Open AI's new model, GPT-4o, taught a student live while he solved a question.
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Computer Science ChatGPT and other Large Language Models
- Thread starter Bogeyman
- Start date
One concept for deployment of AI and LLM models: The large language models are as the name suggest, large. What researchers have realized with transformer architecture is that, as the models grow in size, they start to exhibit different type of human like reasoning that smaller models simply weren't able to. But as these models grow they also require lots of data and lots of computation power. Now LLM work well by training on a general data, the more they know about the world, the better they work at most tasks, much more human like than the AI we had 10 years ago. But when domain specific knowledge is limited, particularly restricted domains like military, fine tuning a model is also difficult.
Also, LLM are not good at maths and numerical data, they perform well with text data and manual labels, they require extra tools for chatgpt to be able to use math( model can write the equation and solve it with external calculator). And so an AI used for various such purposes must also be trained to be able to use those external tools ( via special language tokens). This requires military data to be transformed into a format useable by LLM to leverage them.
Now getting to the crux of the issue. Being able to use these models in real time is a challenge of network communication. You can't have an instance of LLM running on every vehicle, nor would it be as effective. However having a single centralized instance makes bandwidth and security / ew resistance an issue. So the concept should be, basically command and control kind of vehicles, being accompanied by trailers that have servers running instances of LLM and communications equipment, to be able to communicate with assets in the area, and local communication will be much faster as well. This still requires some of the data to be preprocessed in place, however. So any sensor equipped system should be able process and maybe tokenize its data by itself to be shared to these models and compress them. This requires thinking with lessons from Big Data and IoT fields, that is, thinking the system as a whole, having asymmetrical distributed architecture, and trying to minimize data transfer overheads and keep simplicity of data processing commands. Also large assets like ships should have datacenters designed around future use of such advanced AI.
Also, LLM are not good at maths and numerical data, they perform well with text data and manual labels, they require extra tools for chatgpt to be able to use math( model can write the equation and solve it with external calculator). And so an AI used for various such purposes must also be trained to be able to use those external tools ( via special language tokens). This requires military data to be transformed into a format useable by LLM to leverage them.
Now getting to the crux of the issue. Being able to use these models in real time is a challenge of network communication. You can't have an instance of LLM running on every vehicle, nor would it be as effective. However having a single centralized instance makes bandwidth and security / ew resistance an issue. So the concept should be, basically command and control kind of vehicles, being accompanied by trailers that have servers running instances of LLM and communications equipment, to be able to communicate with assets in the area, and local communication will be much faster as well. This still requires some of the data to be preprocessed in place, however. So any sensor equipped system should be able process and maybe tokenize its data by itself to be shared to these models and compress them. This requires thinking with lessons from Big Data and IoT fields, that is, thinking the system as a whole, having asymmetrical distributed architecture, and trying to minimize data transfer overheads and keep simplicity of data processing commands. Also large assets like ships should have datacenters designed around future use of such advanced AI.
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DeepSeek shocks AI world with 'cheap' Chinese chatbot
Chinese artificial intelligence (AI) app DeepSeek has overtaken ChatGPT and other rivals to become the top-rated free application on Apple's App Store in the US, UK and China.
The app has surged in popularity since its launch in January, challenging the widely-held belief that America is the untouchable leader of the AI industry.
It is powered by the open-source DeepSeek-V3 model, which its researchers claim was developed for less than $6m - significantly less than the billions spent by rivals.
But this claim has been disputed by others in the AI space.
After DeepSeek-R1 was launched earlier this month the company boasted of "performance on par with" one of ChatGPT maker OpenAI's latest models - when used for tasks such as maths, coding and natural language reasoning.
Silicon Valley venture capitalist and Donald Trump advisor Marc Andreessen described DeepSeek-R1 as "AI's Sputnik moment", in a reference to the first artificial Earth satellite that was launched by the Soviet Union in 1957.
Advanced chips power the training of AI models like ChatGPT and DeepSeek.
But since 2021 the US government has widened its restrictions on advanced chips being sold to China.
In order to continue their work without steady supplies of imported advanced chips, Chinese AI developers have shared their work with each other and experimented with new approaches to the technology.
This has resulted in AI models that require far less computing power than before. It also means that they cost a lot less than previously thought possible, which has the potential to upend the industry.
