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In traditional startups, early departures are often survivable because value accrues to product, customers, or revenue. In unicorn-at-birth AI startups, valuation is frequently anchored to people. When those people leave — especially co-founders — the asset itself is diminished.

Europe’s data center construction market is projected to grow from $16.3B in 2024 to $27.9B by 2033, driven by cloud adoption, AI workloads, and sustainability mandates. As highlighted in recent TheMarketAI coverage of SWI Group and Nebius, Europe’s buildout is increasingly defined by energy efficiency, sovereign infrastructure, and long-term resilience rather than pure scale.

A new “unicorn at birth” has entered the AI race. Unconventional AI, a two-month-old startup founded by former Databricks AI chief Naveen Rao, has raised $475M at a $4.5B valuation in a seed round. Backed by a16z, Lightspeed, Lux, DCVC, and Jeff Bezos, the company is targeting one of AI’s hardest problems: building a more energy-efficient computer for next-generation AI workloads as power and compute constraints intensify.

Cisco has invested in World Labs, the spatial-intelligence startup founded by Dr. Fei-Fei Li, underscoring a major shift in AI: from language-based models to systems that understand and operate within 3D environments. World Labs’ Large World Models aim to move AI from “understanding words to understanding worlds.” As spatial and multimodal intelligence emerges, it may define AI’s next decade far more than text-only LLMs.

Thinking Machines Labs is reportedly seeking a $50B valuation, just months after raising at a $12B mark. But with co-founder departures and a first product—Tinker—that feels incremental rather than transformative, the jump raises questions. As we’ve covered before, the company still looks more like a research lab than a revenue engine. The widening gap between ambition and output makes this valuation hard to justify.

The global Document AI market is projected to reach $27.62B by 2030, growing at a 13.5% CAGR, according to MarketsandMarkets™. The surge reflects a shift from large, general-purpose AI models to domain-tuned, task-specific agents that can interpret and act on documents with precision and compliance. As enterprises favor smaller, controllable “micro-agents,” the real test will be reliability and whether they can scale without hallucination.