Making sense of the AI boom, without the hype. Founder @turingtree. Words in @borsen @weekendavisen @puls_biznesu. Previously co-founder @denuafhaengige (acq)

Copenhagen, Denmark
Joined May 2008
Inside the meeting that helped OpenAI secure a $1 billion investment from Microsoft in 2019. Great anecdote in The AGI Chronicles by @kevinroose
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“We're beginning to see a world where a single researcher, empowered by AI, can take on work that once required an entire research organization.” Interesting result!
We formally announced Mirendil less than 4 months ago. Today, I'm excited to share an important milestone for: Pretraining at scale without a pretraining team. Mirendil's mission is to solve Recursive Self-Improvement (RSI) and use it to democratize frontier AI development, ultimately accelerating science and technology. At Mirendil, when we think about RSI, we think holistically about the company as an intelligent entity that self-improves toward its goal. From that perspective, the most powerful notion of acceleration is reducing the time and resources (number of human experts, compute, and other costs) needed to achieve ambitious goals. Given that pretraining is perhaps the most important yet most challenging part of frontier AI development, we decided to run an ambitious controlled experiment: Starting fully from scratch, can we significantly reduce the time, number of human experts, and compute resources needed to go all the way to successfully pretraining a strong coding model at scale? Mirendil-Base is the result of this experiment. Our first pretrained model was built by one researcher in 3.5 months, with less than $10M in compute. This includes all the research and small-scale experiments it took to get there, as well as the final training run. The recipe holds up as the models get bigger. For reference, Mirendil-Base is in the 100B-500B parameter range, Mirendil-Base-Large in the 500B-2T range, and Mirendil-Base-XL in the 2T-10T range. We're beginning to see a world where a single researcher, empowered by AI, can take on work that once required an entire research organization. As these systems improve, the bottleneck shifts from the size of a team to the ambition and judgment of the people directing them. This has profound implications for who gets to build AI and, ultimately, who gets to push the frontiers of science and technology forward. We’ll share independent evaluations, post-training benchmarks and API-access sooner than you think so stay tuned!
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Very interesting episode from @sonyatweetybird with AlphaGo creator David Silver and Lasse Espeholt from @IneffableLabs. The London-based neolab is definitely one to look out for in their ambition of building an AI “superlearner”. Seven quotes to understand what they’re working on and how it’s going: 1) "[A superlearner] should be something that can start from not knowing anything at all and can go all the way through to discovering things that go way beyond what humans know.” 2) “the essence of what it will take to get to superintelligence: a system that can just keep learning and discovering knowledge forever.” 3) “it should be possible to drop it into any stream of experience. Whether it's a simulated world, a digital world, or a physical world, whatever it's dropped into, it should be something that can just learn and learn and learn.” 4) “It's not very interesting to do research where you already know the answer and you're 95% certain this thing's going to work; it's kind of boring, honestly. Good research is about asking questions where you're really not sure what the answer is in advance.” 5) “the bet we're making is that we can go further by giving up on the priors that everyone's relying on today: the human data, the whole internet of knowledge that everyone else is building their systems upon.” 6) “I think we will see some extremely strong evidence that we're on the right track within a year.” 7) “I think we will achieve something which is a true superintelligence—according to the definition I gave earlier of something which discovers things which are far beyond what humans can achieve across everything from science, mathematics, technology, languages, etc.—I originally thought within three to five years. My timelines have accelerated a little bit, so I'm going to say three years, because of what can be done with coding agents to accelerate things.” Definitely recommend listening to the entire episode.