Shares in AI-related companies based in the US, such as Nvidia, Microsoft and Meta were down on Monday morning - and the development knocked European share prices.
ASML, the Dutch chip equipment maker, saw its share price tumble by more than 10% while shares in Siemens Energy, which makes hardware related to AI, plunged by 21%.
"This idea of a low cost Chinese version hasn't necessarily been forefront, so it's taken the market a little bit by surprise," said Fiona Cincotta, senior market analyst at City Index.
"So if you suddenly get this low-cost AI model, then that's going to raise concerns over the profits of rivals, particularly given the amount that they've already invested in more expensive AI infrastructure."
And Singapore-based technology equity advisor Vey-Sern Ling told the BBC it could "potentially derail the investment case for the entire AI supply chain".
But Wall Street banking giant Citi cautioned that while DeepSeek could challenge the dominant positions of American companies like OpenAI, issues faced by Chinese firms could hamper their development.
"We estimate that in an inevitably more restrictive environment, US' access to more advanced chips is an advantage," its analysts said in a report.
Last week, a consortium of US tech firms and foreign investors announced The Stargate Project, a company which is putting $500bn into AI infrastructure in Texas.
Who founded DeepSeek?
The company was founded in 2023 by Liang Wenfeng in Hangzhou, a city in southeastern China.The 40-year-old, an information and electronic engineering graduate, also founded the hedge fund that backed DeepSeek.
He reportedly built up a store of Nvida A100 chips, now banned from export to China. Experts believe this collection - which some estimates put at 50,000 - led him to launch DeepSeek, by pairing these chips with cheaper, lower-end ones that are still available to import.
Mr Liang was recently seen at a meeting between industry experts and the Chinese premier Li Qiang.
In a July 2024 interview with The China Academy, Mr Liang said he was surprised by the reaction to the previous version of his AI model.
"We didn't expect pricing to be such a sensitive issue," he said.
"We were simply following our own pace, calculating costs, and setting prices accordingly."
Nvidia shares sink as Chinese AI app DeepSeek spooks US markets
Share prices in US and European firms fall as the Chinese technology threatens to disrupt the AI industry.
www.bbc.com
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The west is likely going to do massive trash talking about the Deepseek AI, but I think it's important that tech companies in Türkiye also try to make one and release it. If we deliver an AI like Deepseek and it means we join the AI bandwagon the tech companies will be hit brutally. I think it's important. Just like the tech war in 90's. That lead to better and faster processors
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While US companies are at each other's throats and China is pushing the sector with state support, the EU region is currently a bystander. In our country, unfortunately, despite the fact that it will affect all industrial and service areas, it is of interest to a very limited circle. The T3 foundation is trying to create synergy for a local language model, but there is no serious capital working on the subject except for a few defense/aerospace based companies.
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It is because every minister is from the stone age. The most they can accomplish is use a MS program and their phone. My cousin were among the top 3-4 tech engineers in the late 90's that was responsible for TC internet infrastructure.
We seriously need to put some money into cyber security, but also work on developing an AI on national level.
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Deepseek V4 has become the tool which I uesd everyday, that is what i never consider before.
I only treat it as a toy at the first.
I'm glad to see such a open source LLM born in China, and it is safe to use it or deploy it that any closedsource LLM.
If someone had not used it, I suggest you have a try.
(I know the Gemini Claude or GPT is also cheap in Turkey, but all of them is belong to US.)
DeepSeek did not disclose whether the permanent price cut was due to increased supply of Huawei’s Ascend 950 chips, which it used to maximize V4’s performance.
The company cut V4‑Pro API costs to between 0.025 and 6 yuan per million tokens (about $0.0035 to $0.83) depending on usage type, from 0.1 to 24 yuan previously, the statement said. A “token” is a unit of text processed by the AI model.
Huawei’s AI chip sales have benefited from U.S. export controls that prevent Nvidia from selling its most advanced semiconductors in China, although separate curbs on chipmaking equipment exports have limited Huawei’s ability to scale up Ascend production.
When DeepSeek launched V4 last month, it said the Pro version would cost up to 12 times more than the less powerful Flash version due to “constraints in high-end compute capacity,” limiting availability.
