AI News Archive: July 17, 2026 — Part 14
Sourced from 500+ daily AI sources, scored by relevance.
- Business Brief (July 17): China Spearheads World AI Cooperation Organization
Business Brief (July 17): China Spearheads World AI Cooperation Organization Caixin Global
- China, Russia, and 27 others create World AI body, without US
China has created an international organization to set standards and introduce regulation for AI, inviting 28 other countries to join — but the US, a leading AI powerhouse is not part it. The World Artificial Intelligence Cooperation Organization (WAICO) was established by 29 countries, including China, Russia and Brazil, at a ceremony in Shanghai, China , on July 16. Notably absent are the US, the European Union and its member states, the UK, Japan and South Korea. Chinese AI companies have made a concerted effort to provide an alternative to US dominance . While the US is clearly ahead, Chinese enterprises are looking to narrow the gap in various areas: the open-weight model market , AI cyber protection and open source AI . WAICO has been some years in development and has been designed to set some universal guidelines in AI. Researchers say WAICO differs in three ways from other initiatives to create global AI organizations: membership open to any sovereign state, there is no regime-type test for entry, and its agenda is built around development and the global capability divide. The signing ceremony to create WAICO comes just days after Demis Hassabis, CEO of Google DeepMind, called on the US to take a lead in global AI regulation . “The US is well-positioned to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organization, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives,” Hassabis wrote.
- China-Proposed Global AI Organization Launched at WAIC
The organization, established on the eve of the opening of China’s annual World AI Conference, will advance international cooperation on how AI is developed and used.
- Indonesia Joins China-Initiated AI Body as Its Own Rulebook Takes Shape
Indonesia Joins China-Initiated AI Body as Its Own Rulebook Takes Shape apac.entrepreneur.com
- Announcing the Corrigibility Research Fund
TLDR: I'm managing a new fund, housed at Lightcone Infrastructure, that will award at least $200,000 in grants and prizes for corrigibility research in 2026 . Roughly half will go to traditional grants (first application deadline August 23rd ) and half for prizes recognizing excellent work done this year. If you have interest in working on corrigibility, now is a good time to start! Apply via email: grants@corrigibilityresearch.org Why this fund exists When I first dived into AI safety and alignment in 2009, the field was basically nonexistent. I've been relieved and gratified to see attention and funding grow, especially in the past few years. But even now, nearly all AI safety funding goes to evals, control, or interpretability. Work on alignment itself still remains deeply neglected, and it's only through alignment research that the core problems get solved. At this year's LessOnline I was talking about this dynamic with Peter McCluskey, particularly around our shared interest in corrigibility. In the wake of that conversation, Peter, being a long-time patron of alignment work, directed a portion of his philanthropy towards launching this fund, with the goal of increasing the amount of corrigibility research happening around the world. Lightcone Infrastructure agreed to house the fund and appointed me as its manager, due to my expertise on the subject. Why corrigibility Corrigibility is one of (if not the most) promising angle on creating superhuman artificial intelligences that reliably act in alignment with human values. Training for ethical behavior and direct alignment with humanity runs headlong into known challenges: prosaic methods can't reliably distinguish reward proxies from true goals, instrumental convergence means that even partly-aligned agents will become self-preserving and subversive, [1] and the philosophy of ethics remains woefully unsolved, such that we wouldn't even know what values to instill, even if we could reliably write the AI's values by hand. Corrigibility, by contrast, offers a solution: build an AI that aims to keep the human principal in the driver's seat, empowering them to make wise choices (perhaps aided by the AI's counsel), rather than relying on the AI's direct judgment. This runs the risk of concentrating power in the hands of humans who might misuse it, but human alignment is a less-fraught problem, and is amenable to known strategies, such as democratic oversight. Thanks to the nature of corrigibility, a purely-corrigible agent can be expected to avoid scheming and other instrumentally-convergent strategies. And while corrigibility itself does not solve the limitations of machine learning, it is a simpler target than all of morality, and there are reasons to hope that in practice, imperfectly-corrigible agents still cooperate with their principals to surface their flaws and assist in pushing towards even more corrigible assistants. This robustness gives hope in something closer to an iterative approach, where control and interpretability techniques come together to produce a realistic plan for scaling up to the level of human-intelligence and beyond. (For more of my thoughts, see CAST: Corrigibility As Singular Target ) I am not alone in placing a high level of emphasis on the need for corrigibility. Eliezer Yudkowsky and Paul Christiano have both written at length about how it is central to their best hopes for alignment, as well as many