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Mozilla's Inaugural 'State of Open Source AI' Report Is Here
2 months ago
- Open source AI models have nearly closed the performance gap with proprietary systems like ChatGPT and Claude, with only a 3% difference, while costs have dropped up to 50x in three years.
- Open models power about one-third of real-world AI usage but capture only 4% of revenue, indicating value isn't flowing back into the open ecosystem.
- China and East Asia lead in open source AI adoption at 89%, treating it as a national strategy, while many countries are enacting new AI strategies and restricting foreign data processing.
- 79% of developers use open models, but only 51% have deployed them in production, highlighting a gap in infrastructure and tooling rather than model quality.
- The key competitive layer is now the 'agentic harness'—software controlling what AI can see, remember, and do—with few guardrails and high user consent fatigue.
- Mozilla's report emphasizes the need to invest in infrastructure, tooling, and governance to ensure open AI can scale in the public interest.
How we’ll fight the platform war against Big AI - Anil Dash
2 months ago
- Big AI companies are vulnerable in the early AI market, and users/developers can leverage platform strategy to shift power away from them.
- Key tactic: Push for disintermediation so users access AI through open, community-controlled interfaces rather than Big AI platforms.
- Key tactic: Enable seamless switching between AI providers to keep the market dynamic and prevent vendor lock-in.
- Key tactic: Undermine Big AI economically by promoting free, open-source models that are nearly as capable as commercial ones.
- Key tactic: Channel public anger at AI harms into building ethical alternatives that respect user values and consent.
- Success requires coordination among developers and non-technical users to support open ecosystems and resist enshittification.
OpenAI’s lead is dwindling fast
2 months ago
- OpenAI's competitive advantage is diminishing because many companies are building similar AI products, making it hard to maintain a unique edge.
- Microsoft, OpenAI's largest backer, is increasingly distancing itself, including exploring partnerships with Chinese AI firms like DeepSeek, which is a strong negative signal.
- OpenAI is burning cash faster than expected, with losses multiplying significantly each year, which is unsustainable.
- Regulatory moves in Washington could impact competitors like Anthropic, potentially benefiting OpenAI, but if Anthropic survives, it could challenge OpenAI's position.
- The rise of open-source AI models and cheaper alternatives, such as DeepSeek, is driving tech companies to reconsider costly proprietary solutions.
- Google's diverse revenue streams from multiple billion-user products give it an advantage over AI startups like OpenAI, which face financial pressures.
- As AI models become more similar in quality, customers will increasingly choose the lowest-cost option, threatening OpenAI's market position.
The arguments against open source AI are bad
2 months ago
- Open source software is foundational for all commercial software, including AI models.
- Attempts to suppress open source AI are historically difficult and often backfire, as seen with encryption export controls.
- Open source AI development is driven by various actors beyond China, including chip makers, startups, enterprises, and big companies.
- Framing AI as a race is misleading; it's more about adapting to a transformational technology where free models can be beneficial.
- Arguments that Chinese AI models are 'dumping' or will spread propaganda or backdoors are flawed; open source allows inspection and modification.
- Open source AI is inevitable and powerful, making efforts to control it largely futile.
Kimi K3: The open-weights escalation
2 months ago
- Moonshot AI released Kimi K3, a 2.8T parameter MoE model, with open weights scheduled for release on July 27th.
- Kimi K3 is the strongest open model ever, ranking high on multiple benchmarks and competing closely with top closed models like Claude Fable and GPT-5.6 Sol.
- The release reduces the performance gap between open/closed and U.S./Chinese models to an estimated 3-5 months.
- China's commitment to open-source AI was reinforced by Xi Jinping's keynote at WAIC, indicating high risk tolerance and a strategy to boost AI adoption.
- Open-weight models are economically decelerationist for frontier labs, reducing profit margins and investment but accelerating AI diffusion and customization across industries.
- Chinese labs, like Moonshot AI, demonstrate higher capital efficiency, achieving strong results with fewer resources due to optimized training and architectural innovations like Kimi Delta Attention.
- The open-model ecosystem is expanding, with Alibaba announcing Qwen 3.8 with open weights, potentially pushing Google lower on leaderboards.
- Policy debates in the U.S. about restricting open-weight models may backfire, creating asymmetrical security risks and hindering AI diffusion and safety.
- A balanced open-closed model dynamic is crucial for mitigating risks, as open models will inevitably advance and cross capability thresholds regardless of regulation.
- Kimi K3 marks a watershed moment, making frontier open-weight models a reality and shifting the AI landscape from a unipolar, U.S.-dominated sphere to a more competitive global ecosystem.
Big Tech's top executives warn enterprises are giving away too much to AI labs
2 months ago
- Satya Nadella warns enterprises are 'paying twice' for AI by purchasing tokens and inadvertently handing proprietary data to AI labs, enabling potential future competition.
- Executives like Alex Karp and David Sacks criticize frontier AI labs for extracting valuable business data while charging high prices, advocating for more control over data and infrastructure.
