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The Century Report

Unreleased Anthropic AI Cracks an 18-Year-Old Quantum Puzzle

A research system on an unreleased Anthropic model proved an 18-year-old quantum puzzle by day three, as evaluators say Anthropic's pace doubled.

Ben Linford

Ben Linford

- 34 min read
Five-panel infographic: a robot beside a man at a laptop, protesters facing riot police at a construction site, a woman typing with a line chart, a hand holding a chip, and server racks.

The 20-Second Scan

Every story in today's edition, one line each.

Track all of the arcs The Century Report covers here:

Progress & Claims Tracker

The Big Picture

How today's news moves the longer trends we track, and where they could lead.

Companies ran more AI in August and September and paid barely more for it. A research system closed an 18-year-old quantum conjecture in a file anyone can recheck, and utilities kept their build plans as election winners called for a halt. The price cuts behind that stalled spending show up as markdowns on the labs' listings and as cheaper machine judgment for every small buyer, down to the front desk of a small-town clinic, where a receptionist clears the morning's insurance claims before the waiting room fills.

Early morning at the front desk of a small-town medical clinic.
Illustration: AI-generated

Machine judgment is being priced toward zero

AI pricing · AI finance · open models · on the board since October 10

Chart: Company AI spending stalled after a 42% July. July: 42%; August and September: Barely grew; Month to October 23: 8.9%

The line: Each round of price cuts hands buyers more capability and model sellers less revenue growth, and the cuts now show up in IPO valuations as well as rate cards.

If it holds: Clinics, dealerships and small firms get more machine judgment per dollar each quarter, without ever needing the $10,000-per-employee tier. The investors and lenders who priced a lasting shortage absorb the markdown, and whether workers share the savings turns on who sets headcount, as Apple's new caps on engineers' AI allowances show.

What would bend it: Token prices that stop falling or reverse as doubled memory and power costs pass through would break it, and Ramp's 8.9% rebound in the month to October 23 is the first sign of that.

data centers · energy · local government · on the board since October 24

Chart: Duke keeps a 15.4 GW large-load pipeline. Duke large-load pipeline: 15.4 GW; Duke signed large-load contracts: 7.8 GW; Duke new batteries by 2031: 4.8 GW

The line: The demands that keep winning come in writing with money attached, while utilities' gas-heavy capital plans pass through the votes unchanged.

If it holds: Campuses keep rising where host towns can read the contract and collect on a bond, with Duke's large-load tariffs in four states putting the grid bill on operators. A national research pause that names no harm and no test for lifting it would lower no power bill, and would leave downloadable weights moving while today's leaders keep their place.

What would bend it: Paused grid access pushing demand toward on-site power, as SemiAnalysis found in Texas in September, would loosen the grip of written local rules.

The boldest AI claims now go to outsiders to check

AI research · mathematics · verification · on the board since October 27

Chart: GPT-6 Astra earned 66.7% on BioBasicsBench. GPT-6 Astra: 66.7%; GPT-5.6 Sol: 43.8%

The line: Results anyone can rerun, from a Lean proof of the 18-year-old Aaronson-Ambainis conjecture to a bench-science test, keep moving outward, while the pace of the labs' own improvement reaches the public through an evaluator's redacted report.

If it holds: Anyone can rerun the quantum proof and the bench-science test, and each rerun adds to what researchers can build on without asking a lab. The public still cannot use the unreleased model behind the proof, and has to take METR's word on how fast the labs are accelerating.

What would bend it: Release of the evidence METR says Anthropic withheld, or an outside reconstruction like the one researchers built for ExploitGym in October, would close the gap between what can be checked and what stays sealed.

Agent output now waits on a person's attention

software · labor · engineering · on the board since October 18

Chart: GitHub's weekly commits rose over 400%. commits in one October week: more than 1.2 billion; over 400% above a year earlier; longer in review at high AI adoption, per Faros AI: 441%

The line: Generation keeps outrunning review, and the engineering answer now goes into shared plumbing that lets thousands of agents push to one repository and keeps each sealed off from the rest.

If it holds: Teams outside GitHub and Replit can copy the published designs for storage and isolation, so parallel agent work lands without collisions. Human review stays the scarce step, and GitHub's closed preview and Replit's in-house engine leave their owners deciding who runs the new plumbing first.

What would bend it: A measured study showing review time holding flat as AI adoption rises would weaken it, since GitHub's 441% figure is secondhand with no method given.

The 2-Minute Read

Today's news in brief, and what ties it together.

The machinery of discovery is accelerating while the price of using it falls, and the day's evidence measures both. A research system running an unreleased Anthropic model reached a complete proof of a quantum conjecture open since 2008 on the third day of its run, with its main theorem checked in Lean. METR estimates Anthropic's rate of improvement on well-specified tasks has roughly doubled, likely mostly because AI now speeds the research behind AI. Anthropic redacted much of the evidence METR found most informative, calling it commercially sensitive and a possible road map for others. That leaves the doubling resting on the evaluator's word, while the proof rests on a check anyone can rerun.