It also said Pro pricing was expected to fall sharply once Huawei Ascend 950 supernodes are launched in large quantities in the second half of the year.
I only treat it as a toy at the first.I'm glad to see such a open source LLM born in China, and it is safe to use it or deploy it that any closedsource LLM.
If someone had not used it, I suggest you have a try.
(I know the Gemini Claude or GPT is also cheap in Turkey, but all of them is belong to US.)
China's DeepSeek to make permanent 75% price cut on flagship V4‑Pro AI model
May 23 (Reuters) - Chinese artificial intelligence startup DeepSeek will make permanent a 75% price cut on its flagship V4‑Pro artificial intelligence model, keeping prices at a quarter of their original level, the company said in a statement on Saturday.DeepSeek did not disclose whether the permanent price cut was due to increased supply of Huawei’s Ascend 950 chips, which it used to maximize V4’s performance.
The company cut V4‑Pro API costs to between 0.025 and 6 yuan per million tokens (about $0.0035 to $0.83) depending on usage type, from 0.1 to 24 yuan previously, the statement said. A “token” is a unit of text processed by the AI model.
Huawei’s AI chip sales have benefited from U.S. export controls that prevent Nvidia from selling its most advanced semiconductors in China, although separate curbs on chipmaking equipment exports have limited Huawei’s ability to scale up Ascend production.
When DeepSeek launched V4 last month, it said the Pro version would cost up to 12 times more than the less powerful Flash version due to “constraints in high-end compute capacity,” limiting availability.
It also said Pro pricing was expected to fall sharply once Huawei Ascend 950 supernodes are launched in large quantities in the second half of the year.
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Another issue is that the widespread use of LLMs has evidently accelerated the deterioration of the labor market environment. Both China and the United States are now facing massive layoffs at high-tech companies and rising unemployment rates. I believe many people in South Korea, Japan, and India have also experienced this. If we combine this with the recent development of embodied intelligence and drone technology, humanity may be on the verge of a new era of industrial revolution. At the very least, it is now clear that the battlefield has been transformed, and I believe that future battlefields will be dominated by drones and multi-legged robots connected to low-earth-orbit internet satellites.(Translate from Chinese by deepseekV4)
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CEO of Chinese Anthropic rival tells Elon Musk that China will have a Fable 5-class AI model before next year — it ‘won’t take that long’ says Jie Tang in response to Musk's prediction of a Q1 target
A Chinese AI lab is allegedly catching up with Anthropic.
The CEO and founder of one of China's leading AI startups has warned that a rival Chinese Fable 5-class AI model is closer than even Elon Musk thinks, in a recent X interaction. In a post on his own platform, Musk stated that China's attempt at an AI model to rival Anthropic's new Fable 5 offering will arrive in "Probably Q1," to which Jie Tang replied, "won't take that long."
Jie Tang might be a relative unknown in the Western world, but he’s the founder of Z.ai, formerly known as Zhipu AI, and is one of the leading AI startups in China, based in Beijing. It released its latest AI model, GLM-5.2, on June 16, 2026, and the company’s benchmarks show that it has almost the same performance as Anthropic’s Opus 4.7 to 4.8, which launched in April and May of this year, respectively. It has also consistently outperformed OpenAI’s GPT-5.5 and Google’s Gemini 3.1 Pro.
Anthropic launched its latest, most powerful public model, Fable 5, last June 10, which itself is a nerfed version of the Mythos 5 that was previewed to select entities in early April. However, three days after the general public could start enjoying Fable 5, the U.S. government put an export control directive on the model and barred all foreign nationals, including Anthropic’s own employees, from accessing it. Since the company determined that it cannot guarantee total compliance with the order, it decided to pull both Fable 5 and Mythos 5 from across the globe.
If Z.ai is indeed working on a more powerful frontier AI model that could compare against or even outperform Fable 5, users who want the latest, cutting-edge models might just switch over to the Chinese AI provider once it goes online. This does not mean that the U.S. would automatically be disadvantaged, though, as it still has Mythos and Fable. The U.S. government claimed that it only put the export control Fable 5 after Amazon allegedly discovered that the Anthropic model’s guardrails could be broken, and that the company refused to address it before going live. In its defense, the AI startup claims that the so-called jailbreak is minor and is replicable in other models like GPT-5.5. Washington claims that once the issue has been resolved through a patch, it will lift the restrictions and allow foreign users to access Fable 5 again.
