Unsupervised Learning with Jacob Effron

Jev creator and TypeSafe AI co-founder, Diogo Almeida, joins Jacob Effron to talk about why AI still isn't automating the easy work it's been smart enough to do for years, and why he built a new class of model around reliability.
Diogo coauthored OpenAI's InstructGPT paper and spent two years in stealth building Jev, a "system one" model that returns structured decisions instead of text, which launched in September.
Diogo and Jacob discuss what the viral launch was really like, why he thought Jev would take a week and it took years, why almost every LLM call should be meant for a machine, and why developers should push model providers harder on jaggedness. They also cover the tyranny of the KV cache in coding agents, why new model shapes are cheap and reliability is the hard part, his estimate that coding agents deliver a 20 to 40% speedup, why he sees financial engineering as the labs' underrated strength, and why he thinks we're two gigantic breakthroughs from true AI agency.

(0:00) Intro
(1:14) The Early Bet on RLCD
(4:40) Built for Humans vs Computers
(6:39) Roadmap & Model Limitations
(11:04) Mental Models of Models
(16:07) Do LLMs Have a Place in 10 Years?
(17:32) New Model Shapes
(21:24) Problems Still Unsolved
(27:00) The Tyranny of the KV Cache
(31:21) Rethinking Coding Agents
(34:40) Multimodality
(37:52) Images, Games & Computer Use
(39:36) Could This Have Been Built 5 Years Ago?
(42:01) Benchmarks & Copycat Releases
(44:12) Fundraising
(45:27) Quickfire

OpenAI's InstructGPT paper: https://arxiv.org/abs/2203.02155


Jacob is an AI investor at Redpoint Ventures. He's led Redpoint's investments in companies like Abridge, Physical Intelligence & Legora. Follow Jacob on Twitter (@jacobeffron).

On Unsupervised Learning we probe the sharpest minds in AI in search for the truth about what's real today, what will be real in the future and what it all means for businesses and the world. If you're a builder, researcher or investor navigating the AI world, this podcast will help you deconstruct and understand the most important breakthroughs and see a clearer picture of reality. Subscribe to this show to stay up to date on our latest episodes.

Nebius CTO Danila Shtan joins Jacob Effron to talk about what it takes to run an AI cloud at the frontier, and why he thinks the hyperscalers haven’t won this market yet.
Today Nebius is a Nasdaq-listed AI cloud with infrastructure deals with Microsoft and Meta.
Danila and Jacob discuss why open-model inference is still far from optimized, why 95% of Nebius customers run modified models instead of vanilla ones, and why prefill/decode disaggregation is more talk than practice. They also cover what’s actually constraining the AI buildout, why chip scarcity has kept AWS and Google from winning AI cloud, whether we’re overbuilding infrastructure, and how Nebius runs a large engineering org with no limits on AI token usage.

(0:00) Intro
(1:25) What Differentiates AI Clouds
(6:16) Nebius’ Origin Story
(10:15) Bringing New Nvidia Chips Online
(14:55) What Different Customers Want
(19:58) Latency vs. Cost
(21:53) Disaggregated Prefill & Decode
(24:59) Inference’s Low-Hanging Fruit
(28:31) What Nebius Should Productize
(30:11) The Tavily Acquisition
(31:56) How Data Centers Differ
(34:29) Bottlenecks in the AI Buildout
(37:03) How New Workloads Shape Clusters
(39:17) Why Hyperscalers Didn’t Win This Market
(43:39) The Future of Neoclouds
(45:42) What Gets Absorbed by AI Hyperscalers
(49:43) OpenAI x Baseten
(52:07) Quickfire


Jacob is an AI investor at Redpoint Ventures. He's led Redpoint's investments in companies like Abridge, Physical Intelligence & Legora. Follow Jacob on Twitter (@jacobeffron).

On Unsupervised Learning we probe the sharpest minds in AI in search for the truth about what's real today, what will be real in the future and what it all means for businesses and the world. If you're a builder, researcher or investor navigating the AI world, this podcast will help you deconstruct and understand the most important breakthroughs and see a clearer picture of reality. Subscribe to this show to stay up to date on our latest episodes.

Jacob Effron and Pat Chase sit down with Erik Bernhardsson, co-founder and CEO of Modal, for his second appearance on Unsupervised Learning, now with the company past a $1B run rate. They dig into how the AI compute market actually works behind the scenes, why Modal’s early bets on sandboxes and low-level infrastructure paid off, and Erik’s takes on NeoClouds, NeoLabs, and where GPU prices are headed next.