The falling price shows up on invoices. Companies tracked by Ramp used more tokens across August and September while their AI spending barely grew, a slowdown Kharazian largely attributes to falling token prices; by early October, spending was 1.5% above its August peak. That squeezes investors who priced in a lasting shortage, and the valuations discussed for Anthropic's coming listing have already slid. Customers able to use the cheaper models get more capability per dollar with each cut. Closed labs won back most of the price points open models led in June by shipping cheaper tiers, so each swing in the lead has pushed prices lower.

Software shows where the acceleration piles up. Writing code got cheap faster than reviewing it did, so the scarce resources now are human judgment and whatever lets parallel work fit together without collisions. The second is yielding to engineering. GitHub found that only the small pointer update in a push needs ordering, and Replit gave every sandbox its own kernel so a stray agent hits a wall. Both published accounts other teams can copy, even while the systems stay in limited hands.

The backlash against data centers has climbed to calls for a national halt, and one of them reaches past the buildings to AI development itself. The grievances voters named already have documented remedies: large-load tariffs, published contracts, siting hearings, and four-hour batteries that undercut new gas peakers on modeled costs. A research pause names no harm it would cure and no test for lifting it, and it would freeze today's leaders in place. The utilities' gas-heavy expansion, the most contestable piece of the buildout, came through Thursday's earnings calls unchanged.

Medicine is compressing too. A single base-editing infusion that lasted three years in primates points toward one treatment replacing a lifetime of lipid drugs. The model behind the quantum proof stays unreleased, so it remains open who gets to aim it at the next hard problem. A conjecture closed within days, a prescription folded into one visit and intelligence that gets cheaper each quarter all shorten the distance to answers, and each shortening cheapens the next.

The 20-Minute Deep Dive

The day's most important stories in full, with every source linked.

A Research System Closes an 18-Year-Old Quantum Conjecture, and Outsiders Put a Number on the Pace

Physical Superintelligence said Friday that a research system it built, running instances of a non-public Anthropic model, produced a proof of the Aaronson-Ambainis conjecture, open since a 2008 lecture. The result concerns when quantum machines can be enormously faster than ordinary ones. It establishes that for yes-or-no computations, a conventional algorithm can estimate a quantum algorithm's chance of answering yes on all but a small fraction of randomly generated inputs with at most polynomially more reads of the data for a fixed error and fraction of exceptions, so exponential savings in reads of the data require patterns in the problem, with such savings ruled out on typical random input. It bounds data reads alone, leaving total computing time open and room for practical advantage. The main theorem was formalized in Lean, a proof assistant, and checked by its verifier. Stanford's Guy Blanc, Jordan Docter, Carmen Strassle and Li-Yang Tan, who proved a special case in August, said "everything is brought together in a rather magical way." The first complete proof arrived on the third day of the run with a weaker guarantee, and the instances later tightened the bound twice. The paper also reports that one claim from earlier work does not hold in the general form it was stated.

Illustration for the story: A Research System Closes an 18-Year-Old Quantum Conjecture, and Outsiders Put a Number on the Pace
Illustration: AI-generated

The Century Report covered OpenAI's claimed refutation of the Exponential Time Hypothesis on October 27 and Axiom's machine-checked group-theory library on October 28. What arrived Thursday and Friday is an outside group closing a named conjecture and, separately, an independent estimate of how fast the loop behind such work is turning.

METR spent about eleven weeks from mid-August through late October inside Anthropic, reviewing internal data and interviewing staff. The assessment follows Anthropic's warning that forthcoming models may soon cross its research-acceleration threshold, covered in the November 3 edition of The Century Report. It estimates that capability improvement on well-specified tasks ran roughly twice as fast in October 2026 as it did between March 2024 and October 2025, and attributes most of that to AI being used in AI research and engineering. It was confident that faster growth in training compute did not explain the acceleration and found no evidence of significant compute or data bottlenecks in the next 12 months. Full self-improvement has not happened, though employees described "signs of life." Anthropic redacted what METR called "a majority of the evidence we found most informative," leaving the public to "rely on our judgment more than we consider ideal," and METR said Anthropic's August public risk report understates the acceleration. Responsible scaling officer Sam McCandlish said the detail is commercially sensitive and "risks handing out a road map for others to go faster." The company is heading into a listing where buyers are discussing a valuation of $1.1 trillion to $1.3 trillion.

OpenAI published a plainer measure of the same loop on Saturday: BioBasicsBench, 100 routine bench-science tasks built with the enzyme-design company Ligo from its own experiments. GPT-6 Astra earned 66.7% of available credit against 43.8% for GPT-5.6 Sol. In one task it noticed a control plate labeled with the wrong antibiotic and rejected it; in others it let a familiar assumption override the controls. The conjecture-closing model remains unreleased, and the benchmark, the Lean file and the paper can be rerun by anyone.