Both China and the U.S. are in a race to get the most advanced AI model possible. The U.S. has put major roadblocks to block China’s progress, including the imposition of export controls on the latest AI chips, tools needed to make the latest semiconductors, and even the software needed to design them. Despite that, Chinese firms find ways to achieve breakthroughs, with one of the biggest developments so far being the arrival of DeepSeek in late 2024. It seems that Jie Tang wants to take the Chinese crown away from DeepSeek by claiming that Z.ai could come out with a model that could match the United States’ best in the coming months.
Another miracle open source LLM.
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China's Moonshot AI claims Kimi K3 can rival OpenAI and Anthropic
Chinese AI start-up Moonshot has unveiled a massive new artificial intelligence model it says can rival top American firms.The company launched Kimi K3, containing 2.8 trillion parameters, which serves as a measure of an AI's scale and processing power.
Kimi K3's full capabilities – coding, knowledge work, and reasoning – will be known when it is released as an open-source model on 27 July.
The sudden breakthrough suggests that China's tech prowess is rapidly narrowing the capabilities gap, upending long-held assumptions in the West that Chinese developers trail their American peers.
Its arrival later this month will make it the world's first open-source model in the three-trillion-parameter class that can be freely downloaded, run and customised by outside developers.
The release comes at a highly sensitive moment for the global technology sector, just weeks after the US government abruptly forced American developer Anthropic to temporarily withdraw its flagship Fable and Mythos models due to severe cybersecurity concerns.
While Washington has since lifted those restrictions, the initial move highlights how the US government now views advanced AI software as critical national infrastructure, labelling frontier models as vital national security assets subject to strict export controls.
However, the rapid arrival of Kimi K3 suggests Chinese firms are successfully bypassing these regulatory barriers and advancing independently despite US restrictions on hardware sales.
Heavily backed by domestic tech giants Alibaba and Tencent, Moonshot has quickly risen to the forefront of China's generative AI ecosystem.
In a statement the company said that K3 stands as Moonshot AI's "most capable flagship model to date".
Unlike closed, proprietary American systems from OpenAI or Anthropic, Kimi K3's open nature allows global users to modify the system for advanced reasoning and complex software development.
Moonshot AI noted that the system is uniquely built to operate with "minimal human supervision" to sustain tasks such as engineering and coding.
Third-party evaluations from Artificial Analysis and Arena.ai show the model performing on a par with leading models in the US, such as OpenAI's GPT and Anthropic's Claude.
In these independent benchmarks, Kimi K3 ranked first in web interface engineering, outperforming Anthropic's Fable system in blind human-preference tests.
While the system's massive size means running it locally requires significant computing equipment, making it open-source could heavily disrupt Silicon Valley's commercial models.
The announcement had an immediate impact on shares in Moonshot's domestic competitors Zhipu and MiniMax, which tumbled sharply in Hong Kong by about 27% and 16% respectively.
China's Moonshot AI claims Kimi K3 can rival OpenAI and Anthropic
The company has unveiled a massive new artificial intelligence model it says can take on top American firms.
www.bbc.com
Too fast.
The other thing is everyone had know deepseek have better, the only question is when will they release it.
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DeepSeek Liang Wenfeng Investor Meeting
Hi all, a transcript of an investor call between DeepSeek’s founder and CEO, Liang WenFeng, and a group of investors has leaked. It is incredibly detailed, outlining his vision, mission, and view on the current challenges faced by Chinese labs. I highlighted the key points I think are crucial, but the full transcript translation is also attached below. I want to preface that, since it was AI-translated, please reference and be aware there might be slight discrepancies in the language. With that, I believe this is worth reading for anyone interested in the open source/ Chinese model space.
