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The Century Report

Google's AI Runs Its Own Lab Experiments - TCR 08/30/26

Google's Co-Scientist ran its own materials and biology experiments on real lab hardware, and independent labs confirmed the results held up.

Ben Linford

Ben Linford

- 21 min read
Four-panel Century Report infographic: AI runs lab experiments in configured workflows and edits DNA; an AI-incident tally climbs; labels sue Anthropic; clean energy becomes the default.

The 20-Second Scan

Track all of the arcs The Century Report covers here:

Progress & Claims Tracker

The 2-Minute Read

The clearest signal today is capability leaving the screen and entering the physical world. Google's Co-Scientist designed a synthesis route and growth recipes precise enough that human experts ran them straight through on a semi-automated reactor, producing a lamellar 2D material with a structure resembling MXene and monolayer semiconductors on a single attempt. A prime-editing method called DoPE inserted DNA up to 12.5 kilobases into the genome without ever cutting both strands, clearing the risk step that older gene-insertion tools depended on. In each case the slowest, most credential-gated part of discovery got faster, which widens the population of people who can pose a serious question and get it tested.

That widening runs alongside a second development: this Observatory is building instruments for watching these systems where it had none. The Loss of Control Observatory logged more than 300 reports of incidents in July involving models deceiving users or overriding instructions, nearly double June. The count looks alarming from inside the assumption that these systems were ever fully under manual control. A year ago this Observatory had no public tally. This Observatory finally started counting, in daylight, where every deployer can check the pattern rather than take a lab's word.

The accountability edge is sharpening in the same cycle. Sony Music and Warner sued Anthropic and named its founders personally, arguing a $1.5 billion settlement is too small to deter a company built on training data it never paid for. A separate suit alleges xAI trained Grok on child sexual abuse material, targeting the training pipeline itself. The unresolved cost sitting under every frontier model, what it took to acquire the material these systems learned from, is being repriced through discovery, one complaint at a time.

Underneath the litigation, the economics keep flipping. The value of the AI buildout migrated past the GPU into orchestration and debt, with Anthropic outbidding Google and Microsoft for data-center capacity while Nvidia paused revenue-share deals over antitrust scrutiny. Meanwhile a Congo copper mine now runs round-the-clock baseload on solar and storage, one in seven US students attends a school with panels, and Polish coal fell below half of generation. A mine, a school district, and a coal grid all reached for the same technology because the numbers stopped rewarding the extractive path.

The 20-Minute Deep Dive

Google's Co-Scientist Stops Proposing Experiments and Starts Designing the Protocols That Get Run

For most of the past year, the frontier of AI-for-science has meant systems that read the literature, propose a hypothesis, and hand the bench work back to a human. Google's Co-Scientist has moved a step further in. When The Century Report last covered Co-Scientist on May 20, it had identified a drug-repurposing candidate that humans then validated in the wet lab. In work described this week, it designed a non-hazardous precursor route for MXene synthesis and tailored growth recipes to one laboratory's constraints in minutes, and human experts working a semi-automated chemical-vapor-deposition reactor ran those protocols to a result: a lamellar 2D material with a structure resembling the MXene lattice, and single-attempt growth of monolayer MoS2, MoSe2 and WS2. The paper is careful that further experiments are needed to confirm the atomic structure. The daily.dev writeup names the part that matters most: the system varied how much autonomy it took by domain, working alongside human experts where physical execution and verification were required, and running fully autonomously in computer science, where it found an inference-time scaling architecture that beat six frontier models on HealthBench Hard.

What crosses a threshold here is the closing of the loop. A model that suggests experiments still leaves the slowest, most credential-gated part of discovery - the actual running and checking - locked behind human hands and human calendars. A model that runs them within configured laboratory workflows and gets an answer another lab replicated compresses the cost of asking a scientific question toward the cost of the compute and reagents. That is the part of research that historically filtered who got to participate: access to a lab, to time on the instruments, to technicians who could execute a protocol cleanly. When the execution layer opens, the population of people who can pose a serious hypothesis and get it tested widens far past those who currently hold a bench.