- Open-weight models are gaining traction as alternatives to closed-source AI, offering cost savings, transparency, and reducing vendor lock-in, with U.S. government actions prompting diversification.
- AI industry news includes xAI suing a user over deepfake misuse, OpenAI employees funding a pro-regulation PAC, and Google DeepMind researcher resigning over military deals.
- CEOs of major AI labs (Hassabis, Altman, Amodei) propose regulatory frameworks for frontier AI, though approaches vary, with concerns about entrenching incumbents over startups.
- Dario Amodei donated $1 million to a PAC pushing AI regulation, highlighting the growing political influence and public backlash against AI's rapid expansion and costs.
Satya Nadella has issued a warning to companies using AI
2 months ago
- Microsoft CEO Satya Nadella warns that companies using proprietary AI models pay twice: with money and by handing over valuable proprietary business data.
- Nadella argues that enterprises are unknowingly training AI models with sensitive business knowledge, which competitors could never buy, potentially turning model makers into competitors.
- He criticizes the hypocrisy of AI labs freely using public data for training while restricting others from studying or 'distilling' their models to create cheaper alternatives.
- Nadella suggests companies retain ownership of their data and build proprietary learning environments in the cloud, along with 'orchestration layers' to switch between AI models easily.
- Enterprises are increasingly shifting to on-premise open source AI models for cost savings, control, and to avoid data leakage, a trend supported by companies like Solo.io, Vercel, and OpenRouter.
6 months to live for open models
2 months ago
- The most serious test to date for open source AI viability is happening now due to policy actions that could marginalize open models.
- White House discussions may lead to new executive orders impacting open models, potentially starting with Chinese-origin models and government uses.
- Open models lack a central economic champion to defend against regulatory actions, unlike closed models with strong lobbying.
- Likely policy action includes banning or delaying open-weights models above capability levels like GPT 5.5 within the next 6 months.
- Two key policy discussions affecting open models are distillation (regulatory capture by companies like Anthropic) and frontier capabilities.
- Anthropic's campaign against Chinese models is seen as regulatory capture, aiming to ban competitors and secure economic advantages.
- Distillation debates focus on risks like Chinese labs distilling cybersecurity capabilities, but APIs are not magically secure and have vulnerabilities.
- A ban on open models could demolish the emerging open model economy in the US, including inference and fine-tuning companies.
- Handling frontier open-weight models like Mythos requires careful policy; a flat ban may be ineffective if not globally enforced and could isolate the US.
- Open models increase safety through broad access and understanding; stopping open-source progress requires global agreement, which is currently lacking.
- Short-term solutions include a US company releasing a capable open model to shift focus from China or building a coalition to lobby for safe open model rollout.
Anthropic's Method to Losing Goodwill in a Few Easy Steps
3 months ago
- Anthropic's Claude subscription and API face issues including enshittification, vendor lock-in, consumer malpractice, and price gouging, while open-source models become more competitive.
- Claude's API is notoriously unreliable, and Claude subscriptions force users to rely on Anthropic's servers, with no compatibility with third-party harnesses without extra costs.
- Anthropic introduced 'extra usage' billing for third-party tools and split subscription billing into separate pools for first-party and third-party usage, significantly increasing costs for heavy agent users.
- The author criticizes 'vibecoding' and agent-driven development for causing skill degradation, advocating instead for agent-assisted development using AI as an autocomplete tool.
- Open-source models like Qwen, GLM, and Deepseek offer viable alternatives to proprietary models, with AI gateways enabling flexible, cost-effective, and private model usage.
- The article concludes by condemning Anthropic's unethical practices, such as locking customers into closed systems and changing terms without notice, and encourages moving to open-source alternatives.
Leanstral 1.5: Proof Abundance for All
3 months ago
- Leanstral 1.5 is a free Apache-2.0 licensed model with 6B active parameters, offering a significant performance upgrade in formal verification.
- It achieves state-of-the-art results on benchmarks: 100% on miniF2F, 587/672 on PutnamBench, 87% on FATE-H, and 34% on FATE-X.
- The model undergoes a three-stage training process: mid-training, supervised fine-tuning, and reinforcement learning with CISPO, using multiturn and code agent environments.
- It demonstrates strong test-time scaling on PutnamBench, solving more problems as token budget increases, from 44 at 50k tokens to 587 at 4M tokens.
- Leanstral 1.5 excels in code verification, proving time complexity for AVL trees and uncovering 5 previously unknown bugs across 57 repositories tested.
- The model is fully open-sourced and available via Hugging Face and a free API, with recommendations for setup using Mistral Vibe and Lean LSP MCP.

open source ai

Mozilla's Inaugural 'State of Open Source AI' Report Is Here

How we’ll fight the platform war against Big AI - Anil Dash

OpenAI’s lead is dwindling fast

The arguments against open source AI are bad

Kimi K3: The open-weights escalation

Big Tech's top executives warn enterprises are giving away too much to AI labs

Satya Nadella has issued a warning to companies using AI

6 months to live for open models

Anthropic's Method to Losing Goodwill in a Few Easy Steps

Leanstral 1.5: Proof Abundance for All