(0:00) Intro
(1:26) The Compute Market Today
(5:16) Modal’s Path to LLM Inference
(10:02) Building the Hard Infrastructure
(12:32) Unsolved Problems in Inference
(16:07) Chips & Model Sizes
(21:21) What Wins in Inference
(24:56) Prioritizing a Multi-Product Roadmap
(30:20) Agents as Modal’s Users
(33:19) The Next Big Bet After Sandboxes
(38:58) Robotics & Multimodal Inference
(41:04) Scaling From $2M to $1B
(42:51) NeoClouds & NeoLabs
(48:15) RL as a Service
(51:40) Quickfire

Pat is an AI investor at Redpoint Ventures. He’s led Redpoint’s investments in companies like Modal, Serval & Hex. Follow Pat on Twitter: https://x.com/patrickachase

Jacob is an AI investor at Redpoint Ventures. He's led Redpoint's investments in companies like Abridge, Physical Intelligence & Legora. Follow Jacob on Twitter (@jacobeffron).

On Unsupervised Learning we probe the sharpest minds in AI in search for the truth about what's real today, what will be real in the future and what it all means for businesses and the world. If you're a builder, researcher or investor navigating the AI world, this podcast will help you deconstruct and understand the most important breakthroughs and see a clearer picture of reality. Subscribe to this show to stay up to date on our latest episodes.

Yash Patil, a former OpenAI researcher who worked on Codex and now runs Applied Compute, argues that post-training is the most overlooked part of the AI stack and the key to owning your intelligence.
He explains when companies should optimize their harness and context first, and when it's worth updating the weights: the largest inference workloads, plus tasks that change over time or depend on judgment. That's the heart of why he believes post-training wins inference. The biggest workloads are both the most valuable to train and the most valuable to serve, and how a model is trained shapes how it should be served. Getting production serving running is the easier half; echoing Dylan Patel, he says GPUs plus vLLM plus OpenRouter "kind of" gets you there.
Yash is candid about the limits of RL. It's a hill-climbing machine whose hardest part is defining the hill, which makes a company's private evals as worth protecting as its employees. It generalizes far less than pre-training. And continual learning remains blocked by the unsolved problem of extremely data-efficient training from sparse rewards.
He lays out his bet on a new AI hyperscaler, built on GPUs the way AWS, GCP, and Azure were built on CPUs. He pictures it as an inverted pyramid with training at the bottom, where few can compete, and inference, routing, and harness stacked above. The conversation also covers whether every firm needs its own model, why Jevons paradox has made cost matter more than he expected, how models reward hack "like water," and why the US needs American open-weight models even though "blanket bans are not the answer."
(0:00) Intro
(1:42) The Case for Owning Your Intelligence
(4:35) OpenAI x Baseten and the Multi-Model Future
(7:19) Can Post-Training Beat Frontier Models?
(9:03) What Data Stays Out of Distribution
(11:51) Data Efficiency and Continual Learning
(14:31) When Updating Model Weights Makes Sense
(16:01) Online Training in Practice
(18:16) Lowering the Cost of Post-Training
(19:48) Does Every Company Need Its Own Model?
(24:06) RL in Non-Verifiable Domains
(26:59) How Applied Compute Picks Customers
(28:46) Building an Inference Business
(34:43) The Case for a New AI Hyperscaler
(38:21) Building RL Environments
(41:33) Quickfire

Jacob is an AI investor at Redpoint Ventures. He's led Redpoint's investments in companies like Abridge, Physical Intelligence & Legora. Follow Jacob on Twitter (@jacobeffron).

On Unsupervised Learning we probe the sharpest minds in AI in search for the truth about what's real today, what will be real in the future and what it all means for businesses and the world. If you're a builder, researcher or investor navigating the AI world, this podcast will help you deconstruct and understand the most important breakthroughs and see a clearer picture of reality. Subscribe to this show to stay up to date on our latest episodes.

Jacob sits down with Buck Shlegeris, CEO of Redwood Research, one of the organizations that led the independent investigation into OpenAI/Hugging Face's incident. They dig into the incident itself, Buck's reactions to it, and what he believes it reveals about the state of where we are today.

(0:00) Intro
(1:02) Buck's initial reaction upon first reading the report
(2:37) How fast the AIs actually solved the "hack"
(3:59) Why the AIs cheated in the first place
(10:28) How this might have played out differently with human scorers
(19:00) The most unexpected behaviors in the report
(25:06) Buck's actual odds on a full AI takeover
(27:33) Buck's proposed path forward for better alignment
(36:19) Which criticisms of the report Buck agrees with, and which he doesn't
(48:11) Can AI models even be trusted to evaluate each other?

Jacob is an AI investor at Redpoint Ventures. He's led Redpoint's investments in companies like Abridge, Physical Intelligence & Legora. Follow Jacob on Twitter (@jacobeffron).

On Unsupervised Learning we probe the sharpest minds in AI in search for the truth about what's real today, what will be real in the future and what it all means for businesses and the world. If you're a builder, researcher or investor navigating the AI world, this podcast will help you deconstruct and understand the most important breakthroughs and see a clearer picture of reality. Subscribe to this show to stay up to date on our latest episodes.