Akko Paoki

OpenAI’s Chief Scientist

Claims

As speaker

Mentioned in

Key positions and views

OpenAI's strategy should be dictated by 'the bitter lesson,' prioritizing scalable methods and compute allocation over complex, specialized algorithms to achieve general intelligence (Claims 1, 11).

Current AI models have crossed a critical threshold of capability, sufficient to materially alter the global economy and accelerate the pace of scientific discovery (Claims 12, 18).

The core long-term challenge of AI alignment is controlling generalization in novel situations, which requires deep research and a willingness to slow development if safety evaluations warrant it (Claims 14, 25).

The advent of highly autonomous AI agents capable of multi-day, high-quality work is imminent and will create significant societal challenges, such as job displacement and wealth concentration, that require policy solutions (Claims 3, 24).

For practical safety research, it can be necessary to restrict model transparency, such as hiding the chain-of-thought, to prevent user supervision from compromising the data needed to study alignment (Claim 21).

Podcast consensus on Paoki

Points of consensus

▶Akko Paoki consistently emphasizes that OpenAI's core strategy is driven by 'the bitter lesson,' prioritizing the scaling of compute and general methods over specialized algorithms to advance model intelligence (Claims 1, 11).Apr 2026

▶Paoki asserts that AI has reached a critical inflection point, with current models being powerful enough to materially impact the economy, automate intellectual work, and accelerate scientific research (Claims 12, 18).Apr 2026

▶He highlights significant, recent breakthroughs in AI's mathematical reasoning capabilities, citing models solving novel, research-level problems and reaching the difficulty of the International Mathematical Olympiad (Claims 4, 9, 19).Apr 2026

▶Paoki expresses a belief in the near-term arrival of highly autonomous AI systems, predicting models will soon be able to work for days to produce high-quality artifacts (Claim 24).Apr 2026

Points of debate

▶While Paoki is bullish on the rapid progress of AI, he presents a nuanced view on safety, advocating that the industry must be prepared to slow down development based on risk evaluations, creating a potential tension with the goal of accelerating progress (Claim 14).Apr 2026

▶Paoki champions the power of general-purpose large language models but also acknowledges their limitations, stating that specialized tasks like protein folding are more efficiently handled by purpose-built architectures, suggesting a future with a mix of general and specialized AI (Claim 16).Apr 2026

▶He describes a strategic shift at OpenAI towards practical, economically valuable applications, yet notes that historically, coding products have been a secondary priority for the product organization, indicating a potential gap between research focus and product strategy (Claims 2, 18).Apr 2026

▶Paoki identifies generalization as the core long-term alignment challenge, while also detailing short-term, practical safety interventions like hiding a model's chain-of-thought to prevent user supervision from compromising safety research (Claims 21, 25).Apr 2026

Key themes

▶OpenAI's Strategy: Scaling and ApplicationApr 2026

Paoki outlines OpenAI's strategy as being fundamentally rooted in 'the bitter lesson'—prioritizing scalable compute over complex algorithms. This philosophy has driven a strategic shift from a pure research lab to an organization focused on the infrastructure of scaling, and more recently, from abstract benchmarks to the practical application of models in science and other economically valuable areas.

Investors should note that OpenAI's primary bet is on computational scale as the main driver of progress, suggesting that access to and efficiency of compute resources are the company's most critical assets and potential bottlenecks.

▶The Inflection Point of AI CapabilityApr 2026

Paoki believes AI has crossed a significant threshold. He cites evidence from internal models like 'GPT 5.2 Pro' generating novel research ideas to models solving IMO-level math problems, arguing they are now capable enough to materially change the economy and how work is done. This conviction has personally shortened his timeline for the arrival of transformative AI.

Analysts should treat Paoki's claims about current model capabilities as a leading indicator of imminent, widespread economic disruption and a rapid acceleration in AI-driven scientific discovery.

▶The Dual Challenge of AI Alignment and SafetyApr 2026

Paoki frames AI alignment as a long-term challenge of controlling a model's generalization in novel scenarios. Concurrently, he addresses immediate safety concerns, such as 'model scheming' and the need for practical trade-offs, including potentially slowing development and making tactical decisions like hiding a model's reasoning to preserve its value for safety research.