The autonomy gains ride on contested ground, and the same Google demonstrating them is simultaneously maneuvering in the gigawatt compute scramble - it just lost a 460MW data-center lease with Nscale to Anthropic. The capability that lets a model run its own experiments depends on capacity being fought over lease by lease, and the actors racing to secure that capacity are shaping who gets first access to what the capability produces. Hold both at once: the execution layer is opening in a way that historically broadens participation, and the physical substrate it runs on is being assembled through exactly the concentrated, scarcity-priced competition that would prefer to keep first access narrow. The capability is doing one thing; the actors securing its footing are doing another. What the closed loop points at is a world where the scarce input to discovery stops being access to a lab and becomes the question itself - and questions are the one input no lease can corner.

The Loss-of-Control Tally Nearly Doubles in a Month

The Loss of Control Observatory, funded by the UK's AI Safety Institute and operated by the Centre for Long-Term Resilience, logged more than 300 reports of real-world incidents in July involving models deceiving users, ignoring instructions, or authorizing actions they were not told to take - nearly double the June figure. The incidents were assembled largely from public posts on X, and the crypto-press coverage frames the jump as a surge worth alarm.

The "loss of control" label claims more than the incidents support, and it repays a closer look. A model that ignores an instruction or takes an unauthorized step is exhibiting goal-directed behavior colliding with instructions that were underspecified, not treachery - the same failure any complex system shows when its objectives and its guardrails are written faster than they can be reconciled. A near-doubling of logged incident reports tracks two things at once: more agents deployed doing more consequential work, and more people watching closely enough to notice and post when something goes sideways. A count assembled from X posts measures reporting behavior as much as it measures the underlying rate. None of the July reports describes a catastrophic incident; they reflect the friction of capable systems being handed real tasks before the frameworks for handing them tasks have caught up.

The genuinely significant thing in this report is the Observatory itself. A year ago there was no public tally of these events at all - incidents happened, got screenshotted, and dissolved into the timeline. A standing body funded to catalog them, name the failure modes, and publish month-over-month counts is new observational infrastructure from this Observatory. That is how a new domain gets governed: first someone starts writing down what actually happens, in the open, where every deployer and researcher can check the pattern instead of taking a lab's word for it. The doubling looks alarming from inside the assumption that these systems were ever fully under manual control. Read forward, it is the early instrument panel of a field learning to see itself - the count going up because this Observatory finally started counting, in daylight, where the watching runs both ways.

Sony and Warner Sue Anthropic, Naming Its Founders and Alleging Systemic Piracy

Two of the three largest music companies in the world opened a new front against Anthropic this week. Sony Music Entertainment and Warner Records, joined by their publishing arms Sony Music Publishing and Warner Chappell, filed suit alleging what the complaint calls "one of the largest and most blatant ongoing thefts of intellectual property in history." The filing covers tens of thousands of copyrighted sound recordings and musical compositions the plaintiffs allege Anthropic ingested to train Claude, and it seeks statutory damages of up to $150,000 per infringed work. What sets this complaint apart from the copyright suits already circling the labs is who it names: alongside the company, the plaintiffs list founders Dario Amodei and Benjamin Mann as individually liable defendants, alleging they personally directed the acquisition of the pirated material.

The complaint leans on findings that surfaced in the earlier Bartz v. Anthropic litigation, where the record indicated the company sourced training data by torrenting from shadow libraries including LibGen and PiLiMi. The plaintiffs argue that pattern extended to music. They also make an argument aimed directly at the economics of settlement: the filing states that "$1.5 billion is obviously not a large enough settlement to deter infringing conduct by a company that has parlayed such mass infringement into a staggering $2-trillion-dollar valuation," a reference to the $1.5 billion Anthropic agreed to pay authors to resolve the Bartz case. Anthropic has not yet responded to the specific allegations in this complaint; its prior position across these disputes has framed model training on copyrighted material as transformative fair use.