TLDR
On Mission
1. Started with no intention of going public or maximizing profit—purely believed AI would benefit humanity.
2. Company is vision-driven, not KPI-driven; no formal organizational structure.
3. Vision isn’t written down—it’s embedded in actions and attitudes toward the world.
4. Jack Welch’s key lesson still holds: a company’s most important asset is its mission.
Open Source Strategy
5. Open source is a core belief, not a forced move (unlike some competitors).
6. AI is big enough (potentially 10% of global GDP) that you cannot monopolize it—must share.
7. Open source doesn’t hurt commercialization—as long as you only take reasonable profit.
8. Open-sourced models are identical to those deployed internally—no weaker versions.
9. Not worried about others deploying their models to compete; market is big enough.
10. Open source is the “sweet spot” for a company of their size—startups too small, big companies too bureaucratic.
Pricing and Commercialization
11. Pricing is based on 10-month cost recovery—reasonable profit, not profit maximization.
12. Once cut a model’s price to 1/4 and the team celebrated—affordability is the goal.
13. Low cost is a result, not an afterthought; efficiency enables training larger models with limited compute.
14. They are already commercializing (C-side users, B-side revenue) but not *as the goal*.
15. Still far from a full pivot to commercialization; AGI remains the priority.
16. Selling APIs isn’t that attractive—just a few people maintain it, no sales or customer support needed.
Product and Technology
17. Products (C-side/B-side) are byproducts of the AGI journey—not the main focus.
18. Off-main-line areas: 3D, video generation, world models—not related to the intelligence ceiling.
19. Multimodality is a component, not the main line or intelligence itself.
20. Hallucinations are a product issue—solvable but not the top priority.
21. Coding Agent is the current priority; other vertical agents (finance, medical) come later.
22. If the AI era produces many trillion-dollar companies, being one of them is already good.
AGI Roadmap
23. AI lacks continuous learning—that’s the next major breakthrough needed.
24. Next-gen model must have continuous learning capability to be called “next-gen.”
25. First goal: models that help DeepSeek’s own development—”useful for us first.”
26. Roadmap: CoT → Agent → continuous learning → self-iterating singularity → embodied intelligence.
27. The singularity will be gradual, not sudden; AI will accelerate AI research.
28. Embodied intelligence is the ultimate endpoint—human needs are physical, not digital.
Competitive Landscape
29. Gap with the U.S. is primarily in compute resources, not talent (same talent pool).
30. Final differentiation among model competitors: cost (#1), time, and user experience.
Additional Key Points
- Restraint is a strategy: give up some things to gain more important ones.
- China has too many base-model companies; convergence is inevitable.
- Anthropic’s lead over OpenAI is episodic, not long-term.
- Team stability is the *only* non-negotiable core interest; funding has helped secure it.
- No desire to become the next ByteDance or Tencent—not chasing super-app status.
- Last year’s viral surge during Spring Festival was never in their plan.
- Two management lines: top-down (formal work, <50% of time) and bottom-up (free exploration).
- They generally don’t work overtime—research needs relaxed environments; they are highly focused.
- The company is a product of its era and circumstances, not imitation of others.
- A vision of “taking more” means you’ve already lost—the world rewards those who take less.
aiproem.substack.com
Hi all, a transcript of an investor call between DeepSeek’s founder and CEO, Liang WenFeng, and a group of investors has leaked. It is incredibly detailed, outlining his vision, mission, and view on the current challenges faced by Chinese labs. I highlighted the key points I think are crucial, but the full transcript translation is also attached below. I want to preface that, since it was AI-translated, please reference and be aware there might be slight discrepancies in the language. With that, I believe this is worth reading for anyone interested in the open source/ Chinese model space.
