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Interconnects

Interconnects

著者： Nathan Lambert
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Audio essays about the latest developments in AI and interviews with leading scientists in the field. Breaking the hype, understanding what's under the hood, and telling stories.

www.interconnects.aiInterconnects AI, LLC 科学
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  • One resignation turned the embers of AI fear into a wildfire

    One resignation turned the embers of AI fear into a wildfire
    2026/09/10
    As AI became more powerful, it was inevitable that a different, growing group would start to take AI safety more seriously – what we did not know ahead of time, is which set of views they latched onto. We have seen that some of the most extreme views of risk, i.e. moderate probabilities of mass extinction, were the ones that reached the masses. A lot in the AI world is about to change due to this.How did we get here? Why did this quitting announcement reach so far? In many ways, the rest of the world’s views around AI in the past was a dampening factor. You can think about this like the damp ground around a fire. Many people were striking matches for years about AI risk – they’d smolder in their community and largely burn out, going unnoticed. As the stakes of AI have risen this year, from the OpenAI-HuggingFace incident and breakthroughs like the Navier-Stokes result (also from OpenAI), the ground has dried out and the latent energy around the AI discourse has increased. More people not in the industry have thought, “huh, maybe I should care about this AI thing.” The ambient temperature and stakes have been obviously rising.Then, some basic factors of human nature apply, with the most crucial being that fear sells. Fear is the simplest story, the one people cannot look away from. Jacob Coxon was the one who stumbled into this new powder keg, totally unaware of what was going to come. What looked like a fairly innocuous event – another AI researcher quitting citing safety risks – landed into a very different environment and it caught like wildfire. The discussion of existential risk, mass extinction, and the trajectory of AI has traveled further than even the most seasoned AI commentariat would ever predict.There are a set of facts we need to get clear, which paint the picture of the situation. The key Tweets to reference are from Jacob Coxon, the resignation thread, and Evan Hubinger, the source of the >10% extinction risk figure .* There are plenty of AI risks which are likely to cause harm, even if estimating annihilation is useless. It is important to weigh these with respect to the benefits. The entire discourse around existential risk is on very poor footing. At least Evan was clear in his post, with “kill all humans,” but a major problem in the AI Safety discourse is that people talk about existential risks, when they mean very different things (much like how AGI is a vaguely meaningless term). I put the probability of complete extinction as being so low it isn’t worth discussing, but the probabilities of AI caused disasters – e.g. cyber attacks on critical infrastructure or bio-risks – as being worth debating. Throwing this whole discussion out because there are not these disasters yet is a harmful reaction.* Jacob Coxon is acting genuinely and with good intentions. The outpouring of support from more well-established AI researchers who know of him and his intentions of resignation is useful. Many factions of AI turned to scapegoating him individually, based on account metadata, personal factors, etc. These are not useful. Many frontier lab employees genuinely have similar views to him. I’m not sure it’s a majority, but there is a substantial group.* Many frontier lab employees, especially at Anthropic, are out of touch and this will impact their forecasting and/or descriptions of current AI events. I say this without blaming individuals, but it’s a common agreement among my friends not at OpenAI/Anthropic (Ant especially) that people at the labs operate with a religious energy. It’s very common to go through very out of touch interactions with them. I do not blame most of the individuals who get distorted views being part of these companies, but the interactions are wild and spill over into a lot of wack discussions in the AI media ecosystem. Living in this environment that normalizes such out of touch behavior will inevitably distort any human’s understanding of technical progress.Interconnects AI is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.* This was not a mass political campaign, but rather an opportunistic media coordination. For context, the Wall Street Journal had an exclusive story that Jacob coordinated before posting. I suspect that Jacob shared his plan of quitting in groupchats with AI safety advocacy groups ahead of time, e.g. the morning of posting, asking for amplification. This is normal practice, and could have included some prominent politicians. From there, I think it’s more likely that other politicians are bandwagoning on a rising issue. When you combine this with other factors, like Daniel Kokotajlo’s appearance on Joe Rogan coming out the same day – it definitely looks like a very well-executed, coordinated media campaign. This doesn’t mean it’s a conspiracy or a regulatory capture tactic within Democratic political structures. The determining factor ...
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    11 分
  • When will average people feel AI’s impact?