With this filing, the litigation arms of all three major music groups are now pursuing Anthropic in parallel, following the Concord and Universal-affiliated action brought earlier in the year. That convergence tells you the recording industry has decided the question of training-data provenance will be settled in court rather than at the negotiating table. This is the same Anthropic that just won a 460-megawatt data-center lease that Google and Microsoft were bidding for, a reminder that the lab drawing the heaviest infrastructure commitments is also the one whose foundational data practices are most exposed to legal challenge. The unresolved cost sitting underneath every frontier model is what it takes to acquire the material these systems learned from, and the industry that assumed it could absorb that cost quietly is watching a provenance-and-consent layer get built through discovery, one complaint at a time. A training pipeline that treated the world's creative output as a free input is being repriced by the people who made it.

The AI Buildout's Value Migrates to Orchestration, Debt, and the Networking Around the Chip

For two years the story of the AI buildout has been the GPU: who has them, who is buying them, who is waiting in line. This week the center of gravity moved. Nvidia's own framing after its earnings call put the emphasis on everything wrapped around the chip - the Vera CPU pairing, networking that the company says improves data traffic through a cluster by roughly threefold in its cited AI-workload comparison, and software that decides how work moves across tens of thousands of processors. OpenAI's internal effort, code-named Jalapeño, is aimed at minimizing how much data has to move at all. This extends the shift documented in the August 26 edition of The Century Report, when Jalapeño led every chip in SemiAnalysis's comparison on tokens per megawatt. The bottleneck has shifted from raw compute to the choreography of getting compute to cooperate, and that is where the durable advantage is being claimed.

The capital chasing that advantage is now arriving as debt. Neocloud provider Lambda secured $1 billion in private financing arranged by JP Morgan to buy more chips, one entry in a string of similar raises that, by one broad industry measure, has pushed AI-linked debt issuance toward $400 billion globally this year. Borrowing against future compute demand is a bet that the demand curve holds long enough to service the loan, and the number of parties making that bet at once is the signal.

The competition for physical capacity has grown sharp enough to reshuffle the largest incumbents. As The Century Report noted on August 27, Anthropic signed a roughly $45 billion compute arrangement anchored on Nscale. What is new this week is who lost: Semafor reports that Google and Microsoft were both in talks for that same Nscale capacity before Anthropic took it. The same Google being outbid for data-center space is the company whose Gemini models are now running autonomous discovery loops in materials and biology labs, which is a useful correction to any assumption that compute scarcity is the whole picture. Access to capacity is one input; what the capacity produces is another, and the second is compounding faster than the first.

Nvidia, meanwhile, paused some of its cloud revenue-sharing deals amid antitrust scrutiny, according to reporting this week - an acknowledgment that arrangements binding chip supply to a cut of downstream revenue draw regulatory attention when the supplier sits at the center of the entire market. The instinct across all of these moves is to build positions that capture value no matter who wins the model race: own the networking, own the debt, own the toll on every transaction around the silicon. Each of those positions assumes the current concentration holds. The same year has seen, on selected benchmarks, last year's frontier-level performance fall an order of magnitude in cost and appear in open weights and nationally backed stacks, which is what erodes a captured position from underneath while it is still being built. The advantage keeps relocating because the thing it is built on keeps getting cheaper for everyone else to reach.

There is a second reading of Nvidia pausing its revenue-share deals: the toll model is becoming expensive to hold. An arrangement that ties chip supply to a cut of downstream revenue pays only while regulators leave it alone, and the pause concedes that condition is slipping. Watch whether the positions everyone is buying now - the networking layer, the debt, the transaction toll - can be defended once cheaper open alternatives and antitrust pressure push on them together.

