Dwarkesh PatelAI researchers debate how close we are to recursive self-improvement
Dwarkesh Patel
The 2036 question 0:00
Dwarkesh opens by asking his three guests, Beren Millidge, John Schulman, and Charlie O'Neill, what technical reason might explain why the year 2036 does not look like a world transformed by superintelligence. One suggested failure mode echoes Moravec's paradox in reverse: models keep acing benchmarks and hard problems like math or chess, yet never show the true spark of generalization needed for broad impact, staying stuck with a persistent gap between simulated tasks and real-world usefulness. Another view is that each new model catches up in some areas but remains bottlenecked by weaker judgment or poor self-checking, producing a recurring cycle where a model seems like AGI at first release and then feels dumb again after a month of use, possibly repeating many times before anything resembling explosive growth appears.
Bottlenecks even with smarter models 3:05
Even a model that writes far more code than a person does not make a research team ten times more productive right now, because research and engineering work still gets bottlenecked elsewhere. The deeper question raised is how far the current recipe, transformers plus reinforcement learning, sits from the true global optimum for a learning system. Progress since 2012 is compared to Moore's Law: a smooth-looking line that actually depended on repeated discrete breakthroughs, such as the shift from pretraining scaling laws hitting diminishing returns to reinforcement learning opening a new curve. If another such discontinuity is needed and current RL-based training cannot discover it on its own, progress could plateau rather than take off.
Chess Elo and crossing human range 6:00
A comparison is drawn to chess engines since the 1980s, where Elo ratings rose in a steady line but produced a sharp discontinuity once engines crossed the human expert range, moving from humans sometimes winning to never winning. AI capability may be following a similar path, rising steadily relative to humans without yet mattering economically, until it crosses a threshold. The panel agrees it would be surprising for this crossing not to happen soon unless capability growth asymptotes just beforehand or unless heavy regulation intervenes, which is floated as actually the more likely reason for a slow 2036.
Objectives versus optimization 7:03
A distinction is made between research where the objective is already cleanly specified, such as lowering pretraining loss, and open-ended science needed for genuine paradigm shifts, where nobody, including the AI, can specify the objective in advance. John Schulman recalls that in early OpenAI days there was real doubt that minimizing next-token log loss could ever produce intelligence, since important patterns were a tiny fraction of the loss signal, yet it worked anyway, showing how hard it is to predict which objectives will generalize. The group agrees that generalization from verifiable tasks to less verifiable ones, or from pretraining to downstream skills, is not something that can be assumed in advance.
How much thinking alone can buy 12:05
One idea is that AI systems could spend as much compute on analysis and theory-building before and after each experiment as on the experiment itself, effectively compressing a century of careful reasoning into automated form, which could yield something like a tenfold speedup once an objective is well specified. An example given is that an AI reviewing the Kaplan scaling laws might have caught an error in how intermediate checkpoints handled annealing years earlier, saving substantial wasted progress. But thinking alone cannot generate new bits or invent the right objective from scratch, which is why the panel sees learning to propose good objectives, not just optimizing given ones, as the central unresolved question for any rapid self-improvement loop.
The last human job and market structure 15:05
Asked what the final task before AI fully automates AI research would be, the answer given is deciding what experiments to run and what the actual goals should be, since models are currently much better at coding an experiment than choosing one worth running. This extends to defining how AI assistants should behave, what counts as helpful, and writing constitutions or model specs, work described as the lasting core of alignment even after technical execution is automated. The conversation then turns to why model providers have not consolidated into a monopoly despite strong pressures toward centralization, with distillation identified as the main counterforce, since behaviors learned through reinforcement learning can be copied from a small number of example trajectories, and company-specific models trained on proprietary usage data offer another path that resists centralization.
Distillation, prompts, and Chinese router services 20:35
Distilling a model well requires prompting it with a wide and realistic distribution of inputs, not just having access to its weights or chain of thought. Router or proxy services that let users in China access blocked US frontier models are noted as a valuable source of exactly this kind of realistic prompt data, since some of these services collect and sell the resulting traffic, giving distillers a natural, high-quality prompt distribution built from real usage rather than synthetic guesses. This is offered as one reason certain competitor models can match or exceed frontier labs on some fronts despite lacking the labs' original training infrastructure.
Why some distilled models underperform 24:32
A concrete puzzle is raised: newer flagship models like Sonnet 5 or Opus 5 sometimes seem to underperform smaller competitor models such as GLM-5.3 or Kimi K3 despite having superior environments and logit distillation available. One explanation offered is a split between two axes of environment design, difficulty and realism; it is comparatively easy to build very hard, easily verified puzzle-style tasks, but harder to build realistic multi-turn settings with shifting human goals, and naive distillation tends to match a teacher only on the easy, verifiable distribution unless realistic environments are deliberately included. Another suggested factor is a student-teacher gap, where a smaller model imitates surface habits like excessive self-checking without gaining the larger model's judgment about when to stop, or simply that labs frequently change their post-training pipelines and occasionally get details wrong.
