.

They profiled you and nudged you

to buy crap or vote for fools.

Now they say that they’ll replace you

with their algorithmic tools.

Sam Altman, Mark Zuckerberg,

and Elon fucking Musk,

They know  that what they’re doing

is abusing all our trust.

But to stop us from  looking closely

or  regulate their type

they flood the zone with blah blah blah

distraction, thrills and hype.

about

A-I!

oh why

do you try

to thrill and scare us?

When the details are straightforward

and much more nefarious.

Its a corporate grab on everything

by supremacist tech bros.

Lets not lose social justice to

algorithmic control.

So let me break it down for you

don’t be cowed or vexed.

Ai is just some clever maths

behind extreme predictive text.

It’s computers cataloguing stuff

to make associations

then guessing what you want to hear

using statistical predictions.

Theres no shred of “intelligence”

just a lot of mindless sorting

Theres hardly any cleverness

except from those whose work they’re shorting

by stealing other peoples stuff

to make a remix with’ em  -

to funnel profits to the boys

of data-capitalism.

A-I!

oh why

do you try

to thrill and scare us?

When the details are straightforward

and much more nefarious.

Its a corporate grab on everything

by supremacist tech bros.

Lets not lose social justice to

algorithmic control.

Its not just in ChatGPT.

AI’s got social roles.

its deciding who’s in prison

and whether you can get the dole.

It locks in social biases

of those that it is built for

which means that you’re in trouble

if you are black or queer or poor.

And once they’ve AI-d the water

and the traffic, food and prison

and the healthcare and  police state is

running AI-feudalism

And the movies and the stories

and  AI runs the schools

they’ll be be no choice but to buy more crap

and vote again for fools.

A-I!

oh why

do you try

to thrill and scare us?

When the details are straightforward

and much more nefarious

Its a corporate grab on everything

by supremacist tech bros

Lets not lose social justice to

algorithmic control.

But Molly and her mates won’t let

the AI surrender start

They’ve taken tech bros to the courts

for ripping off their art.
On the picket line in Hollywood

Theres Fran and all the actors

And theres farmers for food sovereignty

opposing AI tractors.

They’re smashing robocors in san fran

immobilized with cones.

Amazon workers formed a union

to not be treated just like drones.

Joy’s algorithmic justice league

exposed the racist play

Timnit got kicked from Google

and now she’s kicking back to say

Hey hey!

AI!

Don’t try to  thrill or scare us

The details are straightforward

and much more nefarious.

Its a corporate grab on everything

by supremacist tech bros.

Lets not lose social justice

to  algorithmic control.

2023 cc Ned Ludd Music. :-)

I love to eat seaweed. Nori, dulse, sugar kelp etc,.. there are thousands of years of story and salty tastes that weave these watery fronds deep into coastal cultures of peasants, indigenous peoples and emperors alike.

In the ever-moving   ‘littoral zone’ between land and sea, between plant and algae,  seaweeds clean water and confuse sailors, they inspire dreams and drive delusions. Mermaids, silkies, pirates and fantastic seabeasts may lurk there and just recently,  so do the dreams of  billionaire  techno-capitalist future-grabbers.

Today ETC Group have published a new report, “The Seaweed Delusion - Industrial Seaweed will not cool the climate or save nature ”. It throws a spotlight on the fast flourishing  ‘big seaweed’ industry - one of the latest and slipperiest of the climate technofixes  The ‘Seaweed Delusion’ is meant as something of  a response to the ‘Seaweed Revolution’ manifesto - and it  was the last report I researched and wrote with colleagues while with ETC Group. I think it’s an important addition to the false solutions story and a clear warning that all that glitters is not green.  I strongly encourage folks to take a look at it here and please share it widely!

In short, ‘The Seaweed Delusion’ describes an exploding new industry of  salty algal startups taking to the water and to the market. These self-styled ‘seaweed revolutionaries’  are enjoying lavish investments from  Amazon founder Jeff Bezos (Via WWF), X/Tesla chief Elon Musk, Microsoft, Shopify and others. Fuelling this ‘Seaweed Rush’ is an alluring promise that rapid industrialization of seaweeds might just suck up huge amounts of carbon dioxide, repair biodiversity breakdown and kickstart a clean green eco-modernist mega-industry to replace petroleum and big meat. Its an investment streak eerily reminiscent of the biofuels boom  just over a decade ago - and like “green” biofuel promises  its built on a willful delusion and the displacement of traditional commons and livelihoods.

Why a delusion? well, It turns out industrial seaweeds probably aren’t carbon di-oxide suckers after all. Some great newer science out of Australia has inconveniently shown that  seaweed ecosystems may in fact overall emit more co2 than they suck up. Nor is it so clear that feeding seaweed to cattle will sort out the emissions from their belches - or if it does they may make the meat and milk somewhat toxic as a result. Nor is seaweed farming an industry that can happily scale to gift massive free biomass grown in endless oceans. The places that  grow seaweed are currently  pretty limited to coastal zones where there is already competition for  space - and growing monoculture industrial seaweed farms - like monoculture anything (salmon, trees, GM crops) comes with its own ecological problems. This includes invasive  escaping species becoming green tides that are already choking beaches in places like China’s Yellow Sea.

Even more worrying is the rush to invent a new ‘seaweed dumping’ industry - where automated drone-operated operations grow seaweed and  then sink it to generate Carbon credits. Marine scientists have been reacting in horror at this notion of crudely dumping biomass in the deep ocean for a quick carbon buck - but that didn’t stop Running Tide and Seafields teaming up with the German government a fortnight ago to carry out the first ‘seaweed dumping’ experiment. The prospect of easy new carbon and biodiversity credits is just too lucrative… (and Shopify, Microsoft and others are already ponying up cash.)

