Deep Future

If a system still needs a brittle harness, is it AGI? We dig into cybersecurity as an adversarial game, personal agents that become extensions of the self, and why the model layer wants to be a commodity platform — not a permanent monopoly.

Pedro Domingos is an OG of AI — researcher, author of The Master Algorithm and the satire 2040. Moonshot’s Kimi K3 model sparks a conversation about distillation, copied capability, open AI, and the white-hat advantage.

Founders want investing decisions to be rational, legible and fast. Venture capital is none of those things. A great company can be wrong for a fund, a perfect pitch can arrive at the wrong moment and a polite maybe can consume months that should have gone into finding ten more investors.

In the final part of our conversation, Jamie Wood turns the questions around. We talk about what would stop me from investing in AUTONOMIC, why fund fit matters, what warm introductions really buy and why the only durable fundraising strategy is to maximize your shots on goal.

A promising technology is not a company until the hard parts reinforce one another. Product, distribution, evidence, data and timing all have to spin together; if one link keeps breaking, the founder spends eternity pushing the wheel uphill.

Jamie Wood invited the teardown, so in part four we do it properly. We pressure-test AUTONOMIC's product experience, data advantage, wearable strategy, sales motion and scientific proof—then ask what has to be true for all of it to become a compounding brain-data flywheel.

College students are supposed to arrive ready to learn, manage themselves and make consequential decisions. In reality, many are walking into adulthood with battered attention, uneven executive function and support systems built to react after things go wrong.

Jamie Wood thinks that is a product problem. In part three, we look at AUTONOMIC's 18-to-24-year-old beachhead, early working-memory results, the gap between a buyer and a user, and how an AI coach might make useful brain science legible without turning it into another institutional lecture.

Most brain-training products begin by demanding patience from the exact brain that is already distracted, stressed or exhausted. Jamie Wood wants the opposite: ask as little as possible, find the highest-leverage behavior and deliver something useful fast.

In part two, Jamie walks me through AUTONOMIC as if it were a personal trainer for the brain. We unpack the questions, recommendations, feedback loops and social proof behind a product trying to make better sleep, focus, mood, energy and stress regulation feel less like homework.

Changing jobs is easy. Changing the patterns you carry into the next job is harder. Jamie Wood learned that after climbing the corporate-finance ladder, burning out and discovering that a new environment did not automatically produce a new brain.

Jamie is the founder of AUTONOMIC, where she has been turning neuroscience into practical tools for attention, energy, stress, mood and sleep. In part one of our conversation, we get into the unlikely path from finance to brain science — and the client result that convinced her the method could become a self-directed product instead of another coaching dependency.

I keep a circle of heretical physicists on purpose. Not because every weird idea is true, but because some working lab results mater more than a tidy explanation.

In part five, Danielle and I get into evidence versus first-principles gatekeeping, YouTube tinkerers making old experiments visible again, and how AI changes who can connect distant scientific ideas.

Some founders hide in stealth but Danielle Fong has mostly done the opposite: publish the work, take the hits, and let the right people find you.

In part four, we unpack the pluses and minuses of being public, raising from individuals, and building a company where the technical story is also the recruiting story.

AI did not create the data-center power problem, but it made the problem impossible to ignore. Danielle Fong has been building toward advanced high-density power and the market is finally running toward her.

In part three, we get into customers, constraints, roadmap choices, and the personal decision to leave a whole class of academic work on the table.