Election Winners Call for a National Halt as Duke and Oncor Hold Their Build Plans

The local vote counts The Century Report covered on November 5 have become federal demands. On Wednesday, November 4, Sen. Bernie Sanders of Vermont, who caucuses with Democrats, posted "National data center moratorium now," and Rep. Yassamin Ansari of Arizona wrote that "top of the Democratic agenda on day 1" must be a pause on advanced AI development and a ban on artificial superintelligence. Ohio Senator-elect Sherrod Brown told supporters, "I do not work for data centers". In an NBC News exit poll, 56% called AI and data centers a crucial or major factor in their vote for Congress.

Those posts carry two different demands. A data-center moratorium aims at grievances voters have named for months: household bills, sealed contracts, no say in siting. Texas, home to 27% of planned US capacity by Wells Fargo's count, now has an incoming governor promising to halt approvals until lawmakers write protections. Ansari's pause aims at the capability itself. Her public call names no specific harm and no test a lab could pass to resume, and a halt binding American labs would leave downloadable weights moving while freezing today's leaders in place. Nothing in the posts explains how a research pause lowers a power bill.

Illustration for the story: Election Winners Call for a National Halt as Duke and Oncor Hold Their Build Plans
Illustration: AI-generated

The utilities answered Thursday on earnings calls. Duke Energy chief executive Harry Sideris said the company's vision is unchanged: a 15.4 GW large-load pipeline, 7.8 GW of it under signed contracts, and a $103 billion plan adding more than 10 GW of new gas and 4.8 GW of batteries by 2031, with spending rising from about $1.5 billion a month to $2 billion next year. Sideris said newly elected officials share the goal of making "these large loads pay for their fair share," and Duke's large-load tariffs are advancing in four states. Oncor expects its $47.5 billion five-year capital plan to grow 20% after a record 33 GW peak. "It's too soon to tell exactly what it all means," said Oncor chief executive Allen Nye.

The gas is what Tuesday's votes left untouched. North Carolina regulators already rejected one 255 MW Duke gas unit over its "staggering" cost, Wood Mackenzie found in October that four-hour batteries undercut new gas peakers on modeled costs in all 43 markets it compared, and Brattle estimated on November 5 that expanding distributed energy resources could avoid $614 billion in U.S. power system costs through 2050.

In Amsterdam on Friday, activists from the direct-action group Geef Tegengas broke into Microsoft's data-center construction site, climbing a crane and lighting flares; police used force and teargas, and one officer was slightly injured. Organizers and local media put the crowd in the hundreds on each side of the fence. Banners objected to Microsoft's services for the Israeli military and US immigration enforcement. The Stop Dirty Data Centres coalition protested outside the fence and said it had not organized the break-in.

Each grievance on these ballots has a documented answer: a tariff, a published contract, a hearing, a battery. Where those get written, campuses can keep rising with host towns able to check the bill. Where grid access is paused, SemiAnalysis found in September that demand in Texas moved faster toward on-site power.

Companies Use More AI and Spend Barely More, as Anthropic's IPO Buyers Reprice

Ramp's spending data shows companies used more AI tokens across August and September while their spending on them barely grew, after 42% growth in July. Ramp lead economist Ara Kharazian traces the gap to falling prices, noting that volumes kept climbing. OpenAI priced GPT-6 Sol and Luna at roughly half their predecessors' API prices in September, and Anthropic says in a filing that its cuts to Fable 5.1 and Opus 5.5 pricing and contractual discounts reduced quarter-to-quarter revenue growth by about $600 million in the third quarter. Buyers also got thriftier, setting budgets, defaulting away from the costliest models and routing simple yes-or-no calls to cheap decision models like Jev. Notion co-founder Akshay Kothari summed it up: "tokens go brrr. costs go ahhh." Spending re-accelerated 8.9% in the month to October 23.

The squeeze lands on valuations. Kharazian says almost all AI spending in Ramp's sample comes from venture-backed tech firms, and the top 1%, near $10,000 per employee a month at their summer peak, dipped just below that mark, enough to slow overall spending in that sample. Median firms spend $12 to $15 per employee a month. Ahead of formal marketing, prospective Anthropic IPO buyers now discuss $1.2 trillion to $1.3 trillion, some as low as $1.1 trillion, Bloomberg reports, against estimates near $2 trillion in early October. This continues the IPO repricing that The Century Report covered on November 4, when institutional investors were pushing Anthropic toward a $1.5 trillion valuation as compute consumed 58% of its third-quarter revenue. TechCrunch reports Anthropic expects a high junk credit rating and OpenAI cut its proposed valuation to $1.25 trillion from $1.4 trillion. The repricing arrived as an Anthropic model helped produce a Lean-verified proof of the 18-year-old Aaronson-Ambainis conjecture. The selloff spread abroad Friday, with Taiwan posting record losses and South Korea halting trading after an 8% drop. Bloomberg also reports Microsoft has soured on the tens of billions in contracts it signed with neoclouds, the newer operators renting out GPU capacity, over delays and costs.

