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AI wants to write biology, and X is preparing to make its algorithm public—two very different systems on the verge of being rewritten. As Apple quietly taps Google’s Gemini to upgrade Siri, the pattern is hard to miss: the real shift isn’t new features, it’s who controls the underlying logic. Code, platforms, even genes are becoming editable—and the advantage now goes to whoever learns how to shape them fastest.
Video pick: The TikTok Paradox
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Six bullets of updates
🌐 Former Trump advisor joins Meta as president and vice chair, bringing decades of public and private sector experience to the exec team.
🎬 Streaming giant racks up 7 Golden Globes as it moves to acquire Warner Bros in a major industry shakeup .
🤝 Apple taps Google’s Gemini for future Siri upgrades in a multi-year, non-exclusive deal on AI features.
🌱 Carbon accounting gets a €55M boost as Diginex acquires Plan A, setting up a shakeup in Europe's ESG software race .
🛩️ French defense startup Harmattan AI hits $1.4B valuation after raising a $200M Series B led by Dassault Aviation, marking a major win for European defense tech.
🚕 Ads will reach 20 core inDrive markets after successful mid-2025 pilots, targeting new revenue streams.
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AI is coming for gene editing
Photo by National Cancer Institute on Unsplash
UK startup Basecamp Research just unveiled AI models that don’t just analyze genes—they suggest where to insert new ones. Think CRISPR (a gene-editing tool that cuts and pastes DNA) with an AI copilot proposing where to drop new functions, because hand-coding genomes doesn’t scale. Nvidia is backing the bet.
If it works, it marks a real shift: AI moves from predicting biological sequences to actively writing them. That could dramatically compress the design-build-test cycle for strain engineering, cell therapies, and industrial enzymes. The real moat won’t be bigger models—it’ll be proprietary biological datasets, automated wet labs, and the regulatory muscle to survive what comes next.
For Nvidia, it means more compute-hungry bio workloads. For founders, it signals rising demand for curated biological data, microfluidics, and serious quality assurance. Expect tighter biosecurity scrutiny and long clinical timelines; in this space, timelines—not demos—separate signal from noise.
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The TikTok Paradox
TikTok might just be the last new social media giant we’ll ever see. Every major platform before it—MySpace, Facebook, Instagram, Vine—was built on the back of groundbreaking tech innovations. But with no new waves to ride, social media as we know it is hitting a plateau. What comes next for our online behavior, and will platforms ever truly evolve again? Let’s break it down.
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X makes the algorithm public—and changes the game for everyone
Photo by Dima Pechurin on Unsplash
X says it will open-source its core algorithm in seven days, on January 17, 2026. The release will include the code that decides which posts get seen—and how ads are ranked—marking one of the most transparent moves ever made by a major social platform.
This won’t be a one-off. X plans to publish updated versions every four weeks, each accompanied by detailed developer notes. Officially, it’s a transparency push. In practice, it’s also an open invitation to a new SEO-for-social arms race, where creators, brands, and growth hackers dissect the algorithm in real time.
The upside is real: advertisers and researchers gain clearer auditability, regulators may view it favorably under DSA and antitrust scrutiny, and the platform could evolve faster through community feedback. But there are risks. Making the rules public lowers the barrier for spammers, shortens the shelf life of growth tactics, and could increase volatility as ranking logic shifts on a strict four-week cycle.