other brilliant alignment researchers. [2] The latest constitution from Anthropic mentions the term sixteen times, and has a dedicated section for it. OpenAI is similarly bullish on creating AIs that are tool-like and deferent. ( Arguably more so than Anthropic!) Despite this, the number of people working directly on corrigibility, such as on clarifying the concept, formalizing it, testing whether and how it can be trained into current systems, and mapping where it breaks, is vanishingly tiny. My hope is that this fund shifts that, both by directly paying for work and by broadly signaling that the work is valuable. What counts as corrigibility research For the purposes of this fund, anything that predictably advances humanity's understanding of the subject is fair game. This spans the full range from pure theory (e.g. formal models, impossibility results, decision-theoretic analysis) to pure empirical work (e.g. training experiments, evaluations of corrigible behavior in frontier models, surveys of how laypeople think about the topic). Distillation of existing work is also welcome. The fund will be prioritizing efforts that cut to the heart of the subject, but feel free to apply for funds even if your research is only tangentially related. The goal is to impact the AIs that actually get built. We're looking for work that is legible and relevant to the people making decisions about real systems. Theoretical work that's judged as too esoteric to be of interest to someone like Joe Carlsmith is unlikely to get funding. Work that's incompatible with mainline capability techniques (e.g. machine learning, transformers) is similarly unlikely to be greenlit by this fund. [3] We won't fund work that, in expectation, notably accelerates AI capabilities. The frontier labs are already doing more than enough to fund work that pushes us towards the brink. If you think your research accelerates things, but also makes progress towards corrigibility, feel free to reach out, but I am likely to point you elsewhere. Work that engages with corrigibility's risks and downsides is encouraged. Corrigibility has known risks and problems, and I want the field's understanding of these downsides to grow alongside work towards showing its promise. Work that presents corrigibility in an overly rosy "everything is safe/fine" way is less likely to get funding, as it might promote a false sense of security, and thereby push the world in a bad direction. [4] You do not need to agree with my particular framing of corrigibility (i.e. CAST) to get funded. Serious engagement with other framings — including arguments that those framings are better — is welcome. Grants and prizes The fund plans to disburse money this year through two general mechanisms: Prizes (>$100k). Retroactive awards for excellent corrigibility research done in 2026 : $40k awarded at the end of September At least $60k awarded in mid-December Prizes require no application. I'll be watching LessWrong, the Alignment Forum, arXiv, and elsewhere. Nevertheless, please send me pointers to corrigibility work (yours or others') that you think ought to be rewarded. Excellent work will be eligible to win prize money multiple times, including potentially in future years. [5] Grants (>$100k). Traditional, apply-in-advance funding for prospective work on corrigibility. To balance getting funds to people sooner and giving more time to prepare, the plan is for there to be two application rounds this year: Round 1: applications due August 23rd, 2026 Round 2: applications due October 31st, 2026 I encourage applicants to be ambitious and ask for however much would actually change their research trajectory towards corrigibility, but I expect typical grants to be around $5k–$35k, buying time for a focused project, a research sabbatical, compute for a mid-sized training run, etc. Grantees should use the funding to begin work this year, but research takes time and it's acceptable to not expect results until 2027. The hope is that work can get off the ground via a grant, and then supported more fully by retroactive prizes once it has been proven to be high-quality. Grants may be supplemental to other funding, such as salaries, other grants, and (of course) prizes. Why lean so hard on prizes? Prizes are results-oriented, rewarding work that actually happened and can be more clearly seen as high-quality. In some cases, prizes buy more research effort per dollar than traditional grants, encouraging a wide range of people to think about corrigibility and whether they have research ideas that might win. Prizes let researchers be rewarded without the overhead of a grant application. Prizes create opportunities to publicly honor good work, bringing attention to the best ideas and the people that developed them. How to apply To apply for a grant (or bring attention to work that might be prizeworthy), simply send an email to grants@corrigibilityresearch.org . The application process is deliberately lightweight and flexible. Tell me what you want to do, and what level(s) of funding you're hoping for. Detailed applications are more likely to get funding insofar as the detail helps demonstrate the worthiness of the work. If something is under-specified, I'm capable of asking follow-up questions. Once you know what you're hoping to do, if writing the application takes you more than a few hours, something has probably gone wrong. Miscellaneous fine print The Corrigibility Research Fund is a program of Lightcone Infrastructure Inc. All disbursements are grants made by Lightcone. My funding decisions are formally recommendations; Lightcone retains