# Google’s No-Code Android Push, DeepMind’s Research Expansion, and Diverging Views on AI at Work *By AI News Digest • May 26, 2026* Google reported strong early traction for native Android app building in AI Studio. DeepMind paired a broader research-automation agenda with a reminder that AGI remains distant, while commentary on AI at work split between new operating models and skepticism about real-world payoff. ## AI moved closer to real workflows The common thread today was AI moving out of demo mode and further into usable workflows: building mobile apps, assisting scientific research, and changing how teams think about software. What stayed unsettled was the payoff. ### Google’s Android app builder is showing unusually broad early pull Google said people can now build native Android apps directly in Google AI Studio for free, with no coding required, and Logan Kilpatrick said more than 250,000 apps were created since launch last week. He added that likely more than 99% of those creators had never built an Android app before, and pointed users to [ai.studio/build](https://ai.studio/build) as the entry point for reaching Android’s 3 billion active users [^1][^2]. *Why it matters:* The notable signal here is not just another AI coding interface. Google is positioning app creation as a mass-market workflow, with early usage skewing heavily toward first-time builders [^1][^2]. ### DeepMind is widening its research agenda while insisting AGI is still far away > "Today’s systems are nowhere near [AGI]." [^3] Hassabis drew a distinction between solving well-defined problems with large amounts of compute and genuine invention of new objects, dimensions, or problems [^3]. In parallel, DeepMind described a Gemini-based Co-scientist for hypothesis generation, data analysis, and literature summarization; earlier related systems that improved matrix multiplication and computer science algorithms; experiments with agent roles in a game environment; an automated materials lab in London; and a multi-model effort across the drug-discovery pipeline that is now in pre-clinical work [^4]. *Why it matters:* This is a clear read on frontier-lab direction. DeepMind is pushing AI into research assistance, simulation, and lab automation while still arguing that current systems have not crossed into open-ended invention [^3][^4]. ## The work narrative is splitting ### AI-native operating models are getting clearer, but ROI skepticism is not going away Dan Shipper argued that work increasingly happens inside agents such as Codex or Claude Code, with SaaS tools accessed through the agent’s in-app browser so the model keeps full context. He also said every automation still needs a human, and that the more durable organizational pattern may be a shared company “super-agent” maintained by a forward-deployed engineer rather than one personal agent per employee [^5]. Chollet made a similar framing shift, arguing that AI should be seen less as a productivity booster for old workflows and more as a tool for doing new things in new ways, while LeCun said AI should amplify human intelligence and turn more engineers into managers of AI-augmented virtual teams [^6][^7]. Marcus pushed in the opposite direction. He warned that coding models can generate “slop” that hurts large companies, and separately said the AI bubble could pop if enough companies report the same outcome [^8][^9]. *Why it matters:* The conversation is moving past whether AI will affect work. The sharper questions now are how much human oversight remains necessary, what new org charts look like, and whether current deployments are translating into measurable gains [^5][^9]. ## One research paper worth tracking ### Delta Attention Residuals targets a common deep-model failure mode A paper highlighted in r/MachineLearning introduced Delta Attention Residuals as a drop-in alternative to standard residual connections, routing across layer deltas rather than cumulative hidden states to avoid routing collapse in deep models [^10]. The authors reported up to 8.2% lower validation perplexity at 7.6B parameters, gains across 220M to 7.6B scale, and the ability to retrofit pretrained models with at most 0.01% parameter overhead; paper and code are public [^10]. *Why it matters:* If replicated, this is the kind of architectural tweak that matters because it promises measurable gains without a large parameter tax or a full stack rewrite [^10]. ## Also notable - Elon Musk said xAI plans to open source its 0.5T model toward the end of the year, adding that it should still be useful [^11]. --- ### Sources [^1]: [𝕏 post by @OfficialLoganK](https://x.com/OfficialLoganK/status/2058997700218294564) [^2]: [𝕏 post by @OfficialLoganK](https://x.com/OfficialLoganK/status/2058998241677721916) [^3]: [𝕏 post by @ValerioCapraro](https://x.com/ValerioCapraro/status/2058870879807852659) [^4]: [DeepMind’s Insane AI Breakthroughs With CEO Demis Hassabis](https://www.youtube.com/watch?v=huAwz_BR8WM) [^5]: [𝕏 post by @lennysan](https://x.com/lennysan/status/2058914803360600238) [^6]: [𝕏 post by @fchollet](https://x.com/fchollet/status/2058982905368773040) [^7]: [Yann LeCun 2026 NYU Tandon Commencement Speech](https://www.youtube.com/watch?v=pFscM-Z4rCE) [^8]: [𝕏 post by @GaryMarcus](https://x.com/GaryMarcus/status/2058673424449597879) [^9]: [𝕏 post by @GaryMarcus](https://x.com/GaryMarcus/status/2059058176075420111) [^10]: [r/MachineLearning post by u/Mediocre-Ad5059](https://www.reddit.com/r/MachineLearning/comments/1tndn5b/) [^11]: [𝕏 post by @elonmusk](https://x.com/elonmusk/status/2058796067592736866)