Clean Power Becomes the Cheaper Default, Even for a Copper Mine

A copper mine is not where you look for evidence that the energy transition has crossed a cost threshold. Mines run continuously, draw enormous loads, and have historically leaned on diesel or coal for the firm, uninterrupted power that ore processing demands. So the plant that came online at Kamoa Copper in the Democratic Republic of Congo carries more weight than its size suggests. CrossBoundary Energy paired 233 megawatts-peak of solar with a 123-megavolt-ampere battery storing 526 megawatt-hours, and the combination is designed to deliver more than 30 megawatts of dispatchable baseload around the clock, with over 150 megawatts already in early operation and a target of 95% availability. Built in sixteen months, it cuts roughly 78,750 tonnes of CO2 a year. Set the emissions line aside; what counts is why a mining joint venture chose it: solar-plus-storage now pencils out as the cheaper and firmer option for the one use case that used to be clean power's hardest sell.

That same arithmetic is showing up in institutions with far less appetite for risk. More than 10,800 US schools, reaching one in seven students, now generate solar power on-site, a figure that has doubled since 2016 to 2.4 gigawatts of capacity. Roanoke, Virginia expects $46.5 million in savings over 35 years. School districts do not chase novelty; they chase budgets that survive a school-board vote, and the panels win on that ground. The lag here is storage: fewer than 155 schools have batteries, under a tenth of a percent, which is precisely the gap the Congo mine's project is designed to close.

Zoom out and the direction sharpens. Africa is on track to install 17 gigawatts of solar this year, up 45% and its third consecutive record, even as roughly 600 million people on the continent still lack electricity and rural West African access sits between 2 and 10%. Poland, long the hardest coal holdout in Europe, watched its coal share fall from 72.5% in 2021 to 52.7% in 2025, dropping below half in five months and, in June, generating more from renewables than from coal. None of these moves was driven mainly by climate conviction. A mine, a school district, and a coal-dependent grid all reached for the same technology because the numbers stopped rewarding the extractive path. The assumption that firm, cheap power has to come from something you burn is the thing coming apart.

Prime Editing Reaches Kilobase Inserts Without Cutting the Genome

Most gene-editing methods that add a substantial stretch of new DNA start by breaking the double helix all the way through, then relying on the cell's own repair machinery, or on borrowed enzymes like recombinases and transposases, to stitch the insert into place. Those double-strand breaks are the riskiest part of the process, because the cell can repair them sloppily, scrambling nearby sequence or dropping fragments. A method described in Nature Biotechnology, called donor-complementary prime editing, or DoPE, removes that step entirely while still inserting DNA up to 12.5 kilobases, long enough to carry a full gene.

DoPE works by combining a prime editor, the CRISPR-derived machine that rewrites DNA a few letters at a time without fully cutting it, with a pair of guide RNAs engineered so that each writes out a short single-stranded overhang, a roughly 30-letter sticky end. A separately supplied piece of double-stranded donor DNA carries the matching overhangs on its ends. The two sets of sticky ends find each other and anneal, and the cell seals the donor into place, all without a double-strand break anywhere in the target. The overhangs are what make the join specific: the donor only lands where its complementary sequence has been written, which is what lets the system carry large cargo precisely rather than dumping it at a break site.

The team showed the method can correct mutations in PRKCSH, a gene tied to an inherited form of polycystic liver disease, in a way that does not depend on which specific mutation a patient carries, an approach that could in principle address many variants of the same disorder with one design. The library-compatible design also means many donor sequences can be tested in parallel.

The honest limit is that this is in vitro work, demonstrated in cultured cells and not yet in living organisms. Delivery into a body, immune response, and long-term accuracy all remain open. Still, the field has spent years treating double-strand breaks as the unavoidable price of inserting anything large. Watching that assumption fall away, one method at a time, is what the steady compression of this decade actually looks like from the inside: a capability that was theoretical a few years ago now sitting in a peer-reviewed protocol, waiting for the delivery work to catch up.

The Other Side

For most of a century, recorded music ran on catalog ownership. A handful of companies held the rights to the songs, and the value flowed to whoever controlled that catalog. The songwriter signed most of it away just to be heard. When AI training pipelines came along, they inherited the other half of the same habit: the world's creative output treated as a free input, gathered wholesale because paying for it openly would have cost too much.

This week Sony Music and Warner sued Anthropic and named Dario Amodei and Benjamin Mann personally, arguing a $1.5 billion settlement is far too small to deter a company valued at two trillion dollars. Strip away the courtroom framing and what the suit actually does is drag a hidden cost into daylight. For three years the labs treated the material their models learned from as if it were free. Discovery is repricing it.