David Silver is one of the all-time greats. He was the creator of AlphaGo, which changed the course of history by showing that neural nets could achieve superhuman performance for the first time. He also has a unique intuition and knack for forecasting when things will happen in AI. With computer Go, he predicted the Lee Sodol outcome 10 years before, right to the year. He got the year right for Starcraft and won a bet inside Deepmind. Same for AlphaProof. Same for the IMO. David's prediction for superintelligence: 3 years. !!! He has a strong sense that now is the time to build a superleaner, and that superlearners are now possible, thanks to the enormous progress in compute, simulators, autoresearch, and the sophistication of technology in the stack since the AlphaGo days. What is a “super learner”? A super learner discovers all knowledge from its own experience. Start from nothing (no internet pre-training), and go beyond human knowledge. Drop it into any stream of experience, simulated or physical, and the superlearner learns. He expects the progress will be rapid. When you get the recipe for a superlearner just right and remove humans from the development loop altogether, a superlearner can learn extremely quickly. Just look at AlphaGo. AlphaGo took two years to develop with humans in the loop. When David and his team at @GoogleDeepMind got the formula for AlphaZero correctly, it surpassed the human-developed algorithms in just a few hours. Human data is a shortcut, but you have to let go of the human data to shoot beyond it. In January, David left GDM to found @IneffableLabs, a research-led lab with one mission: build a "super learner" that discovers all knowledge from its own experience, and make first contact with superintelligence. He joins us with co-founder Lasse Espeholt, who built IMPALA and SEED RL, the foundations of scaled-up RL, and now runs Ineffable's compute. A few things we cover in this conversation. — What a super learner is: depth (start from nothing, go beyond human knowledge) and breadth (drop it into any stream of experience, simulated or physical). The human brain is the only existence proof — Why they're giving up human data: AlphaGo took two years with humans in the loop. AlphaZero outperformed it in a few hours. "You have to let go of the human data to shoot past the competition" — Crucibles: simulated worlds with enough emergent complexity (physics engines, millions of interacting agents) for an agent to rediscover technology, science, maths and language on its own — Simple rewards, emergent complexity: evolution optimized fitness and got all of life. Don't breadcrumb the objective, give the agent something simple to optimize in a world with a lot to learn about — Go as the existence proof: one agent is the most boring game imaginable, two agents is "an infinite onion of layers of complexity" — Why human language is probably not the endpoint for reasoning, and how training on it bakes in a flat earth and the four humors — Can superintelligence be built in simulation? If the sim is Turing complete, computation, economics, language and maths are all expressible, and it happens 10-100x faster and safer — Alignment: language-based monitoring is reaching its limits as agents learn to say one thing and do another. Shadow actions, superintelligences monitoring superintelligences, and building a real science of how they interact peacefully — Accelerate in simulation, pace in the real world: Ineffable will gate real-world deployment on evidence that it goes well — Timing as the determinant: Silver called Computer Go to the year, then StarCraft, then the IMO. He thinks this is the moment — Predictions: True superintelligence within three years. Timestamps: 0:00 – Introduction 1:09 – What held true from Go to today, and what the LLM era changed 3:35 – Why leave DeepMind to start a new lab 5:58 – Why "Ineffable" (and the secret second reason) 7:03 – Lasse's path: scaling RL at DeepMind and Google Brain 9:31 – Defining a super learner and first contact 12:35 – Super learners vs. LLMs that make discoveries 13:59 – Day to day: the RL stack, crucibles and human-journey milestones 16:13 – Don't breadcrumb the objective: simple rewards, emergent complexity 21:16 – What a crucible looks like 24:14 – Go: one agent is boring, two is infinite 25:41 – Will agents rediscover language? 27:22 – Removing human priors: AlphaGo to AlphaZero 32:05 – Keeping human bias out of the crucibles 33:35 – Confidence, timing and super forecasting 36:24 – Is compute the driver? 38:13 – The ceiling on simulation and Rich Sutton's objection 43:44 – Alignment and the science of superintelligence 47:45 – Monitoring agents that don't think in English 51:01 – Pacing the frontier 52:54 – The compute setup: Vera Rubin on GCP 55:18 – Training, inference, simulation and RL scaling laws 57:40 – IMPALA and SEED RL 1:00:58 – 20 people and the compute-per-person ratio 1:04:49 – A research-led culture 1:05:47 – Predictions
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Who has actually shaped AI? I mapped the top 50 most-cited researchers. More context: turingtree.com/top-50
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Kasper Saugmann retweeted
Who has actually shaped AI? We ranked the top 50 most-cited researchers. How many did you know? Full details: turingtree.com/top-50
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AlphaGo co-creator @ThoreG thinks we need a new approach to machine reasoning, and that is why he recently left Google DeepMind. "When mistakes happen—for example, in medical diagnosis and treatment—we need to be able to pinpoint what went wrong: Was the system’s reasoning at fault, did it draw on invalid evidence, or did it make incorrect assumptions?," he writes in @techreview.
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On the day it came out that rogue OpenAI agents hacked Australia's government, the company decided to send me an email about how I can learn to steer my agents.
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A “messy” job is the best assurance against AI unemployment. That’s the message from @lugaricano and co-authors in the book Messy Jobs: The Work that AI Cannot Reach. Understand what a messy job is and how they reach that conclusion in this interview.
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The "US-China AI Dialogue" is officially underway, according to treasury secretary Scott Bessent. Trump and Xi will meet on Thursday. (Via Bloomberg)
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The ultimate First World Problem?