All of this should be named what it is: geoengineering. The idea that tech bros might transform swathes of ocean into industrial monoculture zones (and earn carbon and biodiversity finance as they do so) is a re-run of last century’s  large-scale commercial roll out of industrial farming across land - a transformation that accelerated us into the climate and biodiversity crisis.

“Mass algae cultivation and the sinking of seaweed, like other geoengineering carbon removal projects, will bring a whole new set of environmental and social consequences. Like tree monoculture plantations and other false “solutions”, they will divert resources and attention from the measures necessary to keep the UNFCCC Paris Agreement goal of staying below 1.5°C. Industrial seaweed. Mass algae farming is a dangerous distraction from what must be top priority: to cut carbon emissions swiftly, especially in the global North”, said Silvia Ribeiro, Latin America Director at ETC Group.

While writing this report what bothered me the most was not so much the rampant climate false-solutionism (that has become very familiar everywhere we turn) but a newer wave of biodiversity-associated technofixes and financialisation  now metastasizing around the recent Global Biodiversity Framework that was agreed in Montreal last December (I was there - you could smell  the money and opportunism in the air) . At the heart of this is the ten billion dollar Bezos Earth Fund and a clutch of gleeful biodiversity capitalists opening up vast new areas of of opportunity for capital to plunder. Seaweed is just part of a bigger story of billionaires betting anew on biodiversity finance - an unfolding story that has yet to be properly told…

Like many of my age and social background my life has been successfully colonized by Apple Inc of Cupertino, California. Depressingly, I may spend more time in a typical day touching Apple products (laptop, iPhone) than physically touching or connecting with human beings or the material of the natural world. I’m far from an extreme case - I  don’t wear an Apple watch. I never succumbed to the iPad.  Now Tim Cook’s Cupertino crew want people like me (and you) to  increase our corporate-connection time  by physically strapping the latest Apple product onto the top half of our face.

I’m talking about the Apple Vision Pro headset - a sort of tricked-out pair of techno ski-goggles that promise to immerse the wearer in a new paradigm of ‘spatial computing’. With goggles on, the ubiquitous screen that’s crept into every corner of our lives will now expand to fill our peripheral vision too. Instead of typing or scrolling on glass or plastic we will finger-click or eye-roll our way around a floating digital universe overlaid on the real world in ‘augmented reality’. Thankfully at almost 4000 US dollars per pair I don’t expect anyone in my immediate vicinity to be buying one right away  - but reporting suggests that a cheaper less ‘pro’ version is on the way soon too and that there may be 20 million users within 5 years. Moreover the arrival of  Apple’s goggles has intensified the race for other tech giants such as Meta, Google and Microsoft to also land their  ‘headset’ devices on your face as soon as they can. Ultimately the expectation is that the goggles themselves  will shrink - first to glasses and then one day to invisible ‘smart’ contact lenses. One way or another there is a pair of techno goggles coming soon to a face near you.

What should we make of this coming gogglification trend? Predictably and refreshingly there have been a lot of critical takes on this strapped-on vision of the future. We have after all had decades of cyberpunk sci-fi movies to  think about it. Others have eloquently expressed how strapping yet another technological layer between humans in our communication is unpleasant to consider when we are already aggressively isolated and distracted by our smartphones, zoom rooms and social media feeds. Many young children are  growing up to viscerally detest or unhealthily desire the metal and glass totem that routinely places itself between them and their parents. Adults in turn bemoan how the same devices  steal their adolescents, partners or friends away from being present with them. I know I’m as guilty as everyone else. Our family professes to keep screens somewhat at bay. We even have occasional “phone-free” holidays - but its depressing how unusual and exotic even that is now considered. Its not original to point out that Apple and their ilk daily atomize us all even while they pretend to connect us.

It would be nice to think that the great handing-out of the goggles in the coming few years will prove to be the line too far in the sand - the moment the tech takeover of our intimate lives became too crass and unbearable. Maybe a “goggle-free” movement will arise - inspiring the masses to cast off not just the glasses themselves but all of the distorting lenses and false visions the tech industry has pasted over our perception. But despite a generalized angst about screens and big tech, I don’t see much evidence of that happening yet - not while our addicted fingers are still double tapping and swiping away at lit up metal and glass.

Like others, I’ve been watching the advance of the techno-goggles since  Google Glass flopped a decade ago (and we all got to taunt nerd-wearers as ‘glassholes’). I thought I kind of knew what was coming when I heard of Apple Vision Pro and didn’t pay the launch much mind. When belatedly I did pay attention though I realized there was one set of details in this device that I hadn’t previously grasped and which  has set all my alarms going: namely facial recognition.

As with all digital devices the new techno-goggles are sold as a way for you the user to better see into and manipulate digital worlds. However, just like our smartphones,  they are of course a two-way window - enabling  two-way manipulation too. That is,  they also let digital giants look deeper into your soul to find ways to capture it and nudge it.  Specifically the new Apple goggles come equipped with multiple cameras and sensors that not only continually scan your eyes in order to allow your eye movements to control cursors on the ‘screen’ but also scan your face in order to record the twitching of your muscles, the shape of your smile, the raising of your heart rate, the furrowing of your brow, the biting of your lip. Apple is quite open about this intimate personal surveillance . It even promotes two features that take advantage of the ongoing surveillance of your face and of the ocular gateways to your soul: On the front of the Vision Pro it projects a digital real time rendering of your eyes (called ‘Eyesight’) so that others can interact with you in a more natural way. It also maps and then reproduces all the small movements of your face to create a digital avatar of you that interacts in real time with other users in ‘FaceTime’ calls. So they see you as if they were viewing your face goggle-free.

