From better modelsto tighter scientific loops

What happened: Tushen Zhihe, founded in Wuxi in late 2023 by Yuguang Wang, an associate professor at Shanghai Jiao Tong University's Institute of Natural Sciences, closed two rounds totalling several hundred million yuan. Shanghai Kechuang Group led the Pre-A, joined by Zhongsheng Yinling, Zhoushan Tuyi and others; Jinding Capital, MegaRobo, Zero2IPO Holdings and others invested in the Pre-A+. The money is to take several of its own programmes toward IND filings, upgrade its ToursynClaw design platform and hire. Its previous round, in September 2025, was an angel+ of around ten million yuan.

Why it matters: The money goes first to the company's own drug candidates, and its own candidates are where an AI protein-design company has to show that its designs work as medicines. MegaRobo, a maker of lab automation, invested to link its automated labs to the company's design-and-test loop, and the Pre-A came from Shanghai's state-backed venture group, which calls AI for science in biomedicine one of its main investment directions.

Evidence and limits: The results are the company's own, in the round's announcement: more than 100 antibody-engineering projects checked in real experiments, one design-and-test round in 14 days, more than 5,000 experimental data points. No paper or partner-reported result was found, its programmes and targets are not named, and neither the split between the two rounds nor a valuation is disclosed.

What happened: Google announced TPU 8i Max, which puts two TPU 8i chips on one package to double compute, on-chip memory and memory bandwidth in the same rack space, and TPU 8tx, which pairs TPU 8t silicon with AMD EPYC hosts so data and model weights stay protected and isolated even while in use. With the training TPU 8t and inference TPU 8i from April, that makes four TPU systems in a year. Amin Vahdat, Google's chief technologist for AI and infrastructure, says TPUs now have nearly 100 customers, universities among them.

Why it matters: Neither system is a new chip: 8i Max packages two existing 8i chips together, and 8tx reuses 8t silicon. Google says that is the point, since a new system takes months and a new chip years, so new configurations can follow what customers ask for. Two inputs change. What can be bought: four systems in a year, one for fine-tuning on private data that stays protected while the chip works on it. Chip design: Google says AI now works on its own hardware, AlphaChip in TPU design and Gemini producing layouts in hours rather than weeks.

Evidence and limits: Every figure is Google's own, measured against its other TPUs, with no independent benchmark. The post gives no absolute specs, prices or availability dates for the two new systems, names no science customer, and shows no example of an AI-made layout.

What happened: Claude Fable 5.2 was asked, with the board design and manufacturing company Diode Computers, to design a USB speakerphone, a voice-assistant hub and a handheld virtual pet. From short prompts it wrote the specifications and schematics, placed the parts on each board and routed the copper, modelled a 3D-printable case, wrote the firmware and listed the parts. Diode built the boards straight from the files and the three prototypes ran with no fix to their circuitry. On Diode's in-house PCB Gym it scored 69%, against 25% for Fable 5.

Why it matters: Models that touch hardware usually stop at chip design or a benchmark score. Here the output is a file a factory built from, and the test is whether the boards worked. For a lab that needs a custom sensor board or instrument controller, layout, routing and the manufacturing checks are the weeks of an electrical engineer's time that stand between a design and a working device; Diode, which does this work for a living, says it did not have to go through the usual rounds of adjustments with the files Claude produced.

Evidence and limits: This is Anthropic's own post with Diode, a commercial partner, which ran PCB Gym, its own unpublished benchmark; the score quoted here is against an earlier Claude. All three are prototypes: no electrical validation, antenna tuning, thermal testing or certification, and none designed for volume production. Claude was connected to no test equipment or factory machine, so Diode's engineers built and tested everything, and the prompting was minimal rather than absent.

What happened: Laboratoires Pierre Fabre, a drugmaker based in France, will have XtalPi design and deliver new small molecules against undisclosed targets with its AI and robotics platform, in several research programmes in oncology and possibly dermatology and rare diseases. Pierre Fabre picks which compounds advance and runs clinical and commercial development. XtalPi gets an upfront payment, milestones and a downstream share; no figures were given. Francesco Hofmann leads Pierre Fabre's pharma R&D; Raymond Zheng leads XtalPi's integrated drug discovery.

Why it matters: Pierre Fabre bought outside AI design earlier this year too: in January it signed Iktos for one oncology target. A drugmaker that keeps the disease biology and the clinic in house, and buys the molecules from outside on several programmes at once, is paying AI and robotic chemistry for the step between a target and a candidate. The release says the programmes go after targets that have been hard to drug.

Evidence and limits: The targets, the number of programmes and every payment are undisclosed, and no compound or result is reported yet. It shows what one buyer pays for, not that the design works.