    When will average people feel AI’s impact?
    2026/09/09
    Housekeeping: Paid subscribers to Interconnects now get a permanent 40% discount on my book when purchasing at Manning.com. Access the code at the Interconnects perks page.Many AI optimists tend to compare what is happening in this AI boom to the industrial revolution, or to other periods of rapid technological advancement and diffusion into society. These comparisons fit on the scale of technological change, but miss a crucial factor in how most people are exposed to that change. The problem facing AI is that most people have no super tangible new goods thanks to it and society has more inertia resisting change than in previous eras.I’m writing this coming back online from a few weeks off for my wedding in New England. In this time it would have been very easy to not think about AI at all. The touch-points that average people have to AI products today are fringe, marginally beneficial, or even just very confusing to them (e.g. many people have heard about and brought up the OpenAI-HuggingFace incident, but don’t know what to make of it). People on the positive side think of AI as a way to make fun images, enhanced Google Search, etc. These are very small benefits. On the negative side is an association with addictive social media algorithms, friends of friends addicted to AI chatbots, and a plethora of takes on data centers.AI is still a rounding error in everyday lifeCore aspects of everyday life — family, food, transportation, and entertainment — have few direct impacts yet. It’s a remarkable breath of fresh air to pop out of the bubble and realize how little what is happening really matters today. Being obsessed with AI is a choice that a very few people have yet opted into. For example, the only thing I used AI for in this time was search and creative work (making the pretty seating chart for my wedding guests to find their table). In industrial revolutions past, average people got absolutely life changing outcomes. The First Industrial Revolution in the late 18th century gave access to cheaper clothing, cooking ware, reading material, and a shift to new livelihoods. The Second Industrial Revolution in the late 19th century introduced household machines (e.g. sewing machines), preserved food, indoor plumbing, photography, better light sources, bicycles, and further benefits of manufactured goods and electrification. The list is remarkable — most of these we still use regularly today — and very physical.While even the most optimistic versions of AI will usher in new scientific discoveries, advanced therapeutics for rare diseases, and potentially even sustained economic abundance, these benefits have the risk of being too indirect. How will a common citizen come to credit OpenAI or Anthropic for saving their life, if they went to their family doctor who told them about a new miracle cure?What percentage of Americans will care about OpenAI solving the Navier-Stokes Millennium Prize Problem? It feels very likely in 50 years that the average American’s day to day life looks very similar. Their home, appliances, relationships, and vehicles will be similar (of course, self-driving will continue to diffuse, but that has been developing on a very independent trajectory from the innovations of LLMs). In this time, AI will get a lot of credit. 50 years is a remarkable length of time with how fast everything is changing today in this, AI-focused narrow slice of the world.The most important part of what is happening early in the AI revolution, is building foundational infrastructure, and a general process, which will compound over decades. A major mathematical breakthrough today will look astonishingly minor in scope relative to the advancements that come later in the compounding journey. It is hard to predict what it looks like for every technology you use daily to get faster compounding improvements due to AI. Interconnects AI is a reader-supported publication. Consider becoming a subscriber.Breaking social stasisMuch of the narrative around AI is trying to push people to care, due to this long-term reality of progress, at least as a subconscious motive. This will take a long, long time to get right, and AI’s buildout faces immediate political problems due to this imbalance. Today’s AI is primarily a tool to serve the elite. For knowledge work, which is roughly half of the U.S. economy, AI is as fundamental as electricity (or quickly will be so, with rapid improvements to agents in the next 18 months). It’s highly destabilizing to have such a transformative, productive tool only bring half of society along. It’s not hard for many people to pick up on this — the technology economy booms while life stays otherwise stagnant.In writing this, I learned of Engels’ pause, which is “the period from 1790 to 1840, when British working-class wages stagnated and per-capita gross domestic product expanded rapidly during a technological upheaval.” If we — the leaders of the AI industry ...
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    9 分
  • I wrote an AI textbook — how long until AI can do it better?