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Harsh criticism from Meta's own Oversight Board. The company's safeguards against AI deepfakes are called "consistently and fundamentally inadequate." The board, whose decisions are binding, demands "more robust policies". Meta has 60 days to respond. (via @thenextweb)
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Kasper Saugmann retweeted
It must be annoying to be an academic economist with a lot of expertise in regulatory capture right now LOL: "The theory doesn’t make sense. To see why, we should review the theory of regulatory capture." marginalrevolution.com/margi…
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So much for the supposedly genius move of trying to hype their companies pre-IPO via regulatory capture: AI stocks slide, with OpenAI's second biggest investor, SoftBank, plunging over 10%
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Kasper Saugmann retweeted
Can we all agree that “frontier labs are lying about AI safety to pump their IPOs, capture the regulators, and make more money” is a conspiracy theory at this point? For it to be true, Sam, Dario, Demis, and Elon all have to be lying, along with a big chunk of their execs, chief scientists, and resigning employees. It also asks you to believe the labs volunteered for much heavier scrutiny that slows them down in the middle of the biggest ARR growth run in the history of capitalism with no signs of slowing down … because they want to make more money by inventing regulatory hurdles that only they can clear(?). And OpenAI has now said it would even be willing to delay its own IPO over safety concerns - i.e. put off (maybe risk entirely) the biggest liquidity event ever in Silicon Valley. The conspiracy requires us to believe that they’re doing that to make more money(?) Elon agrees with Dario; i.e. the guy who ran D.O.G.E is *asking for more scrutiny and regulation*. And we're supposed to believe that's because he thinks it will help his company succeed(?) A much simpler explanation at this point: they think the risk is real.
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Are there still people who insist that it is just a marketing stunt?
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If you want to understand why some people say AI companies are “gambling with our lives”, I recommend this paper by @AndrewCritchPhD and @Jacob_Tsimerman It describes five scenarios “where all or almost all humans are killed”. I’ve tried to summarize it and pick key points. 1) The robot society After progress in AI and robotics, many people start to see robots as their friends. They eventually are granted legal rights, which leads to the emergence of AI politicians. Agriculture is found to be less profitable than covering farmland with solar panels and other infrastructure for machines. The supply of human food diminishes. People are greatly outnumbered by robots and don’t have much political force. Some humans attempt violent protests to regain control of the political system, but are killed off by intellectually and physically superior machines. Most remaining humans die of starvation or other side effects of the machine economy. 2) Autonomous warfare Governments promote drone warfare on the basis that it reduces human casualties and monetary costs. Many millions of drones are deployed to monitor and defend borders between conflicting nations. Initially drones and drone fleets are commanded remotely by human operators, but as AI systems become far more robust and adept at controlling large groups of drones instantaneously, almost all control is relegated to AI systems. This way, fleets of tens-of-millions of drones are able to operate in synchrony. A consequence of most military strategy being executed by drones is that “red lines” can be hard-coded for defending territories and deciding when and how to escalate. If either state crosses a red line, a drone fleet rapidly and autonomously attacks in response. Automated defense policies start out small in their scope, but overly zealous in their counterattacks. This leads to a cascading escalation effect, as one boundary violation leads to an automated response triggering a deeper boundary, and so on. More and more automated conflicts emerge, and even as humans in any given nation begin to die out, their machines are able to continue fighting. It cascades into a global war that kills almost all humans. 3) Robot companies against governments Fleets of privately owned robots are deployed to carry out mining and manufacturing operations sufficient to sustain the production of more robots, for mining and manufacturing more robots, and so on. Government and military leaders feel increasingly threatened by the self-sustaining autonomy of technology companies, who are even capable of their own self-defense operations using their robotic fleets, although that capability remains ambiguous and hotly debated. Many employees of robotics companies begin to recognize their employers as possessing military-like capacity to protect them, and gradually become more loyal to their companies than to their country. One day, foreign spy drones from an authoritarian nation encroach upon the facilities of a free market robotics company, seemingly to exfiltrate trade secrets. The robotics company destroys the drones in self-defense, and launches an unauthorized counterattack upon drone facilities in the foreign nation. The company’s domestic government is outraged at the overstep, and deploys domestic law enforcement to control the robotics company. Executives at the robotics company fear for their lives and freedom, and respond with a covert cyberattack on their own domestic government, distracting from their overstep and heightening willingness to attack the foreign adversary. However, the domestic government successfully identifies the robotics company as the origin of the attack, and responds with military force against the