TLDR
On Mission
1. Started with no intention of going public or maximizing profit—purely believed AI would benefit humanity.
2. Company is vision-driven, not KPI-driven; no formal organizational structure.
3. Vision isn’t written down—it’s embedded in actions and attitudes toward the world.
4. Jack Welch’s key lesson still holds: a company’s most important asset is its mission.
Open Source Strategy
5. Open source is a core belief, not a forced move (unlike some competitors).
6. AI is big enough (potentially 10% of global GDP) that you cannot monopolize it—must share.
7. Open source doesn’t hurt commercialization—as long as you only take reasonable profit.
8. Open-sourced models are identical to those deployed internally—no weaker versions.
9. Not worried about others deploying their models to compete; market is big enough.
10. Open source is the “sweet spot” for a company of their size—startups too small, big companies too bureaucratic.
Pricing and Commercialization
11. Pricing is based on 10-month cost recovery—reasonable profit, not profit maximization.
12. Once cut a model’s price to 1/4 and the team celebrated—affordability is the goal.
13. Low cost is a result, not an afterthought; efficiency enables training larger models with limited compute.
14. They are already commercializing (C-side users, B-side revenue) but not *as the goal*.
15. Still far from a full pivot to commercialization; AGI remains the priority.
16. Selling APIs isn’t that attractive—just a few people maintain it, no sales or customer support needed.
Product and Technology
17. Products (C-side/B-side) are byproducts of the AGI journey—not the main focus.
18. Off-main-line areas: 3D, video generation, world models—not related to the intelligence ceiling.
19. Multimodality is a component, not the main line or intelligence itself.
20. Hallucinations are a product issue—solvable but not the top priority.
21. Coding Agent is the current priority; other vertical agents (finance, medical) come later.
22. If the AI era produces many trillion-dollar companies, being one of them is already good.
AGI Roadmap
23. AI lacks continuous learning—that’s the next major breakthrough needed.
24. Next-gen model must have continuous learning capability to be called “next-gen.”
25. First goal: models that help DeepSeek’s own development—”useful for us first.”
26. Roadmap: CoT → Agent → continuous learning → self-iterating singularity → embodied intelligence.
27. The singularity will be gradual, not sudden; AI will accelerate AI research.
28. Embodied intelligence is the ultimate endpoint—human needs are physical, not digital.
Competitive Landscape
29. Gap with the U.S. is primarily in compute resources, not talent (same talent pool).
30. Final differentiation among model competitors: cost (#1), time, and user experience.
Additional Key Points
- Restraint is a strategy: give up some things to gain more important ones.
- China has too many base-model companies; convergence is inevitable.
- Anthropic’s lead over OpenAI is episodic, not long-term.
- Team stability is the *only* non-negotiable core interest; funding has helped secure it.
- No desire to become the next ByteDance or Tencent—not chasing super-app status.
- Last year’s viral surge during Spring Festival was never in their plan.
- Two management lines: top-down (formal work, <50% of time) and bottom-up (free exploration).
- They generally don’t work overtime—research needs relaxed environments; they are highly focused.
- The company is a product of its era and circumstances, not imitation of others.
- A vision of “taking more” means you’ve already lost—the world rewards those who take less.
MUST READ: DeepSeek Liang Wenfeng Investor Meeting
open source, commercialization, agents, mission, competition
aiproem.substack.com
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Interesting that there isn't anything about Safety/safe usage of the AI/AGI. What about Ethics ?
I just saw this article
www.bbc.com
Man... with AI being trained by AI, anything is possible. It never sleeps.
I just saw this article
China's Moonshot AI stole from Anthropic, Trump tech adviser says
The allegations come as Chinese AI companies are facing increased US government scrutiny.
www.bbc.com
Man... with AI being trained by AI, anything is possible. It never sleeps.
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There is no limit for using AI.Interesting that there isn't anything about Safety/safe usage of the AI/AGI. What about Ethics ?
I just saw this article
China's Moonshot AI stole from Anthropic, Trump tech adviser says
The allegations come as Chinese AI companies are facing increased US government scrutiny.
Man... with AI being trained by AI, anything is possible. It never sleeps.
The Pentagon has been applying AI in warfare since its appered, and Israel is still using AI to massacre Palestinians.
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Open Weights and American AI Leadership
July 24, 2026
In the 1980s, early open-source software pioneers challenged the prevailing belief that software would advance only if companies kept tight control over their code. This movement pushed for a transparent ecosystem where developers around the world could study, modify, and improve software. Software developed by the open-source community now supports most of the internet and underlies systems used by the world’s largest technology companies, as well as the U.S. military and federal agencies conducting scientific research, cybersecurity, and other critical missions. Open source did more than lower the cost of software; it created a shared foundation of knowledge on which generations of American
engineers and entrepreneurs built their institutional sovereignty.
The United States now faces a similar choice with artificial intelligence. Our AI leadership will be judged not by one frontier AI model, but by whether the United States builds a strong, open ecosystem that diffuses into every sector. This is essential for creating opportunities for innovation and prosperity across the country. It requires expanding access to AI, encouraging competition, robust application layers, and giving Americans greater control over the technology they rely on. Open-weight models—AI models that anyone can download, inspect, modify, and run on their own infrastructure—are an important part of that foundation because they make advanced AI more accessible, adaptable, and widely available.