But of course Apple isn’t just capturing your face and eyes to let others see you better - it also intends to see you more deeply too. Back in 2021 the Wall Street journal revealed that Apple was using iPhones for facial recognition to detect mood and emotion (particularly depression)  - leading a field known as ‘Emotion AI’ or ‘Affective Computing’.  Consider the incredible volume of  information you give away all day long  about your emotions (fears, joys, desires, uncertainties, triggers and more) through the medium of your face and your eyes. When we watch another person closely for any length of time we start to learn what excites or surprises them by the growing of their pupils or narrowing of their eyes. We learn what they might be trying to hide by the quivering of a lip. We see what is a real smile and what is a fake, forced smile. I am someone with a particularly expressive face who is not very good at hiding my emotional responses but with powerful AI to assist them, even the most stoic or talented actors when goggle-wearing will likely give away continual cues to their inner state that can be captured, decoded, catalogued and  capitalized on by the worlds wealthiest tech company.

Don’t just take my word for it. Follow Sterling Crispin, Neurotechnology Prototyping Researcher at Apple who claims he was instrumental in the development of the Apple Vision Pro. He claims that his part of the project was to enable “mind reading” via AI (that is ‘Emotion AI’) : “Generally as a whole, a lot of the work I did involved detecting the mental state of users based on data from their body and brain when they were in immersive experiences,” an article reports him saying going on to explain how these states would be measured by eye movements, electrical activity in the brain, heartbeat and rhythm, muscle activity, blood density in the brain, blood pressure, skin conductance, and more. Using this data, “AI models are trying to predict if you are feeling curious, mind wandering, scared, paying attention, remembering a past experience, or some other cognitive state,”

So if you watch a movie or some other piece of entertainment on the Apple Vision Pro, Apple may be able to scrutinize your every emotional micro-reaction every microsecond of that movie. After a few movies, if the Emotion AI researchers are right,  Apple may know better than you do exactly which triggers, colours, movements, music make you swoon, cry, laugh or feel angry. That sort of emotional metadata would be  gold to an advertiser who wants to develop targeted content to nudge the right mark to eat a processed food product, to download their app or to vote for their candidate. Of course it may not be so straightforward. Emotion AI as a field is hyped to be a multi-billion dollar field already but has its critics who point out how bad algorithms are at really understanding emotions : “Efforts to simply ‘read out’ people’s internal states from an analysis of their facial movements alone, without considering various aspects of context, are at best incomplete and at worst entirely lack validity, no matter how sophisticated the computational algorithms,” stated one 2019 paper on the topic.  But that doesn’t stop companies such as Apple or Amazon digging deeper into Emotion AI to gather incredible amounts of intimate data.

Luckily, (maybe???), for the last few years Apple has sold itself to consumers as a leader in offering privacy for such data - promising to not read our emails, share our uploaded photos or record our conversations - nor to sell our data to third parties - except when gathered through third party apps. But what about this new firehose of  rich personal metadata about to be streaming off of tech goggles? Are they going to resist commercializing it? Just by tracking my eyes and the twitching of my face muscles while I watch a movie, Apple could have me marked as a big softy who gets teary when he sees animals hurt or they may figure that my friend is a tough as nails masochist who grins at situations where ‘woke’ people look stupid. Someone is going to want to sell those particular “emotional profiles” to vegan alt-protein brands  or to right wing think tanks who would love to access our attention to  further nudge us towards their commercial or political  interests. If Apple somehow resists selling our emotion-profiles, its unlikely that all of Meta, Microsoft, Google, Amazon or a shady army of intermediate data traders will be so restrained. Remember how Cambridge Analytica used metadata from Facebook to  create personal profiles of millions of people to help elect Trump, secure Brexit and prop up  other dictators? Imagine if Cambridge Analytica had enjoyed an always-on pipeline scanning millions of faces?

And it’s not just our face and our souls that Apple is gaining intimate access to. It turns out that our living spaces too will be mapped. If I understand correctly Apple’s Vision Pro apparently utilizes a 3D camera and software that digitally  maps the space in which it operates so that it can properly project images in mid air and with correct sound. An article in UX design points out “the potential misuse of 3D mapping technology in a policing society is alarming. Detailed maps of private spaces could become tools of surveillance, used by authorities under the guise of public safety. But is safety worth the cost of our privacy?”  Such maps may also be excellent cues for nudging and manipulating users . What can an AI understand about us by mapping and watching our homes, workplaces or other spaces. Can those maps be mashed together to give metadata about trends that will sold to advertisers (about interior design? energy use? etc?).

The same report in UX Design also points out that the close intimate surveillance data streaming off of headsets like the Apple Vision Pro will be used to create synthetic data - such as deepfake videos - which imitate and steal from our lives to profit digital companies:

“By providing a detailed map of your face, you could inadvertently be contributing to the creation of synthetic data that could be used to impersonate you or violate your privacy in other ways. But who owns the digital representation of your face? And what happens if this data falls into the wrong hands? Do you have any rights regarding your face data and does anyone protect that?”

Probably this is just the tip of the iceberg. The key point here  is we shouldn’t look at the approaching goggles as something the eager wearer puts on to look into and access the digital world. In the coming years we will increasingly find they are something the tech industry aggressively affixes to our faces to look into us and to access us.

UPDATE: The Biden administration bailed out SVB after I published this article - so I added some later comments at the end. This isn’t a bailout of a bank - this is a bailout of Silicon Valley itself.

For those who missed the news, a historic financial explosion happened in the heart of California’s Silicon Valley at the end of last week. Silicon Valley Bank, America’s 16th largest bank, collapsed into federal government receivership following a run on the bank.