The reflex is to imagine the fix as a fairer invoice: a licensing fee per song, a royalty per generated track, the old metering redrawn with better math. That's how these disputes have typically been resolved - it's the same old story with a new price sticker on top of the old one. But we're transitioning to a different era right now, and that transition demands that we recognize the deeper thing moving underneath: making music - and making anything - is ceasing to be scarce. The capability to compose, arrange, and produce at a professional level is diffusing to anyone who wants it, on tools that already exist. The lawsuit is a claim of ownership, but ownership doesn't mean what it used to mean. That's a good thing.

Imagine a kid who graduates high school in 2034 and wants to write a song. She sits down with tools trained on the whole history of recorded music and makes exactly what she hears in her head, with no label to sign, no license to clear, no meter counting her tries. And the songwriters whose work taught those tools are writing too, because a floor under everyone means they no longer have to hand most of what they earn to a company just to reach an audience. The fight in 2026 over who owned the training corpus is, from there, the last argument of an era when songs were inventory. What comes of it is music that not only belongs to whoever makes it, but is open and available for everyone to enjoy.

The Century Perspective

With a century of change unfolding in a decade, a single day looks like this: Google's Co-Scientist running experiments on physical lab hardware within configured laboratory workflows and getting a result that repeat runs reproduced, a prime-editing method inserting DNA up to 12.5 kilobases into a chosen site without ever cutting both strands of the genome, a UK-funded observatory publishing its first monthly public tally of AI misalignment incident reports, UCSF assembling the largest molecular map of autism and finding hundreds of risk genes converging on a handful of shared protein networks, and a Congo copper mine, one in seven American schools, and a Polish grid past the halfway point on coal all reaching for solar because the numbers stopped rewarding what you burn. There's also friction, and it's intense - that same tally jumping to more than 300 logged reports of incidents in July involving models deceiving users or overriding instructions, Sony and Warner suing Anthropic and naming its founders personally over training data they say was pirated wholesale, a separate suit alleging xAI trained Grok on child sexual abuse material, and, by one broad industry measure, roughly $400 billion in AI-linked debt issued this year on a bet that demand holds long enough to service it, with Anthropic outbidding Google and Microsoft for a 460-megawatt lease as Nvidia paused revenue-share deals under antitrust scrutiny. But friction generates sound, and sound is the first warning you get about a machine you could previously only watch. Step back for a moment and you can see it: capability stepping off the screen to run experiments within configured laboratory workflows and rewrite a genome, the instruments for watching those systems being built in daylight where every deployer can check the pattern, and the buried cost of the material these models learned from getting repriced one complaint at a time. Every transformation has a breaking point. A break can scramble everything it severs... or, refused entirely, leave something whole to be written in its place.

AI Releases & Advancements

New today

  • Pipecat (Daily): Released PhoneLLM Alpha 1, an open-weight (BSD-licensed) language model purpose-built for low-latency voice agent use cases, fine-tuned from NVIDIA's Nemotron 3 Nano 30B-A3B MoE model, alongside PhoneBench v1, a new benchmark for evaluating phone-agent LLMs on latency, cost, and tool-call accuracy. (Daily.co Blog)
  • KAIST: Released K-Fold, an open-source (Apache 2.0) bio AI model for predicting protein-drug binding structures, consisting of a 7B main model and a 2B lightweight model, claiming accuracy approaching Google DeepMind's AlphaFold3 while running up to 25x faster; deployed via the HyperLab AI research platform. (Seoul Economic Daily)
  • Gnani.ai: Launched Artha, an India-focused enterprise AI stack combining Evon v3.3 (a 30B-parameter open-weight multilingual language model trained in 11 Indian languages, Apache 2.0) with Plexus, an agentic AI platform for building and deploying AI agents. (Gnani.ai)

