Good morning, AI enthusiast.

AI search startup Perplexity is making a stunning move, placing a $34.5 billion bid to acquire Google's Chrome browser. The audacious offer comes as Google faces the potential of a forced sale following a major antitrust ruling.

This isn't just a battle over an application; it's a direct challenge for control over the web's fundamental infrastructure. But can a startup, even with VC backing, successfully pry away one of Big Tech's most valuable assets and reshape how we all access the internet?

In today’s AI recap:

• Perplexity’s surprise bid for Google Chrome

• Anthropic's 1M token memory upgrade

• Microsoft’s Meta poaching playbook

• The AI industry’s reality check

Perplexity's Chrome Cannonball

The Recap: AI search startup Perplexity has made an unsolicited $34.5 billion offer to buy Google’s Chrome browser. The audacious bid is a direct result of Google’s landmark antitrust loss, which could force the tech giant to sell its most valuable digital real estate.

Unpacked:

• The offer strategically positions Perplexity as a solution just as a federal judge decides on remedies for Google's monopoly, with the DOJ pushing for a Chrome divestiture.

• This is a bold play from the startup, with the offer price being nearly double its valuation and backed by promises of financing from venture capital funds.

• Google is fiercely resisting, having called the idea of a forced sale an unprecedented proposal that would harm consumers and security.

Bottom line: This move signals that ambitious AI firms are now targeting the web's core infrastructure, not just the applications built upon it. The outcome of this saga could reshape how billions of users access the internet and who controls the data powering the future of AI.

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Anthropic's Million-Token Memory

The Recap: Anthropic is escalating the AI race by upgrading its Claude Sonnet 4 model with a massive one-million-token context window for enterprise API customers, enabling it to analyze entire codebases or hundreds of documents in a single prompt.

Unpacked:

• This represents a 5x increase from the previous limit, allowing the model to process up to 2,500 pages of text or analyze codebases with over 75,000 lines at once.

• The move puts pressure on competitors like OpenAI, whose most powerful models currently offer a smaller 400,000 token window, reinforcing Anthropic's focus on its API-first strategy.

• A larger memory is essential for more complex, agentic tasks where the AI must track its steps and maintain context to solve multi-step problems.

Bottom line: This upgrade empowers developers to build applications that can understand the full scope of a project, not just small pieces. For businesses, this translates to more accurate AI-assisted coding and the foundation for more capable autonomous systems.

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Microsoft's Meta Poaching Playbook

The Recap: Internal documents reveal Microsoft has a targeted strategy to poach top AI engineers from Meta, complete with a "most-wanted" list and multimillion-dollar pay packages to win the escalating talent war.

Unpacked:

• The competition is fierce, with Meta reportedly offering pay packages as high as $250 million to top AI researchers.

• Microsoft is getting specific, creating a most-wanted list of Meta engineers and researchers shared among its AI hiring managers.

• To move quickly, Microsoft has a new process that flags candidates as "critical AI talent," enabling a top-tier offer within 24 hours.

Bottom line: This aggressive poaching shows that access to elite AI talent is viewed as the single most critical asset for big tech dominance. For professionals, it signals that specialized AI skills command unprecedented market value and leverage.

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The AI Reality Check

The Recap: A wave of skepticism suggests AI development may be hitting a plateau after an underwhelming GPT-5 launch. The era of massive gains from simply scaling up models appears to be over, forcing a significant strategy shift across the industry.

Unpacked:

• The recent hype stemmed from a 2020 paper that proposed "scaling laws," suggesting that just making models bigger would lead to exponential performance jumps.

• With those gains slowing, the industry is pivoting to "post-training improvements," acting more like mechanics souping up existing models rather than building entirely new ones.

• This new approach is yielding more specific, narrow updates rather than broad leaps, leading experts to question if the industry is seeing diminishing returns on its efforts.

Bottom line: Future AI progress will likely feel more like steady software updates than groundbreaking leaps in capability. This shifts the focus from anticipating superintelligence to mastering the powerful, but not magical, tools available today.

The Shortlist

China urged local companies to avoid using Nvidia’s H20 AI chips, signaling a significant push towards domestic hardware and creating headwinds for Nvidia's attempts to retain market share in the country.

Reddit blocked the Internet Archive’s Wayback Machine from indexing its content, citing that AI companies were violating its policies by scraping the archives for training data instead of Reddit directly.

Yomiuri Shimbun sued Perplexity for nearly $15 million in damages, marking the first major copyright lawsuit by a Japanese publisher against an AI firm and alleging the scraping of over 119,000 articles.

Mayo Clinic deployed an NVIDIA DGX SuperPOD powered by Blackwell systems to accelerate the development of foundational AI models for drug discovery, pathomics, and precision medicine.

Signing off,

David, Lucas, Mitchell — The Recap editorial team