Dry Run

Frontier technology is scaling rapidly. Understanding lags far behind.

Dry Run began with a narrow focus on innovation in fintech. Since then, we've been drawn to a broader goal: explaining frontier technology.

By frontier technology, we mean fields where capabilities are outpacing the answers around them, including AI, robotics, biotech, and digital assets.

On Dry Run, we bring on technical experts in their respective fields and ask them to explain the open questions that need to be answered to push the frontier forward.

We break concepts down to first principles, give an unvarnished view of where capabilities and limitations stand today, and look at how these advances change the systems around us: work and labor, software and product development, business and organizations, hardware, and research.

As progress accelerates, these technologies will increasingly affect how people work, create, build, and live. We want to shorten the distance between progress and understanding, because understanding leads to better questions, greater participation, and a clearer view of what is at stake.

Technological change can be empowering, but there is still work to do to make it understandable. We want to make that work a bigger part of Dry Run.

How does gold lending work?

Chunda McCain (@ChundaMcCain), co-founder of Paxos Labs (@paxoslabs), joins the Dry Run to talk about why the world’s most liquid commodity has so few lenders, and what that does to the businesses that run on gold.

00:33 Borrowers, lenders, and syndicators
04:51 Why bullion exchanges need physical gold
06:48 Roughly $40 TRILLION in gold volume in one month
07:54 Hiring crowd control for people selling gold
11:15 How a refiner’s hedge can blow up the business
14:02 Gold holders are leaving income on the table
18:55 Nation states sold US bonds and bought gold
24:20 Buying equities with gold
29:04 Collateral that pays you income

What does rare AI behavior mean for safety?

Zach Horvitz (@zachary_horvitz), a PhD candidate at Columbia researching rare behavior in generative models, joins Dry Run to explain what rare harmful behavior looks like, how researchers measure it, and what it means for the safety of models people rely on.

00:53 What AI safety looks like today
04:05 What rate of harm is "acceptable"?
06:56 Why the same prompt gives different answers
08:08 Why rare harmful outputs matter at scale
11:36 Asking the same harmful question 10,000 times
12:13 How to estimate the odds of a harmful answer
13:50 Using AI models to judge other models
16:28 How human feedback rewards flattery
18:42 Why safer AI also gets used more
24:19 Testing models without access to their internals
27:04 How rare outputs could drive scientific discovery

What is an open weight model?

Shiv Shanker (@sshankar), founder and CEO of Boundless, joins the Dry Run to explain what an open weight model is, and why he thinks most companies will try closed models first but eventually shift to open ones.

00:40 Understanding open weight models
01:57 What labs actually share when they publish weights
06:33 Cost considerations in AI adoption
08:47 Security and data privacy
13:08 You can't vibe code DocuSign
15:48 The MySQL playbook for open source profits
20:33 Why the top open weight models come from China
24:09 What changes after OpenAI and Anthropic go public
25:32 The case against open weight models
30:04 Don't give an agent your passwords
31:50 Regulatory frameworks for open weight models
36:23 Education and workforce development for AI
38:41 The sign that open weight models are winning

How is the AI buildout financed?

Austin Campbell, founder of Zero Knowledge and adjunct professor at NYU Stern, dives into the ins and outs of the AI buildout, especially how it is financed, who's doing the financing, the role of debt, and how the AI boom compares to past bubbles.

03:39 Components of the physical buildout
07:41 Capex spent so far vs. future commitments
10:13 Why Big Tech buybacks are dropping
13:18 How SPVs are structured: good vs. bad uses
20:13 AI makes money, but can it pay back the capex?
20:59 Who takes the hit if demand falls short
24:33 How today's SPVs compare to 2008
32:25 Minsky's three stages of a bubble, applied to AI