# AI Progress **Entity class:** Technological trajectory **Domain:** Artificial intelligence / science / political economy **Maturity:** Developed ## Definition **AI Progress** is the measurable expansion of machine capability, reliability, efficiency, availability, and domain reach across successive systems. ## Mechanism and Consequence Release cadence matters because social and economic institutions adapt to yesterday’s capability while models, agents, and deployment costs continue changing beneath them. ## Relationships - [[wiki/Artificial Intelligence|Artificial Intelligence]] - [[wiki/AI Acceleration|AI Acceleration]] - [[wiki/Machine Succession|Machine Succession]] - [[wiki/AI Price-Performance|AI Price-Performance]] ## Source Dossiers - [[research/AI Is Now So Good They Will Pay You to Quit|AI Is Now So Good They Will Pay You to Quit]] - [[research/Super Intelligence Week Index of Statements from the White House Luncheon|Super Intelligence Week Index of Statements from the White House Luncheon]] - [[research/Jensen Pushes Back on Doomers Moonshots Live|Jensen Pushes Back on Doomers Moonshots Live]] ## Simple Reminders, Quotations, and Thoughts > "The qualitatively new capabilities in Opus 5.5 are more interesting than the benchmarks. It can generate procedural video, procedural animation, and procedural images. OpenAI has video and image models that generate pixels, while Anthropic appears to be taking a different approach in which code underlies everything. With Opus 5.5, you can generate a high-quality video or an interactive textured game procedurally rather than pixel by pixel." > **— Alexander Wissner-Gross**, *MOONSHOTS Live, September 25, 2026* [[reminders/Information/Code-Based Generation May Matter More Than Benchmarks by Alexander Wissner-Gross|Code-Based Generation May Matter More Than Benchmarks by Alexander Wissner-Gross]] > "In the last twenty-two days, we have had twelve major model releases, including four releases in the last five days. We are averaging nearly one model release every two days." > **— Peter Diamandis**, *MOONSHOTS Live, September 25, 2026* [[reminders/Machine Succession/Major AI Models Are Arriving Every Two Days by Peter Diamandis|Major AI Models Are Arriving Every Two Days by Peter Diamandis]] > What OpenAI did is they put 10,000 [AI] agents for 88 hours on [two qualifying conditions of the Navier–Stokes Millennium Prize problem in fluid dynamics], which is a hundred years of talented human time, and then they had a formal proof. > **— Adapted from Emad Mostaque**, *Impact Theory with Tom Bilyeu, 2026* [[reminders/Scientific Acceleration/Ten Thousand AI Agents Compressed a Century of Mathematical Work Into Eighty-Eight Hours by Emad Mostaque|Ten Thousand AI Agents Compressed a Century of Mathematical Work Into Eighty-Eight Hours by Emad Mostaque]] > "[With a formal proof] you can click a button and it just checks it formally and says right or wrong. So it’s highly unlikely to be wrong. Humans can’t do that because it’s so freaking exhaustive." > **— Emad Mostaque**, *Impact Theory with Tom Bilyeu, 2026* [[reminders/Scientific Acceleration/Formal Proof Can Make Exhaustive Mathematics Machine-Checkable by Emad Mostaque|Formal Proof Can Make Exhaustive Mathematics Machine-Checkable by Emad Mostaque]] > "There’s a theorem called Fermat’s Last Theorem. The official proof of that is 127 pages. Anthropic formalized that proof. It’s 13 million lines of code." > **— Emad Mostaque**, *Impact Theory with Tom Bilyeu, 2026* [[reminders/Scientific Acceleration/Formalizing Fermat Required Thirteen Million Lines of Code by Emad Mostaque|Formalizing Fermat Required Thirteen Million Lines of Code by Emad Mostaque]] > The fact that [OpenAI] could scale [its work on the Navier–Stokes fluid-dynamics problem] by attacking it with 10,000 [AI agents] means that formally verifiable mathematics can absorb enormous amounts of parallel computation: you can literally chuck compute at [the search for a proof]. > **— Adapted from Emad Mostaque**, *Impact Theory with Tom Bilyeu, 2026* [[reminders/Scientific Acceleration/Formal Mathematics Can Absorb Enormous Parallel Computation by Emad Mostaque|Formal Mathematics Can Absorb Enormous Parallel Computation by Emad Mostaque]] > "Starting August 29th, OpenAI had a new type of model training. They had an existing model that they figured out a trick with that made it go from 10% on FrontierMath, a benchmark, to 60% overnight, and [it] just started solving these math problems one after the other." > **— Emad Mostaque**, *Impact Theory with Tom Bilyeu, 2026* [[reminders/Scientific Acceleration/One Training Trick Raised FrontierMath Performance Sixfold Overnight by Emad Mostaque|One