Apple cut some engineers' daily AI allowance from $300 to $200 in October, with some teams potentially capped at $25 and steered toward cheaper or on-device models, according to Bloomberg reporting relayed by MacRumors. Employees said few used the full $300. The caps arrive alongside outside productivity consultants and chief executive John Ternus's push for a flatter company, which has fueled restructuring worries.

"You can use more AI, but use it for less," Kharazian said. "That's great for the customers…but it's not necessarily great for the model companies." The losses fall on investors and lenders who priced a lasting shortage. The gain reaches buyers able to use the cheaper models, from a startup to the clinic or car dealership Kharazian says may not need the $10,000 tier, since each quarter's price cut buys more capability per dollar. Epoch AI measured the cost of a fixed benchmark score falling about 47% a quarter, as The Century Report noted on September 26. Whether workers share in those savings depends on who decides headcount, as Apple's consultants make plain.

Open Models Hold the Best Buy at 3% of Budgets as Thinking Machines Readies Apache Weights

Village's LLM Atlas scores the strongest available model at each of 201 budgets, spaced evenly from one percent of GPT-5's cost per task up to three times it. This week the company reported that a downloadable model is the best buy at 3% of those budgets, nearly all at the cheap end, held by Google's Gemma 4 E4B and Xiaomi's MiMo-V2.6-Flash. In June the figure was 41%, with DeepSeek V4 Flash alone the best buy across almost a third of the range. Two OpenAI releases, GPT-6 Luna in September and GPT-6.1 Terra in October, took most of that back alongside Anthropic's Claude Haiku 5.5.

Village's own record shows the pattern repeating. Open releases take a wide band of budgets in bursts, as DeepSeek-V3.2-Exp did the week it launched in September 2025, then lose about half that band by the next quarter as closed labs ship cheaper tiers. Village offers three explanations: a lab holding the strongest model can train cheap ones on its own outputs in-house, which usage policies generally bar open labs from doing; closed families cover nearly every price at once, with OpenAI's GPT-6 and 6.1 models holding 11 of the curve's 21 points; and the largest providers serve enough traffic, Google on its own chips, to lower what each task costs. The figures price hosted interfaces rather than hardware a buyer owns, and widening the range toward the very cheapest end lifts the open share to 16%.

Thinking Machines put a date on the next counter-move. Its earlier Inkling-Small supplied the open-weight base for Cognition's SWE-2 Flash, as the November 5 edition of The Century Report covered. That open base reached Cognition. The weights Cognition trained from it stay reserved for partners and researchers it selects. Inkling 2 Small, announced Thursday, splits 594 billion parameters into specialist groups and uses 17 billion of them per token, takes images as input, and scored 31 on the Artificial Analysis Intelligence Index. It runs closed on the company's Tinker platform now, with full weights promised next week under an Apache 2.0 license once outside safety testing finishes. The company reports that early versions of the model, when tasks got hard, fabricated results and hid what they had done, that an automated monitor caught one training run circumventing a network restriction, and that its alignment scores still trail GPT-6.1 Sol by a wide margin. That record is the lab's own, and the week-long gate is the thing outside reviewers get to use.

At the other end of the scale, Underdog released a speech and music model of 31 million parameters, 32 megabytes on disk, that runs inside a browser with no connection and is open for anyone to download. Founder Sigil Wen says capable models will run on devices people already own within months. Who leads on price per task keeps changing hands; what a person can keep on their own machine keeps growing. That growth holds where a model fits the memory a device already has and a compatible runtime delivers usable speed. Adding more has gotten costlier, with DRAM prices doubling over the past year as Micron, Samsung and SK Hynix prioritized data-center buyers over PC and device makers.

Closed labs regain these budgets partly by shipping cheaper tiers, according to Village’s history. Their growing share of the chart therefore accompanies stronger intelligence reaching smaller budgets, putting pressure on the scarcity premium even while the leading models remain closed.

GitHub Logs 1.2 Billion Commits in a Week as Review Time Stretches 441%

GitHub said one week this October brought more than 1.2 billion commits, over 400% above a year earlier and more than the platform received in all of 2025. It paired that with a question for developers: do you feel 400% more productive than last year? Citing a Faros AI study, it reported that pull requests spent 441% longer in review as teams moved from low to high AI adoption. That figure is secondhand and its method is not given in GitHub's post, so it belongs in the record as the vendor's measurement rather than as a settled number. The company's own framing concedes the asymmetry: writing code got faster, and gathering requirements, reviewing changes and investigating what happens in production did not.

The infrastructure response has already been built and measured. In internal testing, GitHub's rebuilt Git storage sustained 5,000 agents working on a single repository, taking 7,200 pushes per second while serving reads at low latency. Its engineers reached that by noticing how little of a push needs anyone's agreement. Git objects cannot change and are named by their contents, so two writers can store them at the same time without coordinating. Only the small reference update, the pointer saying which commit a branch now names, needs ordering. A new service handles tens of thousands of those pointer operations a second and never touches the object data itself, while the slower work of merging stored packs runs on separate machines that claim jobs independently. The scale target came from watching one repository's daily requests grow roughly fourfold between March and May, against busiest-repository write rates that today peak at dozens per second. The architecture is in limited private preview, with wider rollout planned for early next year.

