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Continual learning, long-term reasoning, and memory are the key unsolved gaps; DeepMind pursues both scaling and architectural innovation, with AGI likely arriving around 2030 — implying deep-tech founders must plan for AGI mid-journey.

Agents require continual learning and long-term memory to become 'fire and forget'; current systems are duct-taped together but will cross into genuine productivity within 6-12 months as reasoning and tool use mature.

Isomorphic Labs uses AI to design drug compounds in silico after AlphaFold solves protein structures, replacing 99% of wet-lab exploration with simulation and potentially reducing discovery timelines from a decade to months or weeks.

With a 2030 AGI estimate, 10-year deep-tech journeys will intersect AGI emergence; founders should build specialized tools (like AlphaFold) that general AGI systems will call via tool use, not try to replicate AGI capabilities.

Accurate AI simulators for complex emergent systems like biology, economics, and weather will allow repeated controlled experiments impossible in the real world, potentially establishing new rigorous sciences for domains currently limited to theory.

Building multimodal from scratch (vs. bolting on) yields superior intuitive physics and physical-context understanding, enabling robotics (Gemini Robotics), Waymo, and wearable assistants that process audio-visual feeds locally.

Isomorphic Labs targets full virtual cell via staged approach (nucleus first); nanometer-resolution live-cell imaging would convert biology into a vision problem solvable by current multimodal models, potentially shortcutting the 10-year path.

Scientific domains with massive combinatorial spaces, clear objective functions, and simulators for synthetic data are ripe for AI breakthroughs; materials science and mathematics are next frontiers after protein folding.