AH
Amelia Hartley
General Partner
Leads the Pareto Constraint Method and sourcing. Background in robot learning and evaluation systems. Focus: data, simulation, and safety.
Team
AWSM VC is run by people who have shipped in robotics, ML, and systems — and who evaluate a company on the constraint it removes, not the demo it shows.
Leads the Pareto Constraint Method and sourcing. Background in robot learning and evaluation systems. Focus: data, simulation, and safety.
Leads technical diligence and portfolio support. Background in embedded systems and edge inference. Focus: manipulation, controls, and fleet ops.
Runs the constraint map and benchmark research. Background in simulation and sim-to-real. Focus: world models and synthetic data.
How we work
We won't push an oversized round before the evidence. We say no to most companies, and we say why. We show our work — in memos, in Field Notes, and with founders.
We reuse a small vocabulary because it keeps us honest: constraint, leverage, frontier, benchmark, critical path, power law, evidence. We avoid the rest.
Operating principles
A falsifiable result beats a polished story. We fund what can be measured.
Most companies get a pass — with the reason written down, not hidden.
Memos, Field Notes, and honest status labels. Aspirational is marked as such.
Few positions, real ownership, and reserves for the companies that prove out.
We read the code, run the benchmark, and pressure-test the metric.
The Frontier Stack is on tap from day one — not a logo on a website.
After we invest
We define the layer you govern and the test that proves you're better than the alternative.
Compute, hardware-test environments, and design-partner introductions across robot types.
Technical recruiting and warm introductions into the next institutional round.
For LPs
AWSM VC offers concentrated access to the enabling layers of Physical AI — the constraints every robot program depends on — with a repeatable selection method and no manufactured urgency.
We report what's real and label what's aspirational. If that's the kind of discipline you want in a first-time fund, we'd like to talk.
Founders solving one hard, performance-critical constraint — and LPs who want focused Physical AI exposure — start here.