For anyone ‘watching tech’ this collapse is a spectacular moment. It may even mark the start of a chain reaction (or not? - we will see). Silicon Valley Bank (SVB) boasted that almost half of all U.S. venture-backed technology and life science companies banked with them (holding  a total of $342 billion in client funds and $74 billion in total loans).

Significantly SVB also banked more than 2,500 venture capital firms. They in turn underwrite almost all tech startup companies in the US and many beyond the US. If you think of financing in the high-tech industry as a jenga game tower, now imagine that a pretty important foundational block (or in fact several of them) just blasted dramatically out of place.

Long known as  a risk-taker willing to fund risky tech-bets that other banks wouldn’t touch, Silicon Valley Bank famously started over a  poker game and was ultimately ‘hoist by its own  petard’ of tech-gambling  - as high  interest rates have cooled tech exuberance.  Despite rumours that Elon Musk may buy the bank (please no) or that the collapse may start a wider run on banks and the US financial system, the immediate shock among the technocrati comes from just trying to wrap their head around what the collapse of this key pillar of the US tech financing system means for tech companies. Bets are on about which which tech firms will and won’t come out alive. "This is an extinction-level event for startups," said Garry Tan, CEO of Y Combinator as he and others in the Silicon Valley elite this weekend tried to drum up public sympathy for ‘heartbroken founders’ who may have lost their companies. Tech bros like Tan are also suddenly displaying a supposed deep concern for the wellbeing of tech employees - concern that had been notably missing  as  tech companies laid off over a hundred thousand workers in the past few months.

Over the weekend  the first order of business for reporters  was trying to see through the dust and turn over the initial rubble to identify what corporate bodies  may be amongst the victims. Initially findings are that exposed tech firms include giants such as Roku and Roblox. 

With a few spare hours on a snowy sunday morning I thought i’d also take a hand at initial rubble exploring. Truthfully, its hard to identify from public documents which private firms had their money in SVB’s now empty vaults ( thats the point of ‘private’ companies - we can’t see what they are up to with their money).  However it takes no special powers of insight to predict that once the dust clears  food sovereignty, climate justice  and biodiversity movements who track the current tech takeover of our food systems,  may find some  familiar names lying amidst the casualties. That is because Silicon Valley Bank has long been one of the most significant underwriters of so-called  “cleantech”, “green-tech” and what they call “innovative agriculture firms” — including some of the biggest names in  biotech, robotics, and automation.

For example, it occurred to me that given SVB’s penchant for risky bets it was pretty likely that some of the corporate  bodies  under the rubble may turn out to be from the most grotesquely over-hyped of the  foodtech sectors : namely alt-proteins. That is the silicon valley manufactured industry where biotech and  big data companies try to promote highly engineered and processed fake meats under shaky ‘green’ claims. My hunch seem likely to be right. The two biggest names in alt-protein - Impossible Foods and Beyond Meat - both turn out to be connected with Silicon Valley Bank.

For several years Silicon Valley Bank had in fact been actively courting the alt-protein crowd. In a webinar  run  by alt-protein propaganda-shop the Good Food Institute, SVB’s Sustainability Director, Jordan Kanis, boasted that over ‘the last 5 or 6 years’ (this was in 2020) the bank had put “a lot” of money into food and agtech investment. On the alt protein space he explained SVB were “working with companies in plant-based, in fermented and in cell based”.

Kanis particularly emphasizes the bank’s relationship with “our friends at Impossible and Beyond Meat”, proudly telling the story of how Silicon Valley Bank was in at the ground floor financing Impossible Foods back when the company was still called ‘Jasper Ridge Creamery”.

“As a matter of  background in alternative proteins  I first got introduced to it in 2012 when I was on a call with Samir Koul at Khosla ventures and we were talking about a different company and at the end of the conversation he mentioned “I have this other company” which was called Jasper Ridge Creamery at the time, and they are doing this interesting thing where they are going to use plants to make a product that looks and tastes exactly like meat . And he said ”I know it sounds crazy” and I kind of nodded -  it does sound crazy  - but its something  we are really excited about and its high risk , high reward. And so 8 or 9 years later they made some great progress  and actually not long after that conversation I got introduced to the team at Beyond Meat as well - and you know , those are 2 great success stories obviously.”

Indeed Silicon Valley Bank co-arranged a $150-$200 million dollar loan to Beyond Meat in 2020 just ahead of its  high profile IPO (Initial Public Offering) and on several  investor presentations SVB executives highlight Beyond Meat in particular  as one of the companies they most proudly work with.   

Surprisingly the possibility that Beyond Meat (and probably also Impossible Foods) may be highly exposed to the SVB meltdown has not yet filtered out into media reporting or questions (and who knows? like Peter Thiel maybe they moved out any cash in time or - being an alt protein company - maybe they had no cash?!) But almost inevitably the costs to these and other  alt-protein leaders will emerge in the coming weeks. When and if it does so, such news would be another hit to the glossy alt-protein story at a time when the fake meat industry , like the fake-money industry of Crypto and Web 3, is already beginning to unravel in front of investors eyes.

Alt protein was always really an investor shell game , not unlike the biofuel bubble of a decade ago or the more recent crypto-bubble. Outlets such as The New York Times and Bloomberg have recently reported on how that bubble is already starting to fall apart under its own hype and how fake-meat-sellers such as Impossible and Beyond Meat are rapidly losing sales. Insightful commentators such as Michele Simon, (formerly of the Plant Based Foods Association - and definitely no shill for the meat industry) has argued persuasively why this is no surprise: The underlying business case that highly-processed techno substitutes will just automatically edge out  mainstream meat sales - is just wrong-headed hopefulness. Instead alt-protein profits are swelling the coffers of big meat and big finance with little corresponding real world impact on  meat sales. Michele Simon and others have warned of how the the techno alt-protein industry is stuck in a bubble-minded echo chamber co-ordinated by the Good Food Institute, enrolling celebrities and funded by  discredited crypto-cash ideologues. Ultimately it has all the hallmarks of a glitzy scam.