What happened: On 8 November Kohli posted that across Google DeepMind's projects "we are witnessing a genuine phase transition in the pace of scientific discovery", and that its releases over the next few weeks would give a glimpse of it. He leads the lab's AI for Science work and is chief scientist at Google Cloud. A second post the same day said that as the acceleration unfolds, the focus will be on helping the wider scientific community use these tools responsibly.

Why it matters: This is not a general claim that AI will speed up science. It is a judgment on his own lab's work by the person who runs its science, with specific releases staked on a few weeks. What to watch is whether it reaches the research workflow rather than the model: how much faster a hypothesis is proposed, an experiment designed, a result checked. Only a discovery cycle that is measurably shorter would carry the word. His own hedge is in the sentence: the releases give a glimpse.

Evidence and limits: Kohli names no release, no result and no number, and does not say what would count as a phase transition or against what earlier pace it is measured. The releases he points to had not appeared when this was recorded, so there is nothing here to check yet.

What happened: Qingxi Zhiyan, founded in Beijing in May 2026 by Siming Dong, a Stanford materials science PhD with more than 15 years in semiconductor R&D, with Zhenlin Zhao as COO and Huan Wang as chief scientist, announced an angel round of nearly 100 million yuan from Zhongxin Juyuan, GL Ventures, AgiBot, Fengwu Capital, Orient Jiafu and industry funds, with no lead named. Dingfeng Kechuang invested in July. It builds models that read materials measurements such as X-ray diffraction, puts them into instruments, and takes contract materials R&D.

Why it matters: Two backers work in industries the company aims to sell materials to. AgiBot makes humanoid robots, and the company says it is developing permanent-magnet materials for robot joint motors with robot makers; Zhongxin Juyuan invests in semiconductors, where the company lists photoresist and memory materials among its programmes. Its route to training data runs through the instruments: AI on characterisation and process equipment, so that each measurement it runs is stored in a form a model can learn from.

Evidence and limits: Every figure is the company's own, given to Z Potentials and Wallstreetcn: contracts worth tens of millions of yuan in six months, tens of millions of experimental records, and a rare-earth magnet about 60% cheaper and 30% more heat-resistant than the previous generation, developed in five months, with mass production targeted for 2027. No paper or outside test was found, and Wallstreetcn notes the magnet figures have no independent source. Valuation not disclosed.

What happened: Mathew Chow and Tao E. Li had one agent (OpenAI's GPT-5.6-sol) propose code changes on training cases while a second accepted or rejected each on separate test cases, by speed and accuracy. On ten other molecules the excited-state (TDDFT) step in PySCF took 84-94% less time, with nearly identical energies; a separate job cut the ground-state calculation 5-28%. LAMMPS water simulations took 19-22% less time with identical trajectories, and a serial molecular dynamics code became CPU and GPU versions two to three orders of magnitude faster.

Why it matters: Computational chemists often write their own code, and the first version is usually serial, as the authors note; speeding it up is skilled work that takes time from the science. Here each change had to pass a second agent on cases the first had not seen, and outputs were compared with the original code. In TDDFT the gain was in the method, not only low-level tuning: on one molecule the solver needed 9 iterations instead of 45. Faster codes mean more simulation for the same compute.

Evidence and limits: One lab's preprint, scored by its authors. Repeat runs of the same model ended at different scores; the LAMMPS gains did not hold in pressure-controlled simulations, where one part got slower; the GPU code ignored the request for mixed precision; and the agent-built SCF strategy did not reliably reach the lowest energy or the shortest time. The paper does not say any change was merged into LAMMPS or PySCF. OpenAI supported the work through its academic researcher programme.

By Mathew Chow, Tao E. Li

What happened: Cancer Grand Challenges, founded in 2020 by Cancer Research UK and the US National Cancer Institute, gave Marinka Zitnik's group at Harvard Medical School nearly $7M to build AURORA, an agent that reads papers and databases, calls tools such as AlphaFold and proposes hypotheses and experiments. Three teams get about $1.3M each to use it: PROMINENT on why some mutated cells turn cancerous, NexTGen on CAR-T for children's solid tumours, MATCHMAKERS on how T cells recognise cancer.

Why it matters: A research funder is paying outside teams to put an AI co-scientist into research already under way, and the evaluation is part of the grant: as the teams test its hypotheses in the lab, Harvard will compare the system's reasoning traces with their results, which would show how often its suggestions hold up. Harvard says it is the first Cancer Grand Challenges project on AI and the first to span several funded teams.