Open weights expand access to the AI economy. Startups, established businesses, universities, and public institutions can build on advanced models without training one from scratch or paying frontiermodel prices for every task. Open weights let every organization match the right model to the right job at the right cost, reserving frontier-scale capability for genuine frontier problems and running efficient, specialized models everywhere else. That discipline is what will make AI economically sustainable as its use scales into the billions of everyday tasks. America wins the AI era by diffusing it into the workflows of factories, hospitals, farms, classrooms, and main street businesses.
Open weights also strengthen competition and competition is what keeps the gains of AI broadly shared rather than concentrated in a few hands. By allowing many organizations to build, adapt, and deploy advanced models, open weights create rivalry not only among model developers but across cloud chips, applications, and services. That competition spurs innovation, drives down costs, and distributes the benefits of AI broadly across our economy.
Open weights also give customers greater control. As organizations invest in AI, they want to know that they will not become locked into a single provider or lose the knowledge and capabilities they build over time. Open weight models help provide that assurance by allowing organizations to control their own data, evaluate and adapt models to their own needs, and deploy them wherever their business requirements demand. And as organizations create value with AI, open weights allow them to own that value through self-improving models, specialized capabilities, and accumulated knowledge that drive American sovereignty and prosperity.
To be sure, open weights carry real and distinct risks. Once released, the weights are beyond the original developer's control, and modified versions are difficult to trace or reverse. But the right response to this risk is not to prohibit open weights. In a world where cybersecurity attackers use advanced AI, defenders need access to models with comparable capabilities so they can detect, simulate, and respond to emerging threats. Open models broaden defensive capability, increase transparency, and allow vulnerabilities to be discovered and remediated across many teams.
In fact, openness may be one of the most important paths to AI safety and security. Relying solely on closed models is not inherently safe: they can be breached, misused, or fail in ways that outsiders cannot detect. And concentrating advanced AI capabilities behind a small number of closed models compounds that risk. It results in a small number of single points of failure, weakens competition, and leaves critical technology in the hands of a few providers. Open weight models, on the other hand, allow a broad community of researchers and developers to examine their behavior, identify vulnerabilities, develop safeguards, and improve them over time. Just as open-source software demonstrated that transparency can be more secure than obscurity, AI safety may depend on giving more people the ability to test and strengthen the models on which society relies. It allows for rigorous benchmarking and evaluation, red teaming, and protections tied to real and demonstrated harms rather than assuming that closed systems are safer by default.
A strong AI ecosystem is not a foregone conclusion. Policymakers have an important opportunity to act. This includes expanding access to compute for startups and researchers, investing in shared training assets (datasets, tools, evaluation frameworks), and keeping the frontier plural by avoiding premature restrictions on open models that stifle competition or drive innovation overseas. These measures must also look at how strong application layers can expand sovereign use of AI across the economy.
In shaping this ecosystem, policymakers should be careful not to conflate legitimate modeldevelopment techniques with misappropriation. Distillation, or the practice of using one model's outputs to help train or improve another, is a widely used technique for model improvement, evaluation, and validation. It reflects a long tradition of learning from, building upon, and improving existing technologies, a tradition that has helped drive innovation since the rise of the open-source software movement. By contrast, unlawful efforts to extract value from closed models raise legitimate concerns. Those concerns should be addressed through targeted legal and commercial frameworks rather than sweeping restrictions on techniques that play an important role in AI innovation.
The age of AI can be one of prosperity. With the right choices, open weight AI can expand opportunity, strengthen competition, extend American technological leadership, mitigate risk, and ensure that the benefits of this extraordinary technology are shared broadly across our economy. That future is worth building, and the United States should lead in building it.
I believe the US has already fallen behind China in the development of LLM.
July 24, 2026
In the 1980s, early open-source software pioneers challenged the prevailing belief that software would advance only if companies kept tight control over their code. This movement pushed for a transparent ecosystem where developers around the world could study, modify, and improve software. Software developed by the open-source community now supports most of the internet and underlies systems used by the world’s largest technology companies, as well as the U.S. military and federal agencies conducting scientific research, cybersecurity, and other critical missions. Open source did more than lower the cost of software; it created a shared foundation of knowledge on which generations of American
engineers and entrepreneurs built their institutional sovereignty.