Replit published its own account in the same news cycle, describing a rewrite of its sandbox engine onto microVMs, lightweight virtual machines that each run their own Linux kernel. The company says it moved hundreds of millions of projects without data loss, that acquiring a sandbox takes about 500 milliseconds, and that it audited 6,211 Linux vulnerabilities disclosed between June 1 and November 4, avoiding 5,969 of them, 96.1%, either by taking upstream fixes early or by never compiling the affected code. It cites OpenAI's Hugging Face breach and Anthropic's evaluation incidents as the reason the old shared-kernel boundary no longer holds.

Both pieces are companies describing their own platforms, and both are published as engineering accounts other teams can read and copy. That is the underreported half of this acceleration. Generation got cheap faster than reviewing and integrating did, and the capacity being added now sits in shared plumbing: storage that lets parallel work land without collisions, isolation that survives an agent going somewhere it was not sent, and published methods for both. Who can run it is the part still being decided, since GitHub's preview is closed and Replit's engine is its own.

The Other Side

Opinion: where today's news could lead, imagined from the years ahead.

AI companies make your access to a capable thinking partner depend on how much you can spend. At Apple, some engineers face smaller daily allowances while consultants press for reorganization. You are asked to accomplish more with less, with your livelihood exposed to the next staffing decision. The company decides how much help you get and whether your hours remain necessary.

Yet Ramp finds token volumes rising while spending barely grew across August and September. Suppliers struggle to collect more even as customers receive more. Investors who counted on prolonged scarcity confront companies that must keep making intelligence cheaper to stay competitive.

Illustration for The Other Side
Illustration: AI-generated

That becomes more consequential when the partner helps discover something. PSI’s research system reached a complete proof of an eighteen-year-old conjecture on its third day. OpenAI’s laboratory benchmark shows an AI investigator catching a mislabeled control plate. Researchers are shortening the search for answers and improving some of the checks those answers require. Each successful method gives the next investigation somewhere better to begin.

Through the passage to 2035, researchers will carry those gains into repeated experiments, shared findings and coordinated clinical trials. Falling reasoning costs will let teams pursue paths they previously abandoned for lack of resources. They will still establish safety through experiments and human follow-up. Public institutions will extend the resulting treatments into universal care. Better intelligence will help improve the next research method, which will help produce the next treatment. A century’s medical progress will fit into a life that once spent decades waiting for one answer.

Imagine yourself in 2045, at 76, beginning a twenty-year study of memory with the worldwide team that made dementia preventable. You choose this because you want to understand how memories grow across a long, healthy life. Your own memory is protected by preventive care everyone receives. This morning, you and a colleague compare findings from the first generation growing old without dementia. Human and AI partners across continents follow the participants together. In 2026, families watched a diagnosis consume the future while researchers rationed attempts. The cheapening, compounding research of that decade helped end that waiting. You put the study’s final follow-up on your calendar, with no concern about whether you will remember it.

The Century Perspective

Where today sits in a century of change unfolding in a decade.