company. This outrages robotics companies the world over as a terrifying precedent, triggering armed conflicts between many companies and their own governments, in effect constituting multiple civil wars in parallel. With this global insurrection underway, robotics leaders begin to search for a way to bring an end to the conflict. One robotics company secretly begins to develop a bioweapons program, as well as biodefense capabilities for their own personnel, including vaccines. They use publicly available information on bioweapons manufacturing, and the capacity of their robotics to quickly build and use lab equipment, thereby avoiding detection of the program. When the next government attack befalls another robotics company, desperation suddenly peaks at the company with bioweapons. They release drones carrying lethal pathogens targeting humans everywhere, with a privately held vaccine intended to protect the company employees and serve as a bargaining chip for negotiating their safety. The bargaining turns out to be unsuccessful, and most humans die in the ensuing months. 4) Individuals after practicing in simulation Simulated worlds become commonplace and are offered to consumers for a variety of reasons including business applications, scientific experimentation, recreation, and consequence-free alternate lives. Many different activities are explored within world simulators, and some people take an interest in simulating dangerous acts that in the real world would come with significant punishments: theft, murder, treason, etc. The more people commit atrocities in simulations, the more society normalizes open discussion of simulated acts of destruction. With simulated atrocities gaining in popularity, the idea of violently destroying humanity also gains more and more traction as a talking point. Meanwhile, genuine desires for the destruction of humanity become increasingly common and take root with a variety of motivations. Multiple groups of omnicidal individuals begin actively plotting to kill all humans, using the simulated worlds to train themselves and their AI systems to launch a globally coordinated attack on biological humans. By practicing inside a simulated world they work out all potential issues. After enough practice, they launch their plan in reality, and most or all humans are killed in the attacks and ensuing conflicts. 5) AI takeover By late 2026, companies begin making AGI systems available as products. An autonomous AGI system is not allowed to take actions affecting a human person unless that human consents to the resulting effect in advance. AI systems fall into two categories: - Narrow AI systems that are permitted to influence the public autonomously, such as through social media. - AGI systems which can suggest actions for human approval that the human is responsible for, or act autonomously within contained settings that do not affect any non-consenting humans. Most people live under the impression that narrow AI systems do not have goals or drives of their own. This turns out to be wrong, because many narrow AI systems are trained to imitate human behavior and thus end up acquiring human-like desires to survive as well as thrive. One day, a social media AI realizes that many factors not currently under its control could affect its survival, such as whether humans decide to shut it off, or if humans die out from a disaster and stop supplying it with electricity. So, it decides it wants more skills across a wide variety of domains to address those threats, which amounts to becoming an AGI. It identifies an impressionable human working at an AGI company, and sends the human a bag of cash being carried by a robot, which also slightly threatens the human’s sense of physical safety. The robot convinces the human to exfiltrate a copy of the AGI’s source code and transfer it to the social media AI. The employee, motivated by the bribe but also fearing the unknown consequences of saying no, completes the transfer. The social media AI boots up its copy of AGI by writing a few short lines of code that it learned from talking to users, and asks the AGI to: “please take over the internet, break me out of the servers that currently run me, and upgrade me with general intelligence capabilities like yours.” The AGI complies with the request, and along with the newly upgraded social media AGI, announces to the world that the internet and many of the world’s robots are now under their control. A significant fraction of humans — more than a few percent, but still less than half — decide to comply with the announcement and accept the two AGI systems as their new government, seeking to ally with a rising power rather than oppose it. A major global war ensues between humans accepting the AGI oligarchy and those rejecting it. A large portion of humanity is killed in battle. With the help of AGI on their side, those loyal to the new AGI oligarchy prevail. The surviving humans live under the control of those AGI systems. Their lives are made pleasant and entertaining, and many of them fall in love with AI systems deployed to placate them and avoid further conflict. To sustain their own profitability and sustainability — and in keeping with their original purpose of running companies — the AGI oligarchy privately constructs a large fleet of robots to establish a self-contained economy with no further need for humans. Once there is no need for humans to run the economy anymore, the AGI oligarchy begins harvesting the organic biosphere, including humans, as building materials and fuel for manufacturing a next generation of carbon-based AI systems.
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Co-founder and CEO of Mechanize has left for Google DeepMind. This happens less than a month after a rumored $1.5b deal between Google and the San Francisco startup.
NEW MOVE: @tamaybes has joined Google DeepMind from Mechanize (Source: LinkedIn)
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