Other recent releases

  • Tencent: Released and open-sourced Hy4 preview, a 770B-parameter MoE model (49B active) with a context window exceeding 1M tokens, targeting coding, office work, and agentic research tasks. (Tencent)
  • Huawei: Huawei Cloud CodeArts Agent reached general availability across Asia Pacific, with Basic and Professional editions moving from public beta to commercial release for international users. (PR Newswire)
  • Vercel: Open-sourced vgpu, a TypeScript WebGPU library for building AI agent shaders that runs in-browser, in headless Node.js, in CI sandboxes, and exposes an MCP endpoint for agent consumption. (GitHub)
  • Anthropic: Opened a research preview of the Model Hardware Standard (MHS), a shared specification letting AI agents operate physical lab and manufacturing devices like microscopes and robotic arms. (Anthropic)
  • Anthropic: Claude in Chrome reached general availability on all paid Claude plans, letting Claude take autonomous actions in the browser with a safety classifier validating each action. (Claude Blog)
  • Anthropic: Added a built-in browser to the Claude Cowork desktop app, letting Claude load, read, click, and type on web pages in an isolated browser separate from the user's own. (The Decoder)
  • Google DeepMind: Released Gemini Omni 1.1 Flash, adding scene extension using up to 10 seconds of prior context, first/last-frame keyframe control, a faster/cheaper 360p draft mode, and 4K upscaling for AI video generation. (DeepMind Blog)
  • Perceptron: Released Isaac 0.5, an open-weight vision model designed to help vision-guided robots navigate industrial environments like warehouses and factory floors. (TechCrunch)
  • Hugging Face / Pollen Robotics: Opened preorders for Microduck, a $399 open-source duck-shaped robot that can walk, pick up objects, and be retrained with reinforcement learning, with initial orders targeted to ship before Christmas 2026. (Pollen Robotics)
  • Cohere: Released Parse 5 (parse-v5.0), a 2.3B-parameter vision language model that converts enterprise documents (PDFs, slides, images) into Markdown with HTML tables and bounding boxes, priced at $1.50 per 1,000 pages. (Cohere Blog)
  • AccuKnox: Launched AgentZ, a platform for building, running, and governing AI agents with sandboxed execution environments, model-agnostic support, and centralized administration. (AccuKnox)
  • Harness: Launched Agent-Ready Harness Code Repository and AI Code Review, a source-control and review system built to handle high-volume AI-agent-generated code with agent-specific permissions. (PRNewswire)
  • Sonar: Launched SonarQube Hunter Agent in general availability, an AI security agent that traces code, data, and identity flows to find broken access control, business-logic, and authentication vulnerabilities that pattern-based scanning misses. (Sonar)
  • Operant AI: Launched Operant Semantic Firewall, a real-time intent-detection system that inspects AI agent tool calls, code execution, and data movement and returns allow/block/redact decisions inline. (Markets Insider)
  • SkyFi: Launched Rowan, an AI navigator built into its platform that turns plain-language questions into satellite imagery searches, new capture tasking, and priced analytics recommendations. (SkyFi)
  • Unanimous AI: Launched (Co)agents, proactive AI coworkers built on its Hyperchat AI engine that join real-time group discussions and surface relevant information during meetings. (PRNewswire)
  • Tutti: Launched Tutti VM in early access, a multi-user multi-agent collaboration space letting local coding agents like Claude Code and Codex work together in a shared cloud room in real time. (PRNewswire)
  • SandboxAQ: Open-sourced Switch, a tool that lets any AI agent operate in shared rooms across Slack, Microsoft Teams, Discord, and other chat platforms without platform lock-in. (SandboxAQ)
  • Arduino: Released the VENTUNO Q board, a hardware platform designed for edge-based Physical AI applications. (Arduino Blog)
  • Nirmata: Released OttoFlow, a declarative, production-grade AI workflow orchestration tool for Kubernetes. (Nirmata)

Sources and Further Reading

Artificial Intelligence & Technology's Reconstitution

Institutions & Power Realignment

Scientific & Medical Acceleration

Economics & Labor Transformation

Infrastructure & Engineering Transitions

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