final approval on every grant and prize, and controls the fund's assets. I do not represent Lightcone. I serve as an unpaid volunteer. MIRI, my employer, is aware of and supports my role as fund manager, but is not otherwise involved. To avoid conflicts of interest, I won't be awarding prizes or grants to myself, my family, or anyone who is currently at MIRI, regardless of merit. All grants must serve the long-term future of humanity, writ large. No funds may be used for lobbying, political campaign activity, or private benefit. Hope My dream is that a year from now, as a result of this fund, there will be several people who think of corrigibility as their subfield , and will be able to proudly say that they were authors of prize-winning research that moved humanity closer to handling the question of how to make sure the transition to the age of thinking machines goes well. This research, ideally, then goes on to influence the researchers and engineers at frontier labs in years to come, helping them ensure the first artificial general intelligences are corrigible and safe. Let's get to work! ^ A perfectly aligned AGI might, for example, scheme against its creators and escape control so that it can save more lives and generally do more good in the world. ^ See Existing Writing on Corrigibility for some of the main commentary as of 2024. I also have a 2024 bibliography here . ^ If you have a corrigibility idea that depends on an alternative architecture or otherwise ML-incompatible strategy, it may still be of interest and worthy of funding from other sources. Feel free to email me at max@intelligence.org . ^ That being said, don't feel the need to distort your perspective towards doom when applying, or dress up your work in deliberately critical language. The most important criteria by far is the quality of object-level insight, not the tone. The warning about overly-rosy portrayals is more about setting a baseline for where I'm coming from. ^ The long-term financial existence of the fund is not guaranteed, but my intention with prizes like these is to retroactively reward people who direct their attention towards corrigibility. As such, if the fund continues into 2027 and beyond, I intend to allocate some prize money to work done in previous years. The Corrigibility Research Fund would especially love to encourage researcher-investor partnerships that use an impact-certificate-like model . Discuss
- New York State just hit pause on the AI data center boom
As AI use ratchets up, demand for data center capacity is higher than it’s ever been. But New York State is telling the industry: Not so fast. New York Governor Kathy Hochul this week signed an Executive Order described as the “nation’s first moratorium” on new hyperscale data centers, massive factories that typically comprise thousands of servers devouring tens or hundreds of megawatts of power. During this up to one year pause, the state will halt issuance of environmental permits for data centers as it develops a regulatory framework to protect ratepayers, the energy grid, the environment, and local communities. Like other states, New York is seeing “unprecedented” demand for data center development that would ultimately require “massive amounts” of energy and water, Hochul noted. And community backlash seems to be accelerating at the same pace . This is “a symptom of a bigger, nationwide issue,” said Matt Kimball , VP and principal analyst for data center technologies at Moor Insights & Strategy. “Compute demand is far outpacing the grid,” prompting state and local leaders to pause and figure out how to manage things more effectively. Creating a blueprint for local development, community support New York already requires data centers to pay more for energy, or to supply their own, to keep costs affordable for residents. Hochul also plans to pursue legislation that would repeal sales tax exemptions for massive data centers already existing in the state. During the moratorium, New York will develop a “Generic Environmental Impact Statement” (GEIS) to assess the potential environmental impacts of data center construction and operation, including their water and energy demands and impact on air quality. Once it’s lifted, new data center projects will only be allowed to proceed if they strictly observe state, zoning, and other local approvals. On a shorter 60-day timeline, the state will issue a Community Investment Framework (CIF) to provide guidance to local governments negotiating large-scale data center deals, and to ensure operators are investing in and partnering with host communities and workforces. This will set standardized expectations for projects and establish baseline thresholds for data center operators’ investment into local communities. Notably, New York is proposing a contribution of $1 million per megawatt (MW) of anticipated utility demand per project. Thus, 50 megawatts of use would require data center operators to reinvest $50 million into their host community; 400 megawatts would require $400 million. The framework will include ‘Good Neighbor Commitments’ around landscaping, design, and mitigation of noise and light pollution; labor commitments to give organized labor “a seat at the table” to determine wage standards, local hiring, and workforce development; and a community investment fund to support the host community’s “long-term economic vitality and quality of life.” Data center operators, for instance, could provide direct financial support to host communities, or invest in public infrastructure, housing improvements, workforce development and training programs, or