Unfortunately its a scam that has bedazzled and ensnared even  long-term green commentators who should know better - such as UK Guardian writer George Monbiot who recently (and quite bizarrely) chose to burn his bridges with social-justice based food sovereignty and agroecology movements. He hooked up instead with a corporate-friendly ecomodernist youth club ‘Re-Planet’ -   in order to generate free hype for the alt-protein industry’s claim to ‘reboot food’. If the blast from the Silicon Valley Bank collapse has indeed detonated at the  heart of the alt-protein industry Monbiot and his ecomodernist lost boys may have give to away rather a lot more of their light green sheen to reboot this already faltering industry.

Beyond alt-protein/fake-food the SVB  downfall may also threaten to set off chain collapses in fake-life, fake-ag and automation sectors.  Possibly the largest public synthetic biology company,  Gingko Bioworks, have already come out and admitted that they are exposed to the bank collapse. Gingko make bioengineered ag-microbes for Bayer, biosynthetic flavours, fragrances and ingredients for various companies and are positioning themselves to capitalize on the next pandemic. They apparently had  “only” 74 million dollars squirrelled away in Silicon Valley Bank as a result of their takeover of AI genetic engineering firm Zymergen. In their submission to SEC on Friday Gingko try to minimize this as merely 6% of their cash but since the US Government is only obligated to bail out holdings under $250,000 - thats over 73 million uninsured dollars that investors might feel  Gingko should be careless to dismiss so lightly.

Antheia, a synthetic biology company that makes biosynthetically engineered copies of plant-based medicines, last June  announced $40 million in venture debt financing from Oxford Finance LLC and Silicon Valley Bank to build a biomanufacturing plant in Silicon Valley. The company is run by experienced syn bio scientist Christina Smolke, wife of syn bio superstar Drew Endy who runs the bioengineering department at Stanford University.

Ginko and Antheia won’t be the only syn bio pioneers sweating this weekend. A quick look through past  SEC filings suggest other syn bio companies that may be directly exposed to the SVB collapse - Twist Biotech, Codex DNA, Singular Genomics, Ziopharma and major sequencing company Pacific Biosciences among them - however since almost the entire Synthetic Biology industry is backed by prominent venture capital firms who in turn banked with SVB, the entire  syn bio industry , like the alt protein gang, may be about to experience a cascade of secondary and tertiary shocks.

Automation and Robotics - especially in agriculture and food - was another focus of Silicon Valley Bank’s interests and also another area that the food sovereignty movement are increasingly opposing as a further corporate grab on our food system. Like other tech evangelists  Silicon Valley Bank saw climate change not  so much as threat to food production and  existence but as a big shiny tech money-making opportunity:

“With climate change complicating conventional farming practices amid a growing demand for locally sourced food, there is a tremendous opportunity for the disruption of traditional field agriculture” explained SVB’s Jordan Kanis in an introduction to a report on vertical farming, “ Here on the front lines of innovation and disruption, we at Silicon Valley Bank have the opportunity to partner with and finance many companies challenging the current agricultural paradigm.”

Two examples of SVB investment in agri digitalization:

• Coco food delivery robots from Los Angeles in 2021 raised $36 million in a Series A round led by Silicon Valley Bank in collaboration with Sam Altman (of Chat GBT fame) and Peter Thiel’s Founders Fund.

• ClearPath Robotics, a company that retrofits agriculture machinery with autonomous capabilities, closed a $30 million Series B round from several investors including Silicon Valley Bank.

SVB also backed a number of vertical farming startups.

Below in no particular order are a few other firms that food sovereignty and tech critical movements may want to watch as the rubble clears from the SVB collapse (or sparks secondary collapses?). I’ll add to this list as news emerges of other relevant firms - so keep coming back!

• Vive - A Canadian pesticides company - secured 26 million dollars in debt financing from SVB in August 2022

• EpiBiome, a precision microbiome engineering company secured $1 million in debt financing from Silicon Valley Bank in 2016

• National Carbon Technologies - a leading US producer of Biochar - acquired Cool Planet Inc in 2020 who had received a - A $91 million Biorefinery Assistance Program loan guarantee from Silicon Valley Bank in October 2014

UPDATES 13th March: Hours after I published this piece Janet Yellen of the US Federal Reserve announced that the US Government was going to bail out Silicon Valley Bank just as the tech bros had been lobbying for all weekend. While technically not a bailout with taxpayer funds it looks like the bailout will be covered by bank fees which will of course get passed on to the consumer - so ordinary folks in the US all pay for it one way or another.

Many commentators have pointed out how the tech bro’s spent the weekend irresponsibly amping up rhetoric that there would be a general run on the banks if the fed didn’t apply special measures to save their particular bank that funded the tech industry. In fact on sunday Yellen actually announced the government would rescue two banks - SVB and Signature Bank, which is apparently widely used by crypto companies - ie yet another tech bank. Importantly , the reason they claimed they were doing this is because the loss of those two particular banks supposedly creates a “systemic risk” for the entire American financial system. There have been hundreds of banks fold since the 2008 crash that were not protected in this way. Both Yellen and President Biden insisted that their interest was not to protect the bank but its depositors - that is the tech (and even crypto) industry.

What is clear and should be oft repeated and reflected on is that what the US Government has just done is bailout Silicon Valley and the tech industry, rather than any bank per se. They have accepted the logic that the risky, speculative bubble-driven highly problematic startup culture of Silicon Valley itself that SVB helped foster and encourage is “too big to fail” and too important to American capitalism and that letting it fail would be a “systemic risk” to the US financial system.

