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.

In physical AI, data companies are scaling rapidly. However, there's no standard yet for which robotics data is actually valuable. Meanwhile, well-funded, VC-subsidized labs are spending heavily to get more of it. For now, the bottleneck is quantity, not quality. That's changing, in the same way it did when @scale_AI gave way to @mercor. Scale sold regular labeled data in bulk. Mercor brought in experts to label very specific components, and quality started to matter. Robotics is going through that shift now. But if higher-quality data means companies need less of it, it's an open question whether today's data vendors survive long term. Naman Kapasi (@naman_kapasi) on @TheDryRunPod

talked to @austincampbell about SPVs for a suspiciously long time

Should we really be building humanoid robots? The robotics industry has conflated two things: humanoids and physical intelligence. A humanoid is one specific form factor: two legs, two arms, a torso, and a head. Plenty of well-designed general purpose robots don't follow that form factor. The case for humanoids comes down to two things: - Locomotion: getting into tight, narrow spaces - Manipulation: moving objects and doing the work humans do In the long term, a humanoid is a great robot. In the short to medium term, it's significantly harder to build than other general purpose form factors. Naman (@naman_kapasi) on @TheDryRunPod

World action models in robotics output the next action, not the next image. Video models have gotten significantly better at fidelity over time by predicting the next frame. World action models work on the same principle of predicting the next state, but the state they care about is the action that needs to be taken. In practice that means generating something like six possible trajectories, one for each action the robot could take, and committing to one. Because the models only look a few seconds ahead, accuracy is surprisingly good. The harder problem is choosing the right action. Naman (@naman_kapasi) on @TheDryRunPod Listen to the full episode below.

A big question right now is how much of the sales process gets handed off to agents. Everett talks about why the relationship layer of enterprise sales will likely remain, while more transactional sales may start happening without any human interaction. As agents start discovering what they need to buy and use, companies may rethink what their products look like when there isn’t always a human in the loop.

"People who start using Clay actually send fewer emails than they did before" Crazy stat. Would not have expected this.

One of my favorite parts of this conversation ⛳️ Everett breaks down a campaign that booked 60% of targets. They picked ~100 CROs they could reasonably infer were golf fans, sent them @TheMasters gear you can only buy at the venue, used Clay to find office addresses, the USPS API to time delivery, and followed up with handwritten letters. Specificity matters more in outreach now that outreach at scale is cheap.

GTM engineering won’t just be a B2B SaaS function. Everett talks about how industries like real estate, insurance, banking, and others are starting to adopt modern demand gen and AI GTM workflows. Effective motions are built around the specific signals that matter in that industry, like a waste recycling company using satellite imagery and dumpster colors to identify potential customers.

HOW GTM ENGINEERS ARE REBUILDING SALES Everett (@retttx), Head of GTM Engineering at Clay, joins @eshita and @khushii_w for a conversation on the rise of GTM engineering. 06:00 - Why the old outbound motion broke 09:51 - GTM loops: data > agents > action > feedback 12:46 - The “golden list” every founder should build 14:04 - Good outbound means sending fewer emails 16:24 - The campaign that booked ~60% of targets 19:48 - How other industries leapfrog into modern GTM 22:17 - The GTM mistake founders make early 29:06 - Why GTME is an index on AI capabilities

Great conversation with @retttx on how GTM engineering works in practice: campaigns he’s built, how GTME can support industries outside tech, and what sales workflows may look like as AI gets more capable. Lots of tactical advice in here if you’re building GTME at your org and want to borrow from how @clay_gtm thinks about it.

Marc Andreessen calls it the original sin of the internet: There has never been a clean, native way to send money online. The internet's creators set aside a status code for payments, HTTP 402, marked it "reserved for future use," and never successfully implemented it. x402 is the attempt to finally fix it. Lincoln (@MurrLincoln) on @TheDryRunPod

2010s: web to mobile. A human was still driving every action. 2020s: mobile to agents. Interacting at a scale and speed we've never seen before. @MurrLincoln on @TheDryRunPod

RT @khushii_w: The Visa card network can’t process a transaction smaller than about 35 cents. Layer on a 2–4% processing fee, and any atte…