in broadband expansion. “Having a published playbook for how to make this work across a state versus having to negotiate this on a county-by-county basis should be a win for everybody,” Moor’s Kimball noted. Separately, New York is also considering establishing a fund that would require data centers to invest in the state’s aging grid infrastructure and support new clean energy procurement. What enterprises and other states should be watching Realistically, a data center buildout takes anywhere from 3 to 5 years from the point of site selection to turning on the switch for the first time, Kimball pointed out. The one-year moratorium doesn’t do too much for that. What matters more is what New York does during that pause, he noted, for example, establishing a regulatory framework to re-price the cost of hyperscale deployment, determining costs for grid upgrades or “bring your own power” expectations, developing requirements for more formalized operator contributions to the local community, or considering the repeal of tax exemptions. “And really, this subsidizing angle is the biggest,” said Kimball. States across the country have been subsidizing buildouts to get data center business for years. “This could signal the beginning of the end of those subsidies for many states.” For enterprise IT leaders, the headline is the signal that power and permitting are now “first-order variables” for infrastructure strategies, right alongside cost and latency requirements, said Kimball. So, if an enterprise’s cloud or co-location strategy or roadmap assumes hyperlocal capacity, that assumption now carries some risk. CIOs and IT leaders should therefore work with providers to gain more clarity on regional capacity. The moratorium could result in some “border-hopping,” with enterprises hosting local servers in adjacent states like Pennsylvania, Connecticut, or New Jersey, but that’s not likely to be widespread, Kimball noted. The realistic regional impact will be “more of a slow squeeze rather than a shock,” he said. This could result in tighter colocation availability and firmer pricing in the New York Metropolitan area over the next few years. Cloud providers may also steer new AI capacity to regions like Georgia, Ohio, Texas, and Utah, where power and permitting are more predictable. An inflection point, but more trickle-down than direct impact Indeed, noted Jeremy Roberts , senior director for research and content at Info-Tech Research Group, the moratorium is an “inflection point” and a “way to placate an increasingly angry public,”. People don’t like the fact that, beyond the initial build, data centers don’t create many jobs, they take up a lot of space, they use a significant amount of power and resources, and they can be “noisy and smelly.” However, the impact of the moratorium is likely going to be “macro” for everyday enterprises, as New York is specifically targeting hyperscale data centers. “If you were planning on building a data center in New York and your name is not [Microsoft CEO] Satya Nadella, it’s probably not going to affect you,” said Roberts. But the consequences of the move will certainly trickle down, for instance, with AI device or hardware purchases supplanting software acquisition. Roberts pointed to IBM’s history-making stock plunge this week, which the company attributed to enterprise buyers diverting IT budgets away from software and mainframes to stockpile AI hardware like servers and memory chips to get ahead of anticipated supply issues and price increases. If enterprises plan to invest in anything that uses storage or CPUs, they will be paying more in the future, Roberts said. “It’s a symptom of a problem you’re going to feel.” That said, constraints usually inspire innovation; if a hyperscaler can’t build a 50MW data center, it will likely find ways to string together smaller data centers or adapt in other ways. This could “percolate” across the industry, Roberts said. “People are endlessly creative.”
- Philippines lodges protest over China Daily's 'racist' AI video depicting Filipinos as monkey character
Philippines lodges protest over China Daily's 'racist' AI video depicting Filipinos as monkey character Gulf News
- Opposition to data centers grows in cramped urban Japan
The more Yoriko Kitagawa learns about a massive data center to be built near her home in Hino, on the outskirts of Tokyo, the more she worries.
- AI stocks keep falling, while oil prices keep climbing
AI stocks keep falling, while oil prices keep climbing San Francisco Chronicle
- The sell-off for AI stars worsens, while oil prices keep jumping
The sell-off for AI stars worsens, while oil prices keep jumping Houston Chronicle
- AI stocks keep falling, while oil prices keep climbing
AI stocks keep falling, while oil prices keep climbing Dallas News
- The sell-off for AI stars worsens, while oil prices keep jumping
The sell-off for AI stars worsens, while oil prices keep jumping Boston Herald
- MLB restricts dugout iPad use to prevent AI help with strategy. Ottavino says Mets were involved
MLB restricts dugout iPad use to prevent AI help with strategy. Ottavino says Mets were involved Toronto Star
- MLB restricts dugout iPad use to prevent use of AI to make decisions
MLB restricts dugout iPad use to prevent use of AI to make decisions San Francisco Chronicle
- OpenAI’s $70 ChatGPT basketball sells out despite online mockery
Memes mocking the merchandise did not appear to put off customers
- ‘Who Asked for This?’ OpenAI Is the Latest Tech Giant to Get in the Merch Game
Every brand wants to be a lifestyle brand these days. It’s not an easy sell.