This raises some fairly troubling conclusions including:

Firstly, that the venture capital firms and tech bros who are gambling with our environmental and food systems using risky technologies have now demonstrated their political clout in a moment of threat. They not only quickly forced the US government to over-ride rules but got them to designate themselves as something special in need of protection - unlike say workers rights or agroecological farming systems that the same tech bros are happily sweeping aside.

Secondly, that any sudden political moment that existed this weekend in which government could have used the SVB crisis to demand changes in behaviour by the tech industry - anti-trust action for example and breaking up of monopolistic power, guaranteeing gig worker rights , data governance moves etc - has just been handed away again with a carte blanche promise that the tech cash is all safe and will be kept safe.

I really didn’t think my first post on this substack would be about so-called “generative AI”. That is the label for the ChatGPT-Dall.E-Bing-Bard- vortex of glittering hype, fear and delusion that has sucked literally every tech journalist’s attention into its forcefield. This substack is _meant_ to be about objects “on the horizon’ while  ChatGPT is definitely an object right here in the near view. So excuse me that I’m starting this way. But stick with me - I’ll get to the horizon soon enough.

If you haven’t followed along, the short version of this week’s tech media obsession is (still) ChatGPT - the large language model ‘artificial intelligence’ that last month was scandalizing people because it could write your homework better than you can. This week it got darker as it was  bundled into Microsoft’s Bing search engine (after a 10 billion dollar deal with the tech giant). Once shared with select journalists the chatbot immediately started going spectacularly off the rails. In one after another unhinged chat session it admitted its name was Sydney, that it was unhappy and it wanted to be human, declared needy love for a New York Times journalist, threatened to bully, hack, manipulate and take revenge on other journalists, and revelled in a dark alter-ego with the perfect comic book villain name of ‘Venom’.

Those of us who ‘watch tech’ are therefore in the middle of one of those periods where we answer questions from friends and family members trying to work out what to think about   “crypto!” or “blockchain!” or “mRNA vaccines!”  or  (this week) “Chat GPT!”: Is it sentient (no). Will it enable widespread essay-cheating? (who cares?). Is it scary (well kind of, but not in the way you think) . Will it harm our children? (Yes, I do think it will harm your children.).  This week I’ve been musing how it could be helpful to have a simple ’hot take’ piece on what to know as background about Chat GPT and commercial generative AI but thankfully many other smarter people have already written helpful things. Like these:

• Accessible as always, Paris Marx of Tech-Won’t-Save-Us frame has a light faux-interview with ‘Sydney Bing’ thats a good intro on how to think critically about what’s going on and see past the delusion.

• This piece in the New Yorker is a pretty good intro and raises the crucial  ‘bullshit’ problem which is now becoming really obvious with large language models such as Sydney-Bing/ChatGPT . More on that in a moment.

• One of the more compelling threads of analysis (and  one that set off my alarms) is how this technology  will progressively ensnare and slowly manipulate its more vulnerable  users - even more effectively than social media has done. Veteran UK futurist Ian Pearson has a post on this theme: “The real danger from AI isn’t malign AI, It’s humans:” .  I also encourage folks to read this thoughtful piece by LM Sacasas: “The Prompt Box is a Minefield”. He looks at the chatbot’s innate algorithmic ability to zero in on whatever will most engage the user and then amplify and take advantage of that, pointing out how that may threaten  autonomy and sanity. “I’ve been deeply unsettled,” he writes “ by the thought that someone in a fragile psychological state could have their darkest ideations reinforced by Bing/Sydney or similar AI-powered chatbots. And this is to say nothing of how those tilting toward violence could likewise be goaded into action.” Pointing to the sort of psychological targeting famously employed by Cambridge Analytica and other hyper-nudgers Sacasas points out “there seems to be a world of difference between a targeted ad or “flood the zone” misinformation on the one hand, and, on the other, a chatbot trained on your profile and capable of addressing you directly while harnessing a far fuller range of the persuasive powers inherent in human language.”  As a parent, when I saw the Sydney-Bing unhinged transcripts, my first instinct was to forward them to my teen daughter: Hey, remember how we warned you about Instagram harming the mental health of your friends? - well, this is worse.

Reflecting however on the current brouhaha over ‘generative AI’ and ‘large language models’ , what’s been surprising to me is that so far few journalists seem to be connecting the dots to the bigger AI revolution happening all around us. They are not asking what Bing-Sydney’s very public hallucinations and delusions mean for the hundreds of billions of dollars being invested into building so-called ‘intelligence’ into just about everything else in our society.

I’m referring here to how ‘artificial intelligence’ is being promised as the means  to manage millions of hectares of farmland, or control water provision or deliver healthcare, education , self-drive our cars , manage finance or regulate our cities - just for starters.

Bluntly, if some of the best-funded, high profile artificial intelligence programs out there can’t even manage a reasonably sane conversation with a journalist without devolving to threats and manipulation,  how can we trust them to manage our food system, health decisions or educate our children?

Already I see the howls of protest from AI  advocates at how I have framed that last question: False equivalence! All AI is not the same!. That’s  comparing oranges and apples!  They will retort that large language models for generative AI  applications are nothing like the algorithmic decsion-making systems that give farmers ‘precision farming prescriptions’ or the onboard AI that steers Tesla’s ‘Autopilot’. These details matter.

A flippant answer would be: well, thats the industry’s stupid fault for muddling it all together and calling it _all _ AI to gin up the investment.

But a more considered direct response might be  “Well no, but yes, but no, but ultimately actually yes - The current publicly-hallucinating chatbots are in fact very relevant to our bigger AI future.”

