Robotics & physical AI
Abdul Al Ali, Venture Investor at Decasonic, Chicago
Deployment is the bottleneck. I back the teams closing the gap from pilot to scale, and the benchmarks that show what actually works in the real world.
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there are parallels that we observe in software AI that will continue to emerge in robotics.
we @decasonic are actively aiming to partner with founders in accelerating the deployment of physical AI at scale.
increasingly, there's a clear-trend of companies emerging with the dedicated goal of selling real world deployment data (logs and traces - "data") to frontier labs.
the physical AI benchmarks and evals market will note a similar trajectory to the software AI benchmarks (from generalized tasks to economic tasks).
one of the main questions in the advancement of the robotics (and wider physical AI landscape) is whether the existing capital funding the current software digital build out will be able to support the physical one.
Investing at Decasonic
- Robocurve The trusted, neutral layer for physical AI benchmarks at scale.
- Shiraz AI Robots that learn on the job, starting with manufacturing.
- PrismaX Robot training data at scale, where humans, robots, and data come together.
Writing
- The State of Physical AI Aug 12, 2026 Physical AI moves to deployment: data, benchmarks, capital and swarms.
- The Web3 x Physical AI Market Map Sep 19, 2025
- Where Value Accrues When Intelligence Is Abundant: Decasonic's Q4 2026 Thesis Oct 5, 2026 Value accrues to differentiated experiences, digital and physical.
- Where We Are Investing Jul 31, 2026 Decasonic's request for startups, laying out sectors of interest: AI interfaces and agents, physical AI, AI applications, networks, AI x fintech and consumer AI.
Market maps
Speaking
© 2026 Abdul Al Ali