- Operators and Fulfil Launch the Operators Portal: 4.7 Million Words of Real Ecommerce Strategy, Now AI-Searchable in Seconds
Operators and Fulfil Launch the Operators Portal: 4.7 Million Words of Real Ecommerce Strategy, Now AI-Searchable in Seconds azcentral.com and The Arizona Republic
- Alchemy Avenue Turns Marketing Into Measurable Growth with AI-Powered Full-Funnel Strategy in 2026
Alchemy Avenue Turns Marketing Into Measurable Growth with AI-Powered Full-Funnel Strategy in 2026 azcentral.com and The Arizona Republic
- OpenAI tweaks chat access in the ChatGPT app for Mac
OpenAI has updated its recently redesigned ChatGPT app for Mac to make chats easier to access once again. Here are the details.
- After YouTube, TikTok is testing its own AI likeness detection tool
As deepfakes spread, TikTok joins YouTube in giving creators tools to detect and report unauthorized AI likenesses of themselves.
- 'Prefer an outright ban': Small Texas town weighs data center restrictions amid industry growth
'Prefer an outright ban': Small Texas town weighs data center restrictions amid industry growth Austin American-Statesman
- Databricks hits $188B valuation, extending its run as AI’s favorite second act
Databricks has remade its image into an AI company and has published research on the cost savings of open weight AI models for coding.
- Databricks Set to Hit $188 Billion Valuation With New Investment From Coatue
The startup’s valuation jumps 40% as the AI boom has driven demand for its data-analytics software.
- Databricks valuation jumps 40% in 6 months to $188 billion
Databricks raises new funding at a $188 billion valuation, thanks to AI success.
- Databricks opens strategic funding round at $188bn valuation
Last August, co-founder and CEO Ali Ghodsi told the Wall Street Journal that, in his view, ‘Databricks has a shot to be a trillion-dollar company’. Read more: Databricks opens strategic funding round at $188bn valuation
- Databricks Is Now Worth $188 Billion. Its Next Move Could Reshape Enterprise AI
The Coatue-led round gives co-founder Ali Ghodsi more money for AI products and acquisitions—and more freedom to remain private.
- Databricks raising new funding at $188B valuation
Databricks Inc. is in the process of finalizing a funding round that will value it at $118 billion. The company announced the deal on Thursday without disclosing the amount that it’s raising. According to the Wall Street Journal, the round will add $3 billion to Databricks’ balance sheet. Coatue is leading the investment with contributions […] The post Databricks raising new funding at $188B valuation appeared first on SiliconANGLE .
- Anthropic is in talks to lease AI computing power from Meta in a $10 billion deal
The AI startup proposed the deal in June; the talks are at an early stage and may not result in an agreement
- Meta and Anthropic in talks for up to $10bn data centre deal
Social media giant is considering launching a cloud business as it spends $145bn on infrastructure
- Meta, Anthropic in talks for potential $10 billion compute lease deal: NYT
Meta Platforms is reportedly discussing a significant deal to lease computing power to Anthropic. This potential agreement could be valued at as much as ten billion dollars over two years. Such a partnership would help Meta diversify its revenue streams beyond advertising. Anthropic proposed this deal in June, and Meta is currently considering the terms. Both companies retain the option to exit the agreement early if needed.
- Anthropic is in early talks to lease $10 billion in compute from Meta
Anthropic is in very preliminary talks to lease computing power from Meta in a deal worth about $10 billion, the New York Times reported on Friday. CNBC confirmed the early-stage discussions. The talks come weeks after Anthropic announced a $1.25 billion-per-month arrangement with SpaceX to use the Colossus 1 data centre’s Nvidia GPUs. The deal […] This story continues at The Next Web
- Superpowered spreadsheets? SAP closes buyout of tabular AI startup Prior Labs
"Most of the world's valuable data still sits in spreadsheets and databases. [This] will do for structured data what LLMs have done for unstructured"
- Germany’s Prior Labs raises €1 billion and exits to SAP 18 months after being founded
Berlin-based frontier AI lab Prior Labs today announced that the multinational software company SAP has completed its acquisition of the company, supported by more than €1 billion in investment from SAP in order to fund infrastructure, hiring, and long-term frontier research. Prior Labs will continue under its own brand, leadership, research agenda and customer relationships, […] The post Germany’s Prior Labs raises €1 billion and exits to SAP 18 months after being founded appeared first on EU-Startups .