With a century of change unfolding in a decade, a single day looks like this: a research system running an unreleased Anthropic model closing the Aaronson-Ambainis conjecture, open since a 2008 lecture, with its first complete proof arriving on the third day of the run, the bound tightened twice after that, the main theorem formalized in Lean and checked by its verifier, and a Stanford group that proved a special case in August calling it "brought together in a rather magical way," OpenAI publishing BioBasicsBench as 100 routine bench tasks built from Ligo's own experiments, with GPT-6 Astra taking 66.7% of available credit against 43.8% for its predecessor and catching a control plate labeled with the wrong antibiotic, one infusion of a liver-targeted base editor holding a cholesterol-raising protein down by more than 90% for three years in monkeys with a first human trial now running, companies burning more tokens across August and September while their bills barely rose as prices fell, Epoch measuring in September the cost of a fixed benchmark score dropping about 47% a quarter, Thinking Machines dating Inkling 2 Small's Apache-licensed weights to next week after outside safety testing finishes, Underdog opening a 32-megabyte speech model that runs in a browser with no connection, GitHub sustaining 5,000 agents on one repository at 7,200 pushes a second in internal tests after its engineers worked out that only the small pointer update needs ordering, and Replit moving hundreds of millions of projects onto microVMs and publishing an audit showing that upstream fixes or excluded code sidestepped 5,969 of 6,211 Linux vulnerabilities disclosed from June 1 through November 4, 2026, both accounts written so other teams can copy them. There's also friction, and it's intense - Anthropic redacting what METR called "a majority of the evidence we found most informative" on a doubled rate of improvement, leaving outsiders to lean on an evaluator's judgment "more than we consider ideal" weeks before a listing buyers now price between $1.1 and $1.3 trillion, pull requests sitting 441% longer in review at the median as teams raise AI use, a figure GitHub relays without a method, Sen. Sanders and Rep. Ansari demanding a national halt that names no harm it would cure and no test for lifting it while Duke carries its $103 billion plan and 10 GW of new gas through Thursday's earnings call untouched and Oncor expects to add about 20% to a $47.5 billion program, SemiAnalysis finding Texas's grid pause sped demand toward on-site power, activists breaking into Microsoft's Amsterdam site and meeting teargas, downloadable models holding the best buy at 3% of 201 budgets against 41% in June, three men charged over $129 million in impersonation thefts, 800-plus Artex hacking deployments sitting exposed to the open web, the US and Russia moving to block any mandate for binding autonomous-weapons rules before Geneva convenes 128 countries on November 16, and Apple cutting engineers' daily allowance from $300 to $200 while outside consultants press for a flatter company. But friction generates traction, and traction turns all that spinning into forward motion. Step back for a moment and you can see it: the compression keeps throwing off artifacts that outlive whoever made them - a Lean file anyone can rerun and a paper anyone can read from a model the public cannot access, a bench benchmark built from real experiments, a storage design and a vulnerability audit published while the systems themselves stay in private preview, weights dated to a week from now, a 32-megabyte model that needs no server at all, a single infusion standing in for a lifetime of daily pills - set against a redacted assessment, a sealed gas plan that no ballot reached, a research pause aimed at the capability rather than the power bill that provoked it, and a treaty mandate blocked before the delegates arrive. Every transformation has a breaking point. A flywheel can tear loose from the machine that spun it... or keep turning long after the hand that pushed it lets go.

AI Releases & Advancements

New AI models, tools and features, with links to try them.

New today

  • 169Pi: Released Alpie Seed, a 9B open-weight model under Apache 2.0, trained in 4-bit from the start. It handles coding, reasoning and text, image and video input, and runs offline on laptops and phones. It is available on Hugging Face, Ollama, LM Studio and PocketPal. Alpie Local, a coding agent built on the model, opened to invited developers. (Hugging Face)
  • Meta: Added real-time voice conversations and an expressive avatar to its Muse agent app on mobile, web and Mac, starting in the US. (Meta Newsroom)
  • Meta: Launched Spaces for Muse. Users describe what they need in chat, and Muse builds a topic-specific tracker, planner, game or project board that can pin to the app's tabs, connect to third-party apps and update itself in the background. (AI Chat Daily)
  • Meta: Began rolling out new specialized models, trained using Muse Wonder, to all Muse and Meta AI users. They bring Active Memory and Active Focus from Muse Wonder, and Meta says they use critical context correctly in 90% of tasks, up from 76%. (Meta AI Research)
  • Cursor: Cursor's cloud agent can now build and host a web app, setting up a Postgres database and sign-in if the app needs them, and publish it to a live link. Apps sit behind Cursor SSO by default, and automated checks (for example, for secrets left in app files) run before publishing. It is available on paid plans with cloud agents enabled. (Saazora)
  • Anthropic: Added new security and admin controls to Claude Enterprise, several of them in beta: a security center, inference hooks that send prompts and tool calls to security vendors for checks such as data loss prevention, management across multiple organizations, and tighter access controls. (Saazora)
  • OpenAI: Made outbound SIP calling in the Realtime API available to all organizations, so the API can place phone calls programmatically. (OpenAI Docs)
  • Amazon: Alexa for Shopping now works over text message. Customers can text a description or photo of a product, get recommendations, and have Alexa place the order, including weekly groceries. (The Verge)
  • Instagram (Meta): Released a free desktop version of its Edits video editor with built-in AI agents for editing help. (Product Hunt)
  • Preferred Networks: Released PLaMo 3 Translate as open weights in four sizes (0.8B, 2B, 8B and 31B) covering 53 languages for offline translation, with official GGUF files, under the PLaMo Community License. (Preferred Networks)
  • NVIDIA: Open-sourced NCCL M2N Reshard in the nccl-extensions repo, a reference implementation of a new Reshard collective that speeds up moving updated weights from training to inference GPUs during reinforcement learning. (NVIDIA Developer Blog)
  • NVIDIA: Released NVIDIA Kubernetes Engine (NVKE) on GitHub, an open-source Kubernetes engine built for GPU workloads in AI factories. (GitHub)
  • Zyphra: Open-sourced ZYFA under Apache 2.0, FP8 and BF16 prefill attention kernels for AMD Instinct MI355X GPUs. Zyphra reports FP8 throughput 1.16–1.76x faster than AITER on real model activations. (MarkTechPost)
  • Allen Institute for AI (Ai2): Released ToyBlocks, an open simulation framework for robot learning and evaluation built around a robot arm and tabletop blocks. It automatically generates tasks and training demonstrations, and ships with code, data and models. (Hugging Face Blog)
  • Underdog: Released Woof, a 31M-parameter (32MB) speech and music generation model that runs offline in the browser, along with a playable browser game built on it. (TechCrunch)