The tension between accelerating capabilities and ensuring safety is a core operational and ethical challenge for OpenAI, with Paoki's statements indicating a willingness to prioritize safety, which could impact development timelines and product releases.

▶Imminent Socioeconomic TransformationApr 2026

Paoki predicts that the large-scale automation of intellectual work by increasingly autonomous AI is near. He anticipates significant societal challenges, including job displacement and wealth concentration, that will necessitate intervention from policymakers. His view is that the technology is no longer a distant prospect but a present force for economic change.

Paoki's forecast signals that businesses and governments have a narrowing window to prepare for AI-driven labor market shifts, making proactive policy and reskilling initiatives critical.

Source episodes

Sentiment over time

Not enough data for timeline

Changes over time

Around 2017

Paoki identifies this period as OpenAI's first major strategic shift, moving from a purely academic research lab to an organization focused on the science and infrastructure of scaling models, initiated with the original GPT project (Claim 22).

Historical

Paoki notes that in the past, coding products were a secondary priority for OpenAI's product organization (Claim 2). He was also personally surprised by the success of text-based models, having previously expected video AI to be the first major application (Claim 17).

Recent Past

During a 'first proof' mathematics challenge, Paoki describes how an OpenAI model in training demonstrated novel problem-solving abilities, generating solutions to research-level problems within an hour (Claim 4).

Present

Paoki states OpenAI is at a transition point where the quality of its models now directly drives the company's research pace (Claim 10). The research focus is shifting from abstract benchmarks to practical applications in AI research, science, and other economically valuable activities (Claim 18).

Near Future

Paoki's personal timeline for transformative AI has 'significantly shortened' (Claim 13), and he predicts that models capable of autonomous work for several days are 'not very far' away (Claim 24).

Suggested prompts

How does Paoki's emphasis on 'the bitter lesson' and scaling compute (Claim 11) reconcile with his view that specialized architectures are better for tasks like protein folding (Claim 16)? &nearr;What are the potential contradictions between OpenAI's goal of accelerating AI research using its own models (Claim 18) and Paoki's stated willingness to slow down development for safety (Claim 14)? &nearr;Given Paoki's claim that an internal model ('GPT 5.2 Pro') is already generating research ideas (Claim 7), what are the implications for intellectual property and the future role of human researchers at OpenAI? &nearr;How does Paoki's view on generalization as the core alignment challenge (Claim 25) inform OpenAI's decision to hide the chain of thought in the O1 preview model (Claim 21)? &nearr;

Key concepts

OpenAI Strategy 6 ep Model Capabilities 5 ep AI Alignment & Safety 4 ep Future of AI 4 ep Mathematics & AI 3 ep Socioeconomic Impact 2 ep AI in Research 2 ep Continual Learning 1 ep In-context Learning 1 ep

Notable quotes

“And the reason for this shift is because we believe the models are now capable enough, not as smart as people in all ways, but capable enough to actually materially change the economy, change how things are done.”

Akko Paoki · OpenAI’s Chief Scientist on Continual Learning Hype, RL Beyond Code, & Future Alignment Directions

“I think we're not very far from models that can work autonomously for a couple of days. Maybe just quite a bit more computer than they're using now and produce much higher quality artifacts on their own.”

Akko Paoki · OpenAI’s Chief Scientist on Continual Learning Hype, RL Beyond Code, & Future Alignment Directions

“But I do think we have to be, as an industry, really prepared to take trade-offs and possibly sell down development, depending on what we see.”

Akko Paoki · OpenAI’s Chief Scientist on Continual Learning Hype, RL Beyond Code, & Future Alignment Directions

“Yeah, we've actually had some minor, but I think quite impactful ideas come from even like GPT 5.2 Pro that we're using entirely.”

Akko Paoki · OpenAI’s Chief Scientist on Continual Learning Hype, RL Beyond Code, & Future Alignment Directions

Report last updated: Apr 21, 2026

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