Adept

Why AI Agents Don't Actually Understand You — Danielle Perszyk, Amazon AGI Lab

Jul 11, 2026 · 48:54

Danielle Perszyk of Amazon's AGI Lab argues that AI agents fail because they don't truly understand users — they model tasks but not the human mind, so reliability must shift from clicking correctly to aligning representations. She explains how Amazon's AGI Lab, seeded by the former Adept team, builds perception agents that perceive digital environments like humans and interact in real time, moving beyond chatbots and coding agents. The lab is exploring new architectures for episodic memory, social world models, and multi-agent systems where agents fluidly negotiate meaning, inspired by human collective intelligence. Perszyk warns that current AI homogenizes thinking, reducing human agency, and advocates for a diverse society of AIs with different biases to augment rather than replace human cognition. She discusses how aligning AI's goals with inferring and matching human representations could unlock generalization and make agents genuinely collaborative, from automating digital drudgery to transforming education with Socratic tutors.

The new Claude 3.5 Sonnet, Computer Use, and Building SOTA Agents — with Erik Schluntz, Anthropic

Nov 28, 2024 · 1:11:08

Anthropic's Erik Schluntz explains how Claude 3.5 Sonnet achieved state-of-the-art 49% on SWE-Bench Verified by using a minimal agent framework that gives the model full control via tools like bash and file editing, letting it self-correct without hard-coded workflows. He details the importance of tool design over prompt engineering, why XML tags work well, and how computer use reduces integration friction by letting models interact with any browser-based interface. Schluntz also shares his robotics experience, noting that while LLMs and diffusion models are promising for general-purpose robots, reliability at 99.9% and hardware variability remain major hurdles, and he expresses skepticism about self-driving as a business due to high vehicle costs versus driver-equivalent revenue.

Agents @ Work: Dust.tt — with Stanislas Polu

Nov 11, 2024 · 58:53

Stanislas Polu, co-founder of Dust.tt, argues that horizontal agent platforms—not vertical, single-use-case agents—will win the enterprise by letting non-technical employees build their own automation. A former OpenAI researcher who worked directly with Ilya Sutskever on formal mathematics and saw GPT-4 internally before ChatGPT, Polu left in 2022 to start Dust, now an open-source infrastructure layer that connects company data (Slack, Notion, GitHub) to LLMs and lets anyone create simple, instruction-driven agents. He contrasts Dust’s API-first, integration-heavy approach with LangChain’s open-source orchestration and Adept’s browser automation bet, insisting that most useful agent workflows require only a few tool calls and that function-calling quality (where GPT-4 Turbo still leads) matters more than model size. Dust achieves 88% daily active user penetration at some enterprise customers, and Polu predicts the first billion-dollar company run by a single person will emerge once platforms like Dust mature enough to let tiny teams scale to massive output.

The 10,000x Yolo Researcher Metagame — with Yi Tay of Reka

Jul 5, 2024 · 2:18:43

Yi Tay, chief scientist at Reka and former Google Brain architecture co-lead on PaLM 2, recounts his journey from an NTU PhD to co-founding Reka, where a team of just 5 people pre-trained Reka Core on $60M of GPU compute—debating at #7 on the LMsys leaderboard. He explains why he believes the Noam transformer (with SwiGLU, GQA, RoPE) remains the strongest baseline, how encoder-decoder architectures offer 'free sparsity' (a 2× flop-efficiency over decoder-only), and why Chinchilla scaling laws are often misunderstood (training past the optimal compute frontier is routine). He argues that long context will eventually outperform RAG for complex reasoning, and that open-source models like Llama 3 are catching up only because Meta invested in a top-tier training stack, not due to grassroots innovation. The episode also covers his productivity habits (working backwards from a paper title, camping arXiv) and the cultural shock of moving from Singapore academia to Google's impact-driven research environment.

Why Google failed to make GPT-3 -- with David Luan of Adept

Mar 27, 2024 · 49:27

David Luan, co-founder of Adept and former early OpenAI leader, explains why Google failed to build GPT-3—its 'brain credit marketplace' prevented critical mass—and why Adept builds enterprise AI agents prioritizing reliability over generality. He recounts the GPT-2 demo that helped secure Microsoft's $1B investment and details Adept's goal: an AI teammate that can do anything a human does on a computer, targeting 'nines of reliability' for workflows like dispatching a physical truck. Luan contrasts Adept's vertical integration—training fast multimodal models (Fuyu) for charts and UIs—with pure-play foundation model companies that sell tokens, predicting commoditization. He notes Adept is sold out for Q1 and raised $420M, and explains how an augmentation focus creates a data flywheel from human oversight.

Why AI Agents Don't Work (yet) - with Kanjun Qiu of Imbue

Oct 21, 2023 · 1:12:37

Kanjun Qiu, CEO of Imbue (formerly Generally Intelligent), argues that AI agents remain unreliable because they lack robust reasoning and proper abstractions, which Imbue tackles by training foundation models optimized for reasoning over data like code. With a $200M Series B and $1B+ valuation, Imbue builds internal tools for debugging and inspecting agent decision-making, rejecting pure reinforcement learning after their Avalon environment revealed RL cannot handle planning. Instead, they emphasize natural language reasoning for inspectability, use code as a curriculum for reasoning, and design interfaces that let users fork and modify agents mid-execution. Qiu also discusses lessons from earlier startups (Sorceress in recruiting, Embark in VR), the importance of treating team members as creative agents, and her role in co-founding communal living space The Archive to foster scenius.