Training Trick Raised FrontierMath Performance Sixfold Overnight by Emad Mostaque]] > "When we first came out with the first video models, [we said,] “Do gymnastics.” … Now they do it flawlessly, and they’ve learned physics." > **— Emad Mostaque**, *Impact Theory with Tom Bilyeu, 2026* [[reminders/Scientific Acceleration/Video Models Learned Enough Physics to Perform Gymnastics by Emad Mostaque|Video Models Learned Enough Physics to Perform Gymnastics by Emad Mostaque]] > "You can say, “I want a boat made of sugar cubes and a boat made of concrete and a boat made of wood,” and [a video model] will actually accurately show them dropping into the water: the sugar cube will kind of float, and then it will melt. It’s learning these things. It’s learning representations." > **— Emad Mostaque**, *Impact Theory with Tom Bilyeu, 2026* [[reminders/Scientific Acceleration/Video Models Can Simulate How Different Materials Behave in Water by Emad Mostaque|Video Models Can Simulate How Different Materials Behave in Water by Emad Mostaque]] > "[Math is] one of the most formally verifiable [domains], but even outside of that we’re seeing human-level performance drop. Two days ago, the AIs tied the best humans in superforecasting. There are some humans who are better than others at forecasting things like outcomes of political campaigns [and] who’s going to win elections. Every year [Metaculus has] a superforecasting championship where the best human teams come together and forecast stuff. AI has just tied the best human, and obviously it will beat the best human now. [Forecasting] is something that’s open-ended, because math is kind of deterministic. You’d think a computer should be able to do math better than a human. This high-level conceptual math is a bit different, but [AI has] got there, and now it’s got there in open-ended domains." > **— Emad Mostaque**, *Impact Theory with Tom Bilyeu, 2026* [[reminders/Scientific Acceleration/AI Has Tied the Best Human Superforecasters by Emad Mostaque|AI Has Tied the Best Human Superforecasters by Emad Mostaque]] > I am deeply empathetic to people who have an existential reaction to AI entering their profession, but mathematics is the computation upon which the universe runs. There are few achievements that would unlock more real-world capability than solving mathematics. > **— Adapted from Tom Bilyeu**, *Impact Theory with Tom Bilyeu, 2026* [[reminders/Scientific Acceleration/Solving Mathematics Unlocks the Computation of the Universe by Tom Bilyeu|Solving Mathematics Unlocks the Computation of the Universe by Tom Bilyeu]] > "The actual solution [to Navier–Stokes] isn’t that interesting in real-world terms, but what other people have built around that, like physics-based neural networks, will be incredibly useful for building new materials." > **— Emad Mostaque**, *Impact Theory with Tom Bilyeu, 2026* [[reminders/Scientific Acceleration/Navier-Stokes Tools Can Help Build New Materials by Emad Mostaque|Navier-Stokes Tools Can Help Build New Materials by Emad Mostaque]] > [Solving great technical problems often produces valuable secondary inventions:] we get to the Moon, and then we have pens that can write upside down. > **— Adapted from Emad Mostaque**, *Impact Theory with Tom Bilyeu, 2026* [[reminders/Scientific Acceleration/Moonshots Create Valuable Inventions Around the Main Goal by Emad Mostaque|Moonshots Create Valuable Inventions Around the Main Goal by Emad Mostaque]] > Across mathematics, forecasting, media generation, software, agentic work, and robotics, AI is reaching a new level of capability at the same time. > **— Adapted from Emad Mostaque**, *Impact Theory with Tom Bilyeu, 2026* [[reminders/Scientific Acceleration/AI Is Reaching New Capability Across Six Domains at Once by Emad Mostaque|AI Is Reaching New Capability Across Six Domains at Once by Emad Mostaque]] > "I joined DeepMind in early 2018, shortly after the world's best Go player had been defeated by AlphaGo. Over the next several years, the conversation inside the lab shifted as progress repeatedly arrived faster than even the researchers expected—through AlphaStar, AlphaFold, GPT-2, and then GPT-3. As hardware improves and algorithms keep pace in using it, we see astounding results in domains thought to be decades from being solved. The urgency of AGI and superintelligence risk was relatively niche then, but it has become more consensual inside the labs as timelines have grown shorter." > **— Vishal Maini**, *Palisade Research interview, September 29, 2026* [[reminders/AI Control/Shorter AI Timelines Made Superintelligence Risk Urgent by Vishal Maini|Shorter AI Timelines Made Superintelligence Risk Urgent by Vishal Maini]]