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<div style="display: none; max-height: 0px; overflow: hidden;">Some of California's biggest philanthropies, nonprofits, and labor groups are joining forces to push back on OpenAI's plan to restructure </div>
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<h1><strong>TLDR AI <span id="date">2025-09-09</span></strong></h1>
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<strong>Redis' fall releases for AI are here (Sponsor)</strong>
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Our latest releases for Redis for AI are full of core enhancements, new integrations, and dev tools to help you build fast, reliable apps at scale—especially AI ones. Here's <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fredis.io%2Fnew%2F%3Futm_source=tldr%26utm_medium=email%26utm_campaign=2025-09-ai_in_production-tldr-newsletter/3/010001992e9be83e-8049f737-2a4d-4224-86e5-5d1b00c42b6d-000000/1kcAZxhA8RDIlfIBcTmVNOEefBR6RbvUyUe_o1g70WI=422" rel="noopener noreferrer nofollow" target="_blank"><span>what's new</span></a>:
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<br>🧠 <strong>Redis for AI</strong>
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<li>Redis LangCache for fully managed semantic caching (Public preview)</li>
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<strong>OpenAI Executives Rattled by Campaigns to Derail For-Profit Restructuring (6 minute read)</strong>
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Some of California's biggest philanthropies, nonprofits, and labor groups are joining forces to push back on OpenAI's plan to restructure into a for-profit company. This has concerned OpenAI executives and resulted in discussions about moving out of the state. OpenAI's proposed plan is being investigated by Attorneys general in California and Delaware. The startup could potentially be sued for breaking nonprofit law and may be required to pay a settlement as a condition for the restructure.
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<strong>Databricks confirms new $100B valuation on $4B ARR (1 minute read)</strong>
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Databricks has confirmed another $1 billion raise at a $100 billion-plus valuation. The company will use the funds to invest in its database for AI agents. The round was led by Thrive and Insight Partners, who also co-led the previous $10 billion raise. Databricks has $4 billion in annual recurring revenue.
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<strong>Veo 3 and Veo 3 Fast – new pricing, new configurations, and better resolution (4 minute read)</strong>
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Google reduced Veo 3 pricing by nearly half to $0.40 per second and added support for vertical 9:16 videos and 1080p HD output.
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<strong>GROK is working on BACKGROUND THINKING! (1 minute read)</strong>
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Grok is working on a background thinking feature that appears to allow users to continue chatting while the assistant thinks. A screenshot of the toggle for the feature is available.
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<h1><strong>Deep Dives & Analysis</strong></h1>
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<strong>RL-as-a-Service will outcompete AGI companies (and that's good) (6 minute read)</strong>
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RLaaS (Reinforcement Learning-as-a-Service) has proven performance, lower costs, is more excludable, and is safer. AI companies pivoting away from pursuing AGI towards RLaaS is good news because it promises a switch to a safer mode of AI development. While RLaaS is not without risks, the transition to an RLaaS model would reduce AI risk. The concatenation of specialized models may create or assist general intelligences.
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<strong>Cognition: The Devin is in the Details (22 minute read)</strong>
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Shawn Wang, founder of Smol AI, is joining Cognition, the makers of the Devin AI software engineer. The vast majority of Wang's equity/investor capital at Smol AI will be rolling over their investment to Cognition as part of the deal. Wang will continue to work on AI Engineer and Latent Space independently of Cognition. This post explains in detail Wang's reasons for betting on Cognition.
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<strong>Why do we take LLMs seriously as a potential source of biorisk? (10 minute read)</strong>
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Anthropic details why it implemented ASL-3 safety protections against biological weapons development, noting Claude now exceeds expert performance on virology evaluations, and controlled trials showed significant uplift with Opus 4 for acquiring bioweapons compared to using just the open internet. It's obviously a low-probability risk, but the impact is so high — just one infraction could mean thousands of deaths — that it still requires serious evaluation and restriction.
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Effortlessly deploy agentic workflows and generative AI with the Inference Cloud powered by Qualcomm. Designed for today's leading models, it offers exceptional performance and seamless scalability at a fraction of the cost.<p></p><p><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flink.cirrascale.com%2F3VwTBym/1/010001992e9be83e-8049f737-2a4d-4224-86e5-5d1b00c42b6d-000000/wP76uZ8R6-MJ_yZuSssWuGefTu09VWtebhPKFyskcMM=422" rel="noopener noreferrer nofollow" target="_blank"><span>Learn more </span></a>or <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flink.cirrascale.com%2FInferenceCloudQualcomm/1/010001992e9be83e-8049f737-2a4d-4224-86e5-5d1b00c42b6d-000000/WfBt_14Ta2PT9urmhOA85vKe_3iWTONaFBv3giuqTd4=422" rel="noopener noreferrer nofollow" target="_blank"><span>sign up </span></a>now!
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<strong>I made AI coding agents more efficient (16 minute read)</strong>
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AI coding agents are great, but they're too wasteful, they don't hold the context of the database, they only have a surface-level understanding, and they're too susceptible to context rot. These problems could be solved if agents had a semantic understanding of all functions and context about how functions are connected to other parts of code. This article looks at practical ways to make coding agents more efficient.
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<strong>Meta's Set Block Decoding (19 minute read)</strong>
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Set Block Decoding enables parallel sampling of multiple future tokens by combining next-token and masked token prediction. This reduces decoding passes up to 5x without sacrificing accuracy.
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<div style="text-align: center;"><span style="font-size: 36px;">🎁</span></div></div>
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fbenanderson.work%2Fblog%2Fdont-build-rl-env-startup%2F%3Futm_source=tldrai/1/010001992e9be83e-8049f737-2a4d-4224-86e5-5d1b00c42b6d-000000/UNEXnUpZEXPlM--bf7ImHMTDMLXK6GP1ifCNEK_q3FY=422">
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<strong>Don't Build an RL Environment Startup (7 minute read)</strong>
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Reinforcement Learning environments are sandboxes for AI models to learn from. They keep track of an internal state, prompt AIs to take actions to complete a task, and assign scores based on the outcome. More and more startups are offering increasingly challenging environments under the assumption that machine learning is solved and the only remaining work is to provide models with simulations to learn from. Frontier AI labs pay a lot of money for these environments, but the supply side is set to become increasingly crowded - it likely won't be a long-lived business.
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<strong>Judge skewers $1.5B Anthropic settlement with authors in pirated books case over AI training (3 minute read)</strong>
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A federal judge expressed serious concerns about Anthropic's proposed settlement over the use of 500,000 pirated books to train an internal version of Claude. He questioned how the settlement money would be distributed: “I have an uneasy feeling about hangers on with all this money on the table.”
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<h1><strong>Quick Links</strong></h1>
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<strong>Nebius signs $17.4B AI infrastructure deal with Microsoft (2 minute read)</strong>
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Nebius will provide Nvidia GPUs and AI cloud services to Microsoft out of their new New Jersey data center over 5 years.
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<strong>Perplexity for Government (2 minute read)</strong>
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Perplexity introduced a secure, zero-data-usage AI platform for U.S. government agencies, alongside a $0.25-per-user Enterprise Pro offering tailored to public sector needs.
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<strong>Anthropic Backs California AI Safety Law (3 minute read)</strong>
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Anthropic endorses California's SB 53, a bill mandating transparency in safety practices for frontier AI systems.
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<strong>Cognition raises over $400M at $10.2B valuation (3 minute read)</strong>
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Cognition raised over $400M at a $10.2B valuation, in a funding round led by Founders Fund with participation from Bain Capital Ventures and others.
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