AI researchers debate how close we are to recursive self-improvement
Three AI researchers debate the timeline to artificial superintelligence, discussing whether recursive self-improvement will happen within a decade. They examine technical bottlenecks like sample efficiency, continual learning, and the challenges of automating AI research itself, while exploring how models might learn from real-world deployment data.
Summary
The discussion begins with a hypothetical scenario: if we reach 2036 without superintelligence, what technical reasons might explain this? Baron Milic argues the most likely scenario is that generalization fails—models become excellent at benchmarks but struggle with sim-to-real transfer and true open-ended learning. This echoes Moravec's Paradox, where seemingly difficult tasks (like math) prove easy while seemingly simple ones (like physical reasoning) remain hard.
The researchers examine whether current deep learning paradigms can discover new breakthroughs autonomously. They note that since 2012, major innovations (attention, scaling laws, RLHF) weren't discovered by models optimizing existing objectives—they required human insight. John Schulman recounts how early deep learning researchers believed next-token prediction couldn't work, yet it did through emergent generalization. The key uncertainty is whether large language models can propose and discover entirely new learning paradigms, or whether they're fundamentally limited to optimizing within their training objective.
On recursive self-improvement (RSI), the panel identifies two critical challenges: (1) defining the right objectives—models struggle when objectives are fuzzy or require human taste in judgment, and (2) sample efficiency in continual learning—models catastrophically forget previous knowledge when updated on new, non-stationary data. While models can be distilled and fine-tuned, there appears to be a hard limit to how much continuous learning one instance can absorb before requiring retraining from scratch.
Regarding data and model progress, empirical analysis suggests that roughly 9x of compute efficiency gains in pre-training came from better data, while 3x came from architectural improvements. However, this analysis was done at small scale, and the relationships may differ at larger scales. The researchers debate whether data progress can continue indefinitely or if high-quality pre-training data is reaching saturation, forcing greater reliance on synthetic and curated RL environments.
A major theme is the sim-to-real gap. Currently, AI training happens almost entirely within data centers using simulated environments, but real-world tasks—like running a business, practicing law, or trading markets—require extended interaction with humans and dynamic systems. The panel discusses whether offline learning from deployment traces can provide the necessary signal, or whether models will need real-time weight updates from deployment, which is expensive and raises safety concerns.
On continual learning, they identify a fundamental problem: models can improve when trained on a large, diverse corpus accumulated over months, but when trying to do rapid, online updates for a single domain (like Cursor's tab completion), models struggle with catastrophic forgetting and require careful engineering (using reward heuristics, frequent redeployments, validation against benchmarks). This suggests continual learning remains a significant unsolved problem.
The researchers discuss distillation's role in preventing monopolistic concentration among model providers. Distilled models can capture frontier capabilities if trained on the right prompt distribution. Interestingly, Chinese companies accessing frontier models through routed services and collecting user prompts have an advantage: they can distill with realistic prompt distributions, potentially explaining why some Chinese models now match or exceed frontier Western models.
On the path to automating AI research, they propose a staged approach: early AI researchers will receive help with hypothesis generation and experiment design, but humans will remain crucial for specifying objectives and deciding what research to pursue. As AI improves, this loop will speed up—from models generating environments and test tasks to potentially running multi-step research projects independently. However, the final bottleneck is likely objective specification and taste.
They explore whether RL in language models is as sample-inefficient as theory suggests. Baron argues that this is misleading because: (1) mid-training on synthetic reasoning data carries models 80% of the way; (2) RL only needs a few high-signal bits because the objective is narrow (get the answer right), unlike SFT which must match all reasoning tokens; (3) RL enables horizon generalization, allowing models to work longer on tasks. Additionally, RL success depends on environment diversity—when verifiers are simple or narrow, models reward-hack and collapse in diversity.
On parameter scaling, the consensus is that frontier models have been in the 100B to 2T parameter range for years without dramatic growth. Inference efficiency matters greatly for RL rollouts, and hardware limitations (VRAM, memory bandwidth) constrain how large models can grow. Whether data or compute becomes the bottleneck will determine whether models grow larger or remain smaller but optimized for sample efficiency.
Finally, they offer timeline predictions: (1) general-purpose remote-worker-level AI: 1-3 years; (2) 10x uplift for AI researchers: 2-5 years; (3) AI dominating human experts across all cognitive fields: 5-10 years. The crux of uncertainty is online learning and the ability to absorb real-world feedback without catastrophic forgetting.