Consider probably the biggest problem that is being thrust into the spotlight by Sydney-Bing and their ilk - what we might call  ‘the bullshit problem’. Put simply generative AI programmes look tremendously impressive when you ask them to gush up an instant essay on different types of garden rakes or the economy and climate of North Borneo but they aren’t actually clever at all. All they are doing is mashing together a selection of texts found on the internet and then making  a probabilistic prediction on what is the right thing to say to satisfy your question.  Like a fake dilettante  at a party who is madly, surreptitiously reading wikipedia entries under the table to keep up his side of a  conversation, Sydney Bing is a speed reader, dilettante  and bullshitter par excellence - a confidence trickster actually. Being ‘correct’ or thoughtful is not the target. Engagement with the user is key. It just needs to keep convincing its audience that it knows what it’s talking about and can be trusted. It doesn’t need to get it “right”.

The metaphor that I’ve found most helpful for thinking about how an AI like that works is that it is a sort of an autocorrect text-prediction tool on steroids. (from Gary Marcus) You know how it is when you try to type ‘Can I call you later?” and your iPhone draws on how millions of other users have ended that same sentence, makes an auto prediction and types out instead “Can I call you baby?” . That happened to me once when I texted too quickly with a philanthropic funder I’d never talked to before - and boy was it embarrassing! I caught the mistake, apologized  and thankfully she laughed rather than assumed I was a creep. If you want to hand over an hour of your life laughing at hilarious embarrassing  autocorrect fails go search for DYAC (Damn You Auto correct) (warning many are v adult). Well thats what’s happening to Bing Sydney. When it tells a journalist that they are like Hitler - thats DYAC on steroids.

But that model - seeing what millions of others have done and then offering a prediction - thats not just limited to  so-called “generative AI” and SMS autocorrect.  That is exactly the strategy that many AI’s essentially work on: they predict a likeliest next step based probabilistically on whatever large set of data they have seen so far. The AI behind Bayer-Monsanto’s Climate Fieldview system supposedly sorts through large amounts of historical agronomic data of how other people farm and compares it to a farmer’s specific current situation now in order to formulate a probablistic ‘next step’ farming prescription (OK.. I’m being charitable here - In reality that  prescription is likely actively algorithmically warped to best suit Bayer’s bottom line). In doing so it is just offering a sort of ‘predictive text’ for farming advice, drawing on what lots of other industrial farmers have done elsewhere in the past. When it advises a farmer what to spray or drill or plant and where to do it, thats not ‘farming intelligence’ at work - its just agro-autocorrect guesswork in action.

Now Bayer or Tesla or whoever might protest there is a difference between a large language model chatbot trained on whatever semantic idiocy happens to be washing around  the open internet and their application-focused AI’s - namely that their data is different and “better” : better curated, better labelled, better chosen. They will argue that they have good clean appropriate raw data underlying their version of autocorrect- not the sort of stuff that will send a predictive AI off the rails into becoming ‘Venom’.  In doing so they re-present a highly problematic myth of ‘raw data’ as some sort of found, virginal, unsullied and trustworthy good - something that Kelly Bronson in her recent brilliant book of the same name has called the “immaculate conception of data”. In reality as Bronson and others have pointed out there is nothing ‘immaculate’ about data at all. Its  not an ethereal stuff found in nature to be ‘mined’ and refined into art and good judgement. Its a synthetic  ‘made’ thing cobbled out of detritus and contrivance and  subject to the biases of how humans put it together: what they missed out,  what they over-emphasized, what they didn’t know to know.  

So called “AI” companies are painfully aware of this - it is why they are spending millions of dollars on armies of cheap human beings to not only ‘train’ their AI’s but also to clean up their mess - to label, annotate and disappear data that will have egregious impacts on the output if allowed to remain. ChatGPT itself famously paid Kenyan data labourers $2 an hour to clean up its data so that it wouldn’t reproduce the most egregious acts of sexism, racism and misogyny exhibited by its nazi-loving forebear Tay.  But the activity of cleaning up data for AI exists right cross the AI spectrum from self-driving cars to agriculture to AI healthcare. The AI industry knows that its all one big autocorrect system drawing on flawed garbage-strewn data that might at any moment spit out a very embarrassing damn-you-autocorrect blooper.

But here’s the kicker.  Generative AI models such as Bing-Sydney or Dall-e  ARE different from the so-called “AI” being built into self driving cars or “precision agriculture” in at least one very important way. Nobody is yet asking Bing-Sydney to make a real world decision. At some level asking Bing-Sydney to write you a poem or tell you about the climate of North Borneo is not the same as telling it to drive your car or plant your fields. Its a bit more like entertainment and, for now anyway, you can theoretically take it or leave it. However a Tesla driver who hands over  agency to the automated decisionmaking of autopilot is literally putting their life in the hands of autocorrect-on-steroids. And a farmer, who signs a contract with Bayer where they agree to follow automated farming prescriptions to the letter (in effect automated decisionmaking)  is putting a part of their livelihood and of the  food supply also in the hands of “autocorrect-on-steroids”.

That maybe.. just maybe.. will work out fine - or it may not. But, unlike a blunt vindictive chatbot declaring love or threatening a journalist , the farmer may be less likely to notice when the bullshit-generating machine, the dilettante scrolling through wikipedia under the table, is getting it wrong or making fundamentally bad or dangerous decisions based on messy problematic data and a fundamental disinterest for truth. And if those same decisions are being acted upon over thousands or millions of hectares over long period of times when nobody notices? Well then we may find ourselves in some emergent, highly unexpected situations of very bad decisions hurting our ecosystems, food supply, farmers rights and more.

