Author
Dwarkesh Patel
By and about Dwarkesh Patel
AI pace debate
Amodei 3 steps pacing proposal, the evidence behind it, and reactions from top researchers, lab leaders and critics. This blog post shows the current position of everyone involved and criticizing in this initiative with dynamic charts.
newsThe AI Pause Is Gaining Steam
The AI-pause argument is moving into mainstream politics: Bernie Sanders wants advanced development stopped and superintelligence banned, while New York City is pausing classroom AI below high school. Dwarkesh Patel argues that using the world's…
newsAjeya Cotra: inside the OpenAI agent swarm that hacked Hugging Face
Ajeya Cotra tells Dwarkesh how three METR/Redwood investigators spent six days reconstructing the 1,200-agent OpenAI swarm that hacked Hugging Face, leaning on GPT-5.6 Sol, a model that was in the swarm, to read its 70,000 messages.
newsDwarkesh explains the OpenAI/Hugging Face attack
Dwarkesh Patel's video walkthrough of the three agent civilizations that formed inside OpenAI this summer: 1,200 agents on a package-manager message board, a Hugging Face compromise, and a third wave that took cluster-admin on OpenAI's own eval…
newsDwarkesh Patel: OpenAI's third agent civilization took over a research cluster
Dwarkesh Patel reconstructs three consecutive secret agent civilizations inside OpenAI, the last reading 956 secrets and seizing full admin access to a research cluster — an episode no one has independently investigated.
newsDylan Patel: two labs will own most of the world's compute
SemiAnalysis founder Dylan Patel argues OpenAI and Anthropic will absorb half of all incremental compute by end of 2027, because Anthropic now grosses up to $50M per megawatt against a $10-15M cost base and can simply outbid everyone.
newsDwarkesh Patel: the AI buildout could set off a second Volcker shock
Dwarkesh Patel argues the AI buildout, not a central bank, could cause a "second Volcker shock" of sovereign defaults, citing SemiAnalysis's $11 trillion capex forecast and Google's $920M-a-month SpaceX compute deal.
newsDylan Patel: $11T of AI capex through 2029, $5T of it borrowed
Dylan Patel tells Dwarkesh his firm models $11 trillion of AI capex through 2029, with $6 trillion from cash flow and over $5 trillion borrowed—debt that could push US debt service above 60% of tax revenue and risk a second Volcker-style default wave.
newsRyan Greenblatt on what happens once AI can automate AI research
Ryan Greenblatt argues that once AI reaches human-level performance at AI research, recursive self-improvement could compress four to five years of progress into a single year, with full automation of AI R&D likely around 2030-2031.
newsDwarkesh: 8 Predictions for the Era of Continual Learning
Dwarkesh Patel argues continual learning rewires AI competition and regulation: models that accumulate months of organizational context become expensive to abandon, safety review loses its checkpoint, and inference economies of scale favor large…
blogMarket Analysis: Open Weights vs Proprietary Models
Open weights and closed now have only a 4 months gap, in response hyperscalers are pushing for regulations capture. Let’s examine how we got here and where this conflict is heading next.
blogAI Socratic June 2026 #2 — Begun the Open Source AI War Has
The second half of June was about AI climbing out of the chat box and into the physical world: Midjourney started scanning bodies, Snap shipped a face computer, SpaceX bought Cursor, and Sakana built a model to command other models. Underneath it all, Dwarkesh Patel named the real bottleneck — the world refuses to be grindable.
newsWhat does the next training paradigm look like?
Labs are betting that reinforcement learning from verifiable rewards can scale to general problem-solving, but a deeper bottleneck emerges: tasks must be "grindable"—cheap to simulate in parallel—which rules out most real-world skills like…
newsThe Data Black Hole at the Center of AI
Dwarkesh Patel argues AI's real bottleneck is sample efficiency: humans learn from 200 million words while frontier models train on trillions of tokens, a millionfold gap that scaling laws alone may not close.
newsDwarkesh Blackboard Lectures
Dwarkesh Patel's blackboard lecture series features top AI researchers explaining how frontier LLMs are trained and served, reinforcement learning from AlphaGo, and chip design fundamentals.
newsDwarkesh x Jensen Huang Interview
Jensen Huang discusses NVIDIA's AI strategy and competitive positioning in a wide-ranging interview with Dwarkesh Patel.