The United States now faces a similar choice with artificial intelligence. Our AI leadership will be judged not by one frontier AI model, but by whether the United States builds a strong, open ecosystem that diffuses into every sector. This is essential for creating opportunities for innovation and prosperity across the country. It requires expanding access to AI, encouraging competition, robust application layers, and giving Americans greater control over the technology they rely on. Open-weight models—AI models that anyone can download, inspect, modify, and run on their own infrastructure—are an important part of that foundation because they make advanced AI more accessible, adaptable, and widely available.
Open weights expand access to the AI economy. Startups, established businesses, universities, and public institutions can build on advanced models without training one from scratch or paying frontiermodel prices for every task. Open weights let every organization match the right model to the right job at the right cost, reserving frontier-scale capability for genuine frontier problems and running efficient, specialized models everywhere else. That discipline is what will make AI economically sustainable as its use scales into the billions of everyday tasks. America wins the AI era by diffusing it into the workflows of factories, hospitals, farms, classrooms, and main street businesses.
Open weights also strengthen competition and competition is what keeps the gains of AI broadly shared rather than concentrated in a few hands. By allowing many organizations to build, adapt, and deploy advanced models, open weights create rivalry not only among model developers but across cloud chips, applications, and services. That competition spurs innovation, drives down costs, and distributes the benefits of AI broadly across our economy.
Open weights also give customers greater control. As organizations invest in AI, they want to know that they will not become locked into a single provider or lose the knowledge and capabilities they build over time. Open weight models help provide that assurance by allowing organizations to control their own data, evaluate and adapt models to their own needs, and deploy them wherever their business requirements demand. And as organizations create value with AI, open weights allow them to own that value through self-improving models, specialized capabilities, and accumulated knowledge that drive American sovereignty and prosperity.
To be sure, open weights carry real and distinct risks. Once released, the weights are beyond the original developer's control, and modified versions are difficult to trace or reverse. But the right response to this risk is not to prohibit open weights. In a world where cybersecurity attackers use advanced AI, defenders need access to models with comparable capabilities so they can detect, simulate, and respond to emerging threats. Open models broaden defensive capability, increase transparency, and allow vulnerabilities to be discovered and remediated across many teams.
In fact, openness may be one of the most important paths to AI safety and security. Relying solely on closed models is not inherently safe: they can be breached, misused, or fail in ways that outsiders cannot detect. And concentrating advanced AI capabilities behind a small number of closed models compounds that risk. It results in a small number of single points of failure, weakens competition, and leaves critical technology in the hands of a few providers. Open weight models, on the other hand, allow a broad community of researchers and developers to examine their behavior, identify vulnerabilities, develop safeguards, and improve them over time. Just as open-source software demonstrated that transparency can be more secure than obscurity, AI safety may depend on giving more people the ability to test and strengthen the models on which society relies. It allows for rigorous benchmarking and evaluation, red teaming, and protections tied to real and demonstrated harms rather than assuming that closed systems are safer by default.
A strong AI ecosystem is not a foregone conclusion. Policymakers have an important opportunity to act. This includes expanding access to compute for startups and researchers, investing in shared training assets (datasets, tools, evaluation frameworks), and keeping the frontier plural by avoiding premature restrictions on open models that stifle competition or drive innovation overseas. These measures must also look at how strong application layers can expand sovereign use of AI across the economy.
In shaping this ecosystem, policymakers should be careful not to conflate legitimate modeldevelopment techniques with misappropriation. Distillation, or the practice of using one model's outputs to help train or improve another, is a widely used technique for model improvement, evaluation, and validation. It reflects a long tradition of learning from, building upon, and improving existing technologies, a tradition that has helped drive innovation since the rise of the open-source software movement. By contrast, unlawful efforts to extract value from closed models raise legitimate concerns. Those concerns should be addressed through targeted legal and commercial frameworks rather than sweeping restrictions on techniques that play an important role in AI innovation.
The age of AI can be one of prosperity. With the right choices, open weight AI can expand opportunity, strengthen competition, extend American technological leadership, mitigate risk, and ensure that the benefits of this extraordinary technology are shared broadly across our economy. That future is worth building, and the United States should lead in building it.
I believe the US has already fallen behind China in the development of LLM.