Evidence and limits: Nothing has been tested yet: the project launched on 5 November, and neither release gives an end date, a measure of success or a plan to publish the comparison. Neither names the language models AURORA runs on. Harvard gives the money as nearly $7M plus $4M for the teams; Cancer Research UK puts the project at £8m.

What happened: Kos, a life sciences investor based in New York and Athens, said it invested through its first fund, Kos BioVentures, in Fathom's oversubscribed $55M Series A led by Sutter Hill Ventures; co-founders Simos Simeonidis and Alex Tzoukas spoke for the firm. Fathom's own release of 29 October gives the same $55M, dates the round to May and names Lilly Ventures among the participants. It also named Bret Williams, from Magnet Biomedicine and before that Relay Therapeutics, as chief scientific officer.

Why it matters: Since April the stated size of the round has grown by $8M, and the October release names Lilly Ventures, the venture arm of a large drugmaker, among its backers. Fathom says it is now turning molecules designed by its protein-motion simulations into drug programmes, and hired a chief scientific officer who has led target and drug discovery to do it.

Evidence and limits: Neither release says when the extra $8M came in or who put it in, or whether Lilly Ventures was among the unnamed investors in April. The claim that the platform already yields potent, selective candidates against targets once thought undruggable comes with no data.

What happened: The New York Times reports that Radical Numerics has acknowledged it did not work alone on the 20 October announcement: Tabula carried out much of the research, including making the phage cocktail the patient received. Eric Nguyen, its chief executive, wrote to the paper that the two worked closely for seven months and that the contract required both to keep the collaboration secret. Tabula says it led the phage therapy. Both agree Radical designed some of the cocktail's phages and Tabula supplied others.

Why it matters: Radical Numerics did not name Tabula when it announced the treatment on 20 October, and Tabula did much of the lab work. So who made an AI-designed therapy is contested by the two companies that made it. George Church held roles at both; he left Radical's on 5 November and told Nguyen that the argument from confidentiality was very weak on common sense and basic ethics. The Times sets it beside two autumn disputes over whether AI companies used other researchers' work, on an OpenAI proof and on Anthropic's enzymes; both companies denied it.

Evidence and limits: The Times's report was read through a licensed translation; nytimes.com refuses automated readers here. It says Church left the board; where he sat is the one thing the accounts differ on. Neither company has published the contract, and the Times writes that the research was done and the treatment given. Tabula says it will give its own account later; the patient's outcome is still undisclosed.

What happened: A language model labelled 207,049 research sessions on Claude's consumer plans, from 2 August to 30 September 2026, by field and task. Data analysis took 33.7% of Claude sessions and 48.1% on Claude Code, against an estimated 27.6% of researchers' time; data gathering about 12% against 28.6%; ideas 4.2% against 7.1%. In a survey of 528 scientists, mostly in the life and health sciences, those two lagging tasks ranked as the most important. Johannes Hermle, Ben Jones and Lukas Althoff led it.

Why it matters: Like Google's study in September, it puts the gain in work done at a screen, not at the bench. Physical steps take 38-41% of researcher time in chemistry, materials and the biomedical sciences, against 4% in the median field, so the report's model caps their output at three to four times what it would be without AI even if AI did all other work, against up to 15 times in computer science and statistics. On Claude Science, the version wired to research databases, idea generation takes about twice its share on Claude.

Evidence and limits: Only consumer-plan sessions are counted, a model rather than a person assigned each one its field and task, and researchers' time shares come from another model. The survey reached active AI users. The speed-up figures are scenarios from an economic model, not measured output, and this is Anthropic's report on its own products.

By Johannes Hermle, Ben Jones, Lukas Althoff

What happened: The startup, which uses AI agents to look for chip materials, posted five problems written with outside physicists and chemists, Stanford's Felipe Jornada among them: a predictive theory of cuprate superconductors (up to $400,000); cheap but accurate reaction energies, and how chip interfaces get their electrical properties from processing ($200,000 each); what a laser pulse does to a material, and how heat crosses solid interfaces ($100,000 each). Four are scored on published results; the chemistry one on a hidden computed benchmark.

Why it matters: Mathematics has prize problems that show whether AI is closing in; TechCrunch notes materials science has no such set. These come with fixed numbers to hit, from superconductors to the interfaces inside chips. Co-founder Advaith Sridhar told TechCrunch the aim is goalposts that show whether models are improving in this field. On the company's own benchmark, models proposed over 500 new materials but a plausible way to make only one.

Evidence and limits: That frontier models given days on each problem made little headway is the company's account, with no runs published. The company alone judges a solution, may solve a problem itself and close it unpaid, and takes a perpetual, non-exclusive commercial licence to every submission, winning or not.