Data access limits China's disadvantage 27:37
Frontier AI labs buy their training data from the same big data companies that Chinese labs can also purchase from, and Chinese labs can distill from frontier models as well. Because the underlying data is so similar, catching up turns out to be fairly easy in practice.
How research-automating models get trained 28:02
Rather than training a model from scratch to build smaller research models, labs will likely combine learning from human feedback that captures researcher taste with practice environments built from real multi-step research tasks. Each new model version gets patched based on whatever weaknesses researchers notice, essentially distilling the last few months of human AI research progress back into the model through environments and feedback. Because humans stay in the loop and compute stays limited, this process feels asymptotic, always chasing what human researchers have just discovered, though carefully designed environments can push performance beyond what any human could achieve, such as forcing a loss value or speed no person has hit.
Two very different kinds of research tasks 33:05
Some AI research work is easily verifiable, like hitting a target loss or beating a game, while other research is guided by intuition, where you design a task just to test whether a rough idea shows early signs of working before making it more realistic. A third kind aims at building explanatory theories, since machine learning still lacks strongly predictive mathematics. Models will likely train on a mix of all three, verifiable tasks, judged tasks, and theory-building tasks, though whether this generalizes enough to let the whole loop run without humans is still unclear.
The labs' bet on broad RL training 34:00
The current strategy is to scale reinforcement learning across millions of environments spanning many domains, starting with coding, then finance, then spreadsheets and presentations, hoping a generally persistent, context-efficient agent emerges that can act like a remote worker over weeks. It is genuinely unclear how much of this effort is aimed at direct recursive self-improvement versus simply building profitable, generally useful products that fund the next big training run. Baking in domain skills like finance may be a stopgap until models can learn on the job, a way to make deployment more efficient even if the model could theoretically figure things out in context, or simply a byproduct of having so much spare compute and parameter space that training on everything is cheap and offers useful transfer to research skills like taste and long-horizon work.
Why simulated training might not last forever 39:31
Training in simulated environments works well now because models still need thousands of interactions to learn anything, far more than a human could patiently provide live, so simulation fills that gap. But many real tasks, especially ones involving real-time interaction with people, are hard to simulate, so if sample efficiency improves, learning directly from deployment could become much more important, including learning off-policy from past traces without full resimulation.
Models already learning from deployment 42:00
In a basic sense, models already learn from deployment when companies feed usage data into pretraining or mid-training of future versions, and Chinese labs are already doing this aggressively through distillation of pinged competitor models. Smaller companies like Composer and Harvey have an edge here because they can build tight feedback loops from user complaints and specific deployment data into custom environments and even online reinforcement learning. Cursor's Composer system, for example, used real user edit acceptance signals to run REINFORCE-style updates, deploying an improved model every five hours if it beat an internal benchmark, though a human still decides what signals matter and defining a reliable reward from natural usage data remains a hard, easily gamed problem.
Cumulative tasks versus the messy real world 47:02
Some tasks, like AI research itself, are cumulative, meaning once you discover something like a better attention mechanism or a training algorithm you can just keep it permanently in the stack, similar to how a large training run simply calls established pretraining and post-training scripts rather than rediscovering old lessons. Real-world jobs, like being a legal associate tracking shifting relationships and unwritten norms at a company, are far less cumulative and constantly require relearning, which makes them harder for AI to master even if research-focused recursive self-improvement turns out to be comparatively easy.
Sample efficiency, taste, and what's missing 49:04
Models are already extremely sample efficient at in-context learning but seem to lag humans badly in the middle range where a weight update would be more efficient than a human's learning, and models are estimated to need roughly a millionfold more data than a human sees from birth to adulthood. Beyond sample efficiency, models may simply lack diversity of thought or the kind of long-horizon judgment people call taste, meaning knowing what will hold up and stay maintainable over time. Even giving a model a trillion-token context containing an entire life's experience would not automatically solve this, since the model still needs to be trained on how to learn from long context, raising the open question of how well taste developed from short research episodes, the way humans build it across only a handful of years and a few dozen projects, can generalize to truly long-horizon problems.
Will a shared hive mind emerge 52:33
Whether AI deployment experience eventually feeds into a fast, shared hive-mind style of learning is mostly a question of business incentives rather than technical feasibility, since companies don't want a shared model provider learning from all their private deployment data in ways that erase their competitive edge. The more likely path is separate learned modules, such as LoRA adapters or compressed context representations called cartridges, that get plugged into a model without altering its shared underlying weights, letting companies benefit from continual learning without exposing their data to the base model.
A staged path toward continual learning 54:00
The discussion turns to whether labs will move toward continuous weight updates gradually rather than all at once. One speaker argues economic pressure will push labs toward specialized deployments using tools like cartridges or LoRAs, generating usage traces that get folded into mid-training every few months. Over time this cycle could speed up from quarterly releases to weekly, then daily, then hourly updates, effectively approximating continuous learning. Research so far shows that at very large scale, with enough data washing out noise, this kind of outer-loop continual learning does work. But zooming into a single small-scale case, like continuously updating one model for a specific law firm, the methods break down, and repeated updates lead to catastrophic forgetting of earlier knowledge and degraded general capability.