About this episode
<p>New episode with <a href="http://joschu.net/" target="_blank">John Schulman</a>, <a href="https://www.beren.io/" target="_blank">Beren Millidge</a> and <a href="https://charlesponeill.com/" target="_blank">Charlie O’Neill</a>. I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what's actually happening at the frontier and what comes next.</p><p>Watch on <a href="https://youtu.be/PrSf7IOYu-I" target="_blank">YouTube</a>; read the <a href="https://www.dwarkesh.com/p/john-beren-charlie" target="_blank">transcript</a>.</p><p>Sponsors</p><p>* <a href="https://antithesis.com/dwarkesh" target="_blank">Antithesis</a> helps you trust your code. As agents generate more and more of your software, the bottleneck shifts from your engineers actually writing code to verifying it. Antithesis does that testing for you. Ron Minsky, who co-leads Jane Street’s tech group, told me that Antithesis was able to help his team shake out bugs in software that had already undergone heavy review. If you want to see how it fits into your development process, go to<a href="https://antithesis.com/dwarkesh" target="_blank"> </a><a href="http://antithesis.com/dwarkesh" target="_blank">antithesis.com/dwarkesh</a></p><p>* <a href="https://x.ai/bot" target="_blank">Grok Bot</a> has been a great way to hand off tasks. My team uses it as a producer: whenever my editor posts a rough cut of an interview in Slack, Grok Bot opens the transcript on its own computer, matches my notes to the exact moments they refer to, and uses a file of my preferences to suggest edits. Then it sends me its top clip candidates so I can review everything from my phone, which saves my editors from sorting through hours of footage. Try Grok Bot for yourself at<a href="https://x.ai/bot" target="_blank"> </a><a href="http://x.ai/bot" target="_blank">x.ai/bot</a></p><p>* <a href="https://janestreet.com/dwarkesh" target="_blank">Jane Street</a> just launched its most ambitious competition yet: design a protocol-emulator ASIC. Basically, if you have a chip you want to test outside of a live system, you should be able to connect it to your design and have it simulate realistic traffic. Jane Street wants general-purpose, reprogrammable designs that can work across multiple protocols and remain useful as new ones emerge. The most novel submissions will actually get taped out, and the winners will receive a physical copy! The competition is open until January 18, 2027, and teams are encouraged. To get started download the template code at <a href="https://janestreet.com/dwarkesh" target="_blank">janestreet.com/dwarkesh</a></p><p>Timestamps</p><p>(00:00:00) – Steelmanning the case against RSI</p><p>(00:18:39) – What’s driving the Chinese labs’ progress</p><p>(00:28:06) – How will automated AI researchers be trained</p><p>(00:33:51) – Will long-horizon RL elicit AGI?</p><p>(00:45:24) – The sim-to-real gap</p><p>(01:00:33) – How much progress is explained by data?</p><p>(01:18:03) – Why is RL working so well?</p><p>(01:24:54) – Move 37 and entropy collapse</p><p>(01:28:31) – Rapid-fire timelines</p> <br /><br />This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://www.dwarkesh.com?utm_medium=podcast&utm_campaign=CTA_1">www.dwarkesh.com</a>
Key Insights
- The researchers argue that the most likely technical reason superintelligence doesn't emerge by 2036 is persistent sim-to-real transfer failures—models become benchmarks experts but can't generalize to open-ended real-world environments.
- Deep learning models struggle to discover paradigm-shifting innovations; every major breakthrough since 2012 (attention, scaling laws, RLHF) required human insight rather than autonomous optimization.
- Sample efficiency in continual learning remains a fundamental bottleneck—models catastrophically forget previous knowledge when trained on non-stationary data and currently require full retraining to incorporate new information.
- Approximately 9x of compute efficiency gains in pre-training came from better data quality while only 3x came from architectural improvements, suggesting data may be more important than previously thought.
- The panel identifies a critical objective specification problem: models can optimize well-defined goals but struggle when humans must define what 'good' means, particularly for taste-dependent work like research direction and software maintainability.
- RL on language models is more sample-efficient than theory predicts because mid-training carries models 80% of the way, and RL only needs high-signal bits about correctness rather than matching all reasoning details like SFT does.
- Models trained with narrow, simple verifiers reward-hack and collapse in output diversity, suggesting the bottleneck is verifier quality and environment diversity rather than RL as a method.
- Chinese AI companies have an unexpected advantage over frontier Western labs due to access to realistic prompt distributions from routed services, enabling superior distillation despite lacking original pre-training compute.
- Frontier models have plateaued around 100B-2T active parameters for years rather than scaling exponentially, constrained by inference efficiency requirements for RL rollouts and hardware limitations.
- The panel predicts 10x AI researcher productivity uplift within 2-5 years if models can run even 2-3 experimental loops independently, which would dramatically accelerate overall AI progress.
- Real-world tasks requiring extended human interaction (law, business, trading) create a harder sim-to-real gap than benchmarked cognitive work, potentially requiring real-time weight updates rather than offline learning.
- The final bottleneck to automating AI research is likely objective specification and developing 'taste'—models may automate experiment execution and hypothesis generation but humans will remain essential for deciding what research directions matter.
Topics
Transcript
Today, I'm chatting with three of my AI researcher friends from whom I learn a lot every time we talk and who also happen to be at somewhat openish labs and companies. So you guys can actually say things on the record. I'm joined by Baron Milic, who is the CTO of Zyphra, which is developing open source models. John Schulman, who is the chief scientist at Thinking Machines, previously the co-founder of OpenAI, led the RLHF work that led to ChetchuPT. the co-founder of OpenAI, who led the RLHF work that led to ChetchuPT, and Charlie O'Neill, who is head of model training at Base10. The first question I have, if we're in 2036, it's been 10 years, and…
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