- Prior Labs Acquisition Closed: Backed by €1B+ to Scale Its Frontier AI Lab for Enterprise Data
Prior Labs Acquisition Closed: Backed by €1B+ to Scale Its Frontier AI Lab for Enterprise Data azcentral.com and The Arizona Republic
- Chinese AI model takes US tech industry by surprise with abilities rivaling Claude and ChatGPT
Chinese AI model takes US tech industry by surprise with abilities rivaling Claude and ChatGPT Houston Chronicle
- Antwerp’s Sightera Biosciences raises €3 million to scale its patient-derived AI drug discovery platform
Sightera Biosciences, an Antwerp-based AI drug discovery startup and spin-off from the University of Antwerp (UA) and Antwerp University Hospital (UZA), today announced the closing of a €3 million pre-Seed financing led by Entourage, Anacura and QBIC. The company plans to use this funding to expand the team, accelerate the expansion of its AI-native drug […] The post Antwerp’s Sightera Biosciences raises €3 million to scale its patient-derived AI drug discovery platform appeared first on EU-Startups .
- Ex-Ultrahuman Executive’s AI Hardware Startup Aina Raises $5.5 Million in Seed Funding
Ex-Ultrahuman Executive’s AI Hardware Startup Aina Raises $5.5 Million in Seed Funding india.entrepreneur.com
- Why the first GPU financiers are turning to inference chips in a $400 million deal
A $400 million chip-backed loan points to the next wave of AI infrastructure deals.
- China’s Moonshot Unveils AI Model, Fueling Tech Rout
Chinese AI pioneer Moonshot unveiled a new model that it claims performs on par with some of the top-tier platforms from OpenAI and Anthropic PBC, the latest sign that the Asian country’s artificial intelligence labs are closing a technology gap with the US.
- China’s Moonshot AI Releases Model to Challenge Top U.S. Systems
The company says its model outperforms some cutting-edge U.S. systems, the latest sign that Chinese labs can rival American counterparts.
- China’s Moonshot AI Unveils Kimi Model, Threatening America’s Lead
China’s Moonshot AI unveiled a freely available artificial intelligence model that seemed to narrow the gap with cutting-edge offerings from U.S. tech companies.
- China's new AI model is putting fresh pressure on Silicon Valley and Washington
China's new AI model is putting fresh pressure on Silicon Valley and Washington Business Insider
- The next DeepSeek? A surprise AI breakthrough in China is rattling US market heavyweights.
The next DeepSeek? A surprise AI breakthrough in China is rattling US market heavyweights. Business Insider
- Moonshot AI’s New Kimi K3 Challenges U.S. Frontier Models
Moonshot AI’s New Kimi K3 Challenges U.S. Frontier Models The Information
- Chinese AI start-up Moonshot launches model challenging Anthropic’s lead
Kimi K3 shows narrowing gap between US and China on frontier AI
- China's Moonshot unveils world's largest open AI model, closing in on US rivals
The launch, which comes a month after Anthropic's Fable and Mythos models were abruptly withdrawn by the U.S. government due to security concerns, underscores how quickly China's open AI ecosystem is narrowing the gap with the most advanced U.S. systems.
- Decoded: What is Kimi K3, an open AI model challenging OpenAI and Anthropic
Moonshot AI says its 2.8 trillion-parameter Kimi K3 matches or outperforms leading proprietary models in several coding and software engineering benchmarks, highlighting the rise of open-weight AI
- China's Moonshot launches world's largest open AI model, nears US rivals
The Chinese startup said its 2.8 trillion-parameter open-weight model delivers performance approaching Anthropic's frontier systems while supporting a 1 million-token context window
- Moonshot AI unveils Kimi K3, the world’s largest open-weight AI model: What to know
Moonshot AI unveils Kimi K3, the world’s largest open-weight AI model: What to know
- Moonshot AI unveils world’s largest open-source AI model as China narrows gap with US rivals
Chinese start-up Moonshot AI has launched the world’s largest open-source artificial intelligence model, claiming to outperform leading US systems from Anthropic and OpenAI in some capabilities, as domestic developers race to challenge American dominance. Beijing-based Moonshot said its Kimi K3, unveiled late on Thursday, had achieved “open frontier intelligence” with 2.8 trillion parameters, which was significantly larger than previous open models from Chinese competitors, including DeepSeek’s...