Uber burned through its entire 2026 AI budget by mid-April. Four months. Gone. In December, Uber rolled out Claude Code to roughly 5,000 engineers. Internal adoption took off so fast that the company set up a leaderboard ranking teams by total AI tool usage. Per-engineer API
ENTERPRISE AI
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NVIDIA accelerates bounding box detection 10x by removing mandatory token prediction
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🚨 NVIDIA just pulled off something crazy: making bounding box detection 10x faster by ripping out the exact step the entire industry assumed was mandatory ↓
— Charly Wargnier (@DataChaz) 1 juin 2026
Every VLM grounding model treats boxes like sentences, predicting them token by token. It’s inherently slow.
Enter… pic.twitter.com/OE7fxZFF4VNVIDIA just pulled off something crazy: making bounding box detection 10x faster by ripping out the exact step the entire industry assumed was mandatory ↓ Every VLM grounding model treats boxes like sentences, predicting them token by token. It’s inherently slow. Enter
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AI Efficiency Trap: Faster Tasks Could Cost Companies the Future
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The AI Efficiency Trap: Why Doing Things Faster Could Cost Companies The Future Many companies are using #AI to #automate #tasks, cut costs and speed up existing #workflows, but that approach risks missing the much bigger #opportunity. The real competitive #advantage will come
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Who will own the artificial intelligence of data centers?
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75 billion euros to build data centers in France. Very good. But a question remains: who will own the artificial intelligence that will run inside? A data center is just an infrastructure. The value is not in the walls, it is in the models,
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Large scale pretrained models remain important in AI
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This was right five years ago, and still is: “Large scale pretrained models are certainly likely to figure prominently in artificial intelligence for the near future, and play an important role in commercial AI for some time to come. The results that have been achieved with them
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Benchmarks are boring; enterprises need shipping LLM capabilities now.
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Wall Street is watching frontier model benchmark porn while enterprises quietly die waiting for capabilities that actually ship. MMLU and HLE were very useful for about a nano-second. But now they are just boring. We don’t need to know how well an LLM can do on a test. We need
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Generational upgrade of evals/analytics into continual learning platforms
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every evals/analytics startup is going through a onetime generational upgrade into a continual learning platform in 2026 many will fail but as always the tasteful ones win
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AI Agents: Orchestrating Multiple LLMs for Enhanced Capabilities
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AI Agents ≠ single LLM They’re systems of models • General LLMs → reasoning
• Domain LLMs → expertise
• RAG → real-time data
• Tools → actions & automation
• Open-source → control & privacy Real power = orchestration of multiple LLMs Via Giuliano Liguori -
AI agents process manufacturing data at scale, speed, and compliance.
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Agents process manufacturing data at scale and speed no human could match while maintaining regulatory compliance.
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Digital Workers: Contextual AI Orchestration, Not Basic Automation
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Digital workers contextually understand specific operating procedures and regulatory guidance, unlike basic automation of the past. The human role shifts from data bridge to orchestrator of AI agent fleets. Partner content with Adlib. #adlib_iiot pic.twitter.com/7SQom7t9YS
— Lucian Fogoros (@fogoros) 31 mai 2026Digital workers contextually understand specific operating procedures and regulatory guidance, unlike basic automation of the past. The human role shifts from data bridge to orchestrator of AI agent fleets. Partner content with Adlib. #adlib_iiot