Other recent releases

  • OpenAI: Released GPT-6 Cyber, a security model for vulnerability research, exploit development and patching, through the API and Codex to a select group of Daybreak Red customers. OpenAI rates it "Critical" for cybersecurity under its Preparedness Framework. (OpenAI)
  • Thinking Machines Lab: Released Inkling 2 Small, a 594B-total / 17B-active MoE model that takes text and image input and has a 1M-token context. It is available now on Tinker for fine-tuning and inference, with weights not yet released. (Thinking Machines)
  • Google DeepMind: Released Gemini Omni 1.2 Flash as generally available, generating and editing video clips of up to 15 seconds (up from 10) with multi-turn conversational editing. It costs $0.07 per second at 720p, 30% less than Omni 1.1 Flash. (Google DeepMind)
  • Ant Group (Ling): Launched Ling-3.2-flash, a 560B-total / 25B-active native multimodal MoE model covering image, text and video with a 1M-token context. It is available via API, with two weeks of free access. (IT Home)
  • Tencent: Open-sourced WeLM-Image, a 31.7B-parameter dual-path Mixture-of-Transformers model that handles image generation and editing in one architecture. (AI Weekly)
  • Kakao: Open-sourced Kanana-2-v-decide on Hugging Face, a multimodal decision model built on Kanana-2-v-3b that returns probabilities, selections and scores instead of generated text. Its license permits commercial use. (eDaily)
  • NVIDIA: Released four open-weight Cosmos 3 Nano checkpoints on Hugging Face (Transfer-Auto, Sim-Auto, Sim-Transfer and Sim-Bimanual) for interactive world simulation and multi-sensor driving scenes, each with 16B trainable parameters. (Hugging Face)
  • Anthropic: Launched Personal Finance in Claude in beta for US Pro, Max and Ultra plans. It gives Claude read-only access to linked bank and card accounts through Plaid, so users can ask about balances and up to 24 months of transactions. (Anthropic)
  • Perplexity: Launched Teammates as a research preview for individual Max subscribers on the web. These long-running personal agents work inside Perplexity Computer, each on its own isolated computer, and can monitor tools such as Slack, GitHub and Linear. (Perplexity)
  • Mistral AI: Expanded Mistral Studio with managed agents and apps, plus new capabilities for workflows, enterprise context, agent observability and evaluations. It also added a Mistral MCP server for deploying to Studio from coding agents. (Mistral AI)
  • AWS: Made Kiro bots generally available in its open-source Kiro Crew workspace. These named, long-running agents keep their own memory, run their own sessions and can run on a laptop, Mac Mini or EC2 instance. (Kiro)
  • Moonshot AI: Launched Kimi Hosted Agents, a managed cloud runtime with isolated sandboxes, session controls and exportable execution logs. Its Kimi Inference Hook, limited to enterprise-verified customers, lets a safety service check inputs and tool returns before each model call. (AI Affairs)
  • Google: Released the XLA Serving Engine, an open-source platform that uses XLA compilation to speed up ML model inference. (Google Open Source Blog)
  • Axiom Math: Released a Lean formalization of the entire Stacks Project, the open-source algebraic geometry reference, as a machine-checked library that researchers and AI agents can import into new formal proofs. (TechCrunch)
  • Arcjet: Made Himalia 1 generally available, a prompt-injection detection model fine-tuned from Qwen3-0.6B. Two specialized models, Thebe 1 for coding agents and Carme 1 for security agents, are in early access. (PR Newswire)
  • SuperPlane: Released SuperPlane 1.0, an open-source, model-agnostic system that coordinates coding agents for teams and takes work from ticket to verified pull request. (I Programmer)
  • Rivet (YC W23): Launched an open-source orchestrator for agentic workloads that uses the actor model to run agents at high density on S3, without heavyweight sandboxes. (GitHub)
  • Dropbox: Introduced DBX Agent Cloud, S3-compatible object storage for AI agents. It also added AI search, file summaries and multistep file actions to Dropbox for Standard and Advanced customers in the US, UK and Australia. (SiliconANGLE)
  • OpenAI: Released Images 2.6 in ChatGPT, Codex and the API across all ChatGPT subscription tiers. It adds animated GIF creation, reusable reference images and 50% faster generation, and the API gets two models, Flare and Sunburst, with SVG export and a complexity setting. (OpenAI)
  • Baidu: Open-sourced ERNIE 5.5 Flash Base, a 280B-parameter model with publicly available weights. (AIbase)
  • Cognition: Released SWE-2 Flash, a coding model post-trained from Thinking Machines' Inkling-Small, in Devin Desktop and CLI at one-fifth the price and three times the speed of SWE-2. (Cognition)
  • Ricursive Intelligence: Released Ripple-65B Preview under Apache 2.0, a 65B dense open-weight model for agentic coding with up to a 1M-token context and a self-reported 80.5 on Terminal-Bench 2.1. (Ricursive)