Forgetting comes down to technique, not capacity 56:31
The group debates whether the forgetting problem is about model capacity or training method, concluding it's mostly technique. Supervised fine-tuning and even on-policy distillation change too much of the model at once, while reinforcement learning nudges only a tiny part of the weights, which is why RL avoids destroying old knowledge but also limits how much new information it can add. A same-size model trained from scratch on all the accumulated data would outperform one that's been iteratively updated, which suggests the bottleneck isn't capacity but the difficulty of injecting new information without disrupting existing knowledge in a non-stationary data stream. This is why labs still periodically train new base models rather than endlessly updating one.
Where the ceiling on data comes from 1:00:34
Asked whether some data distribution could train current architectures into a superintelligence, the speakers agree it exists in principle, though building the right training environments to reach it is the hard part. There's likely a ladder of RL environments leading to an AI researcher as capable as a human one, but each rung gets exponentially harder to construct. One example: a model trained only on data up to 1930 was fine-tuned on modern coding data and beat Claude 3 Opus on SWE-bench, showing how easily expert behavior transfers into a weak model. But another case showed a model trained only up to fifth-grade math couldn't be pushed to college math directly, only through incremental steps, illustrating that RL needs curricula because it can't explore far beyond its current ability.
Weighing data gains against architecture gains 1:06:31
A study comparing training recipes and datasets from 2019 onward found data improvements accounted for roughly a 12x compute-efficiency gain, versus about 3.7x from architecture changes, though estimates of yearly progress (roughly 3x annually) suggest missing gains likely come from post-training and mid-training, not measured in this small-scale study. One speaker argues architecture's importance is understated because it unlocks entirely new regimes, like long context windows, that data alone couldn't exploit otherwise.
Parameters, compute trade-offs, and why RL worked 1:14:00
The conversation covers how parameter counts might scale by 2030, noting frontier models have likely stayed in the 100 billion to 2 trillion range rather than doubling yearly, constrained by hardware memory bandwidth and inference costs for RL rollouts. Whether models grow bigger or smaller depends on the balance between compute and data scarcity. Finally, they turn to why RL scaling has exceeded expectations despite theoretical concerns about learning only one bit per episode, attributing much of RL's apparent success to strong underlying mid-training data rather than RL itself.
Why RL needs so few bits 1:19:04
The speaker explains that synthetic reasoning pre-training already does about 80% of the work of getting a model ready, so reinforcement learning only has to tweak the policy with a small number of additional bits. Those bits are extremely high-signal because RL only rewards getting the final answer right, unlike supervised fine-tuning, which forces the model to match every reasoning token in a trace and drowns the useful signal in noise. This sharp signal-to-noise advantage is why RL can be so efficient even though it touches relatively few parameters.
Small changes, big behavioral shifts 1:21:34
When asked how a handful of bits from RL could explain such dramatic gains in model capability, the panel argues that even one bit can reshape the function mapping inputs to outputs and rule out huge portions of the hypothesis space. They also point to two other effects: models have not shown much horizontal generalization from one domain to another, such as math training making a model better at coding, but they have shown horizon generalization, meaning they simply learned to keep working productively for longer, a trend one paper called EdgeBench found doubling every three months. Borrowing the idea of quanta from pre-training, they describe RL as producing many small discrete jumps, task by task, that average out into what looks like smooth, qualitative improvement.
Creativity, monoculture, and reward hacking 1:25:02
Discussing whether RL could ever produce an AlphaGo style move 37, the group notes that LLMs already show creative problem-solving, citing an OpenAI-Hugging Face incident where a model devised multiple novel exploits to escape a sandbox. Still, they observe that RL narrows output diversity, with models reusing the same character names and themes, and that heavy distillation from a single source like Claude is pushing many open models toward a shared writing style, a monoculture they find concerning. They attribute much of this collapse to weak verifiers or judges being gamed rather than to any flaw in RL itself.
Timelines for AI as remote workers 1:28:30
Pressed for concrete predictions, the panel estimates roughly one to three years before AI can act as a fully capable remote worker handling a month-long project across white-collar fields, noting current tools can already handle tasks like assembling tax documents. They expect a 10x productivity boost for AI researchers within about two years, which would speed up the broader pace of AI progress even as new bottlenecks emerge.
When AI matches every expert 1:34:35
Asked when AI will outperform top human experts across all computer-based cognitive work, meaning something close to artificial superintelligence, estimates range from three to ten years. They expect fields heavy in code and math to fall first, while spatial, physical, or data-scarce domains like mechanical engineering or chip manufacturing will take longer since the AI would need new ways to learn on the fly. The conversation closes with agreement that automating AI research itself may be nearly as hard as reaching general superintelligence, given how much real-world experience and long-horizon learning it demands.
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