    I wrote an AI textbook — how long until AI can do it better?
    2026/08/12
    There are a lot of criticisms of AI writing, but most of them are focused on more creative, high-voice writing like this blog. Those — including my own piece — often argue that it is because good writing is high-voice, has a point of view, has a deep human expression that needs to come across, and or a process of thinking that you peek into with the chosen words. As LLMs get more refined as tools, rather than conversational assistants, I think we are actually going backwards on our goals of having models produce inspiring writing.On the other side of things is non-fiction writing. Filler, copy text was one of the genuinely useful abilities of an LLM (Sam Altman said so much about the early business of GPT-3 on a recent podcast). It has seemed like any flaws here were mostly down to a general lack of intelligence in the models, or some other training issue, and all non-fiction and explanatory text would get obliterated by the rapid pace of progress eventually. Having worked with the models as a writing assistant over the last few years, they’ve gotten a bit better, but it’s worth reflecting on what’s holding them back. Models being stagnant in long-form, non-fiction writing should be alarming to those reliant on models autonomously solving grand, open science problems in the near future. The models today struggle to organize and compellingly present some of the most established science in their area. This seems like a natural prerequisite that we should expect the models to master before they can solve broad, open-ended problems on their own. Until this is solved, the progress of LLMs for science will look closer to solving low-hanging fruit and merging distant connections across fields, rather than any sort of revolutionary insight.This is a somewhat controversial take for someone who is very optimistic about AI’s progress, especially writing it on the day that Anthropic published a blog post on Claude making some progress on the famous Riemann Hypothesis. Scientific problems have a vast breadth, and I don’t think current AI models have as much coverage as many think.Organizing knowledge is a compression. This compression is needed to make insight. Today’s LLMs increase entropy in long-form non-fiction writing, and I don’t see how that can be stacked on top of itself endlessly. They’ll be reliant on humans acting as sort of guides.I am still very optimistic about translation from these narrow forms of science, like the extreme advancements we’ve seen in math, into consistent, broader progress — LLMs are the most powerful assistants scientists have ever used. I first need to explain how observing the models work on such grounded, low-level knowledge problems in writing makes me see a surprising lack of generalization.For more context, I just finished writing a post-training textbook, Reinforcement Learning from Human Feedback (buy on Manning or Amazon). I used LLMs in many ways to support this, from helping wrangle LaTeX formatting for equations, doing extensive copyediting, and creating diagrams for programming languages like TikZ (in LaTeX) or Python.Why have models stagnated in writing quality?I would’ve expected way more progress on non-fiction writing from the models. I almost thought I would look dumb publishing a non-fiction book in 2026, given how things looked in 2024. Today, some of the most famous models on writing ability are pretty old, examples include OpenAI’s big GPT 4.5 and Moonshot’s Kimi K2. In and around these releases, the models have gone from okay to superhuman at other tasks like coding and mathematics. Maybe a closer, but still imperfect, comparison is how the models went from incapable to decent at search and research tasks. The pace of progress on most other skills is steep, but writing well feels orthogonal to most of them. I do not think writing is just ignored, but rather it’s challenging and lacks good training data to specifically intervene on it.There is certainly some low-hanging fruit for making AI models better at writing — such as specialized harnesses like Claude Code, prompts, and training environments that make models spend a lot more inference tokens on the output, but I don’t think these will have a multiplicative impact on ability. Writing well is a very hard task! It’s a shame that we haven’t unlocked inference-time scaling for one of the great intellectual pursuits. Regardless, writing seems very different than what the models are good at.Today, the models seem genuinely horrible at long-form technical writing. They can get a sentence right, but if you try and get them to write an entire chapter it’ll be a mix of sprinkled with confusing wording, muddled in its organization, and generally a bit off. They try to be too cute where they don’t need to be and in the process make random conceptual errors. The models in the near future will get much better at the small errors, especially as models get bigger — which ...
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    13 分
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