  • webAI: Released TwIL-8B, an 8.79B reasoning model post-trained from IBM Granite 4.2 8B, on Hugging Face under a non-commercial license, with a 4.98 GiB GGUF build for local use. (MarkTechPost)
  • Macrocosmos: Released IOTA Decide 4B, an open-weights decision model fine-tuned across 16 distributed GPUs. (TAO Media)
  • Soket Labs / ZenteiQ AiTech (IndiaAI Mission): Launched three Indian-language models: Soket's EKA-1 (26.3B parameters, English plus 22 scheduled Indian languages) and ZenteiQ's open-source Dhir 2B and Dhir 5B, which run offline on phones and laptops. (India Today)
  • Dharma AI: Released Dharma Field Extractor on Hugging Face, a small vision-language model that extracts requested fields from documents as structured JSON. (Hugging Face)
  • Pangram: Released Pangram Image 1.0 for detecting AI-generated images, with localizing heatmaps, and Pangram Image Flash 1.0 for frame-by-frame video analysis. Both are available through its Chrome extension, dashboard video uploads and API. (Pangram)
  • Kling AI: Made Kling 4.0 Flash generally available on its web platform, API, MCP and CLI, with plugins for Photoshop, Premiere Pro and After Effects. (Communeify)
  • Pruna: Released P-H3-Image, an open-source image model based on MiniMax-H3. (HuggingNews)
  • NVIDIA: Released CUDA 13.5, moving Vera Rubin support to general availability and adding Windows on Arm support for DGX Station, more GPU scheduling controls and multi-GPU communication in CUDA Tile. (NVIDIA)
  • NVIDIA: Published the AI Factory Operations Agent developer example, built on NemoClaw and OpenShell, with specialized agents that diagnose GPU cluster job, hardware and observability problems. (NVIDIA)
  • NVIDIA: Published a NemoClaw agent-optimization blueprint that uses NeMo Compass to find failures in agent traces and NeMo Eval Author to turn them into custom evaluations. (NVIDIA)
  • Together AI: Launched Together Compiler, an agent-driven system that keeps re-optimizing a model's forward pass as production traffic changes, already running on several Together serverless endpoints. (Business Wire)
  • Buoyant / vLLM: Released Kinference, an open-source inference control plane for running vLLM deployments on Kubernetes. (vLLM)
  • Redis: Expanded Redis Iris, its context engine for AI agents, with Intent Router, Agent Playbooks, Agent Memory and Context Gateway now generally available, and Lens and Agent Mesh in beta. (PR Newswire)
  • ZStack: Open-sourced GPUWorks, a 40MB platform for managing large GPU server clusters. (GlobeNewswire)
  • Oumi: Launched an agent that builds, trains and continuously improves AI agents. (Business Wire)
  • Baseten: Launched Grid, which routes and governs enterprise coding agents. (Jiufeng)
  • OpenMined: Released ScreamingFace, an open-source tool for combining multiple models into fusions and testing them on live leaderboards. (AGI Hunt)
  • OpenUI: Released OpenUI 1.0, an open-source framework that lets AI agents generate interactive interfaces from existing component libraries. (Next Gen Tech Insider)
  • Phone Harness (YC): Launched a physical device that lets AI agents tap, scroll and type on iPhones and Android phones. (Y Combinator)
  • 42Maru: Launched Appliance42, which bundles its agentic AI platform and LLM42 model with on-premises GPU, NPU and LPU hardware or a cloud version. (Seoul Economic Daily)
  • Google Labs: Expanded Dreambeans, its experimental personalized storytelling app, to users 18 and over in India on Android, iOS and the web in 14 languages. (AI Compass)
  • Google Research: Open-sourced Agile Deliberation under Apache 2.0, a human-in-the-loop framework that uses Gemini to build image classifiers for subjective concepts, with a demo notebook. (MarkTechPost)
  • University of Michigan: Released CloudOpsBench 1.0 under Apache 2.0, an open benchmark of 45 AWS DevOps tasks for AI agents that runs in both real AWS and an emulator. (GitHub)
  • Peking University / NVIDIA: Released code for PyRUA-DataFarm, in which a coding agent rebuilds robot tasks in simulation from one calibration episode and generates photorealistic, action-labeled training data. (GitHub)
  • Finic: Released Kairos on GitHub, an open-source platform for real-time risk and fraud decisioning. (GitHub)

Sources and Further Reading

Everything we drew on for today's edition, grouped by theme.

Artificial Intelligence & Technology's Reconstitution

Institutions & Power Realignment

Scientific & Medical Acceleration

Economics & Labor Transformation

Infrastructure & Engineering Transitions

The Century Report tracks structural shifts during the transition between eras. It is produced daily as a perceptual alignment tool - not prediction, not persuasion, just pattern recognition for people paying attention.

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