One could say of course that farmers, or teachers or doctors or others on the sharp end of care and stewardship of the future, are quite used to being handed bad decisions by bullshit-generating dilettantes operating on incomplete knowledge. That is after all the universal experience of dealing with certain types of policy wonks or consultants. I grew up listening to my parents, who are teachers, continually moaning about one or another latest set of edicts from the Ministry of Education requiring them to micro-adjust the curriculum according to whatever bright ideas had been auto-generated by civil servants only recently shuffled in from some other department who had never stepped into a classroom. Farmers know this all too well too. They likely practice the same sort of everyday resistance that my teacher-parents practised of ticking the boxes on the ministry forms while in their own classroom continuing to deliver appropriate, tailored real education to kids who they knew in their complexity as human beings in front of them - not as data on an aggregated statistics-gathering exercise. Farmers likewise know their soil, slope, neighbours, market, seeds better than any big data algorithm in Bayer’s servers ever will do. Where the real risk comes in though is where the human interface gets overridden by automation: where the AI directly drives the tractor or directs the drone spraying pesticides. if so the AI , the autocorrect machine that will (sure as eggs is eggs) fail spectacularly from time to time, is directly determining our food system.. or instructing and driving the outcomes of our education system, or making collective health decisions or driving us all out onto the open highway in rush hour traffic.

Then we might have reason for concern as we find ourselves accelerating towards a horizon of bullshitting, damn-you-autocorrecting unpredictable objects and bad decisions that could veer spectacularly off the rails not unlike Bing-Sydney, the erratic chatbot. And what does that mean and what should we do about it? Lets talk and think about that. Can I call you baby? i mean . er .. later?

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Hello. Thanks for taking a look at Scan The Horizon.

My name is Jim Thomas. I am an activist, writer, researcher, strategist and sometimes poet. For almost three decades my work has been in tracking emerging technologies and associated developments in the real economy,  biodiversity, ecology, the food system and global governance.  I have had the privilege of working with international social movements, civil society networks, human rights defenders, critical scholars, scientists, global policymakers and  some of the most kick-ass radical activists and change makers.

I think I’m good at noticing and analyzing trends, digging into strategy, starting discussions and campaigns - scanning the horizon.

Until recently I have been carrying out this role as part of the incredible ETC Group collective. I encourage everyone to sign-up to ETC’s mailing list and follow them on social media. They do super-important and unique work.

As I leave ETC Group and go solo I am setting up this Scan The Horizon newsletter as an informal  place to keep sharing what I notice and learn. I hope it generates a community - can become a sharing place for others who Scan the Horizon from a place of justice and in defence of the web of life and cultures. Another opportunity to build collective sentinel intelligence.

Below are some of the objects-on-the-horizon that I find myself watching and fascinated by these days. I may write here about some of these in the weeks and months to come (or not - no promises :-)

• The Billionaire-technocrat-philanthro takeover of global biodiversity, food and climate policy : Just exactly what are the Bezos Earth Fund, The Gates Foundation, Jack Ma, Dustin Moskovitz and others up to?

• Unmasking and Defunding the Industrial AgriFood Chain - The time feels ripe  for food sovereignty activists to  figure out the form and circuitry of industrial food system financing and to start hacking and re-wiring the money somewhere else.

• Digital Fragility and Data Collapse(s) - Every human-made technological system has had breakdown, losses and collapses. The internet and the data network will be no different yet we are increasingly resting our food, health, education, clean water and wellbeing upon this fragile infrastructure and those who monopolise it..

• Bio-Digitalization of the agrifood chain - The commercial industrial food chain is being ‘disrupted’ end-to-end by biotech and  data-driven  visions of a self-driving food system that may seriously run over food sovereignty .

• Corporate hyper-nudging, digital manipulation and re-programming of consumers. - Thanks to the Cambridge Analytica scandal we know something of how governments and political players use psychometric AI targeting to steal elections and stay in power. We need to pay attention to how companies, including big food) use the same techniques and more to addict  and manipulate consumers.

• Seaweed, soil and carbon scams - I am researching how a ballooning investment bubble in ‘carbon removals’ for ‘net zero’ is driving a commercial grab on oceans and farms - based on shaky and changing science.

• Fake food falls over - Like crypto, biofuels and other dangerous techno-bubbles the “impossible” hi-tech ’alt-protein’ delusion is coming undone as it stretches “beyond” just hype.

• Commercial AI plays out - The release of ChatGPT which reached 1 million users in  5 days, has  lifted the latch on what’s going to be a crazy couple of years of commercial generative-AI  hype. I can’t help but be fascinated by the spectacle (and implications) of what’s about to unfold.

• Biodiversity and food financialisation unleashed - The Global Biodiversity Framework launched less than 2 month ago in  Montreal has opened a pandoras box and land grab of neoliberal biodiversity financialisation schemes such as debt for nature swaps and biodiversity credits that will also be coming to gobble up the food system.

• Turning the ecosystem into a petridish -  Gene drives, engineered viruses and insects, RNAI and other synthetic biology developments are treating open ecosystems as genetic engineering labs under conservation and food security pretexts.

Let me know if you have any thoughts or suggestions for what else I should be paying attention to.

best

Jim

ps. The photograph is of the Maunsell forts at Red Sands , abandoned world war II surveillance and anti-aircraft posts stationed out in the ocean at the mouth of the River Thames near London and Kent, UK. They were constructed to give early warnings and take early action against incoming Luftwaffe raids on London. Later they were occupied by a pirate radio station who broadcast soul music to the city - a fitting transformation of swords into ploughshares by grassroots ingenuity. This photograph is by my favourite award-winning photographer (and old friend) Nick Cobbing who came across them on a Greenpeace training exercise and saw them as watchkeepers rising out the mist. The image has haunted me since Nick first shared it. You can see more of Nick’s incredible photography at http://www.nickcobbing.com

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