You don't need to be in the room to run a real lab experiment anymore. Jonathan Green talks with Keith Robison, a computational biologist of 35 years who now works on lab automation at Ginkgo, about the company's autonomous lab: reconfigurable carts you assemble like Lego, an agent that turns scientific intent into a protocol, and a run you can launch, leave, and check from your phone. They talk about what that means for students and amateurs, and what still gets in the way, from slow, expensive DNA synthesis to instruments that were never built for robots.

Key Takeaways:

• Keith has run an experiment on the autonomous lab from 30 miles away. A scientist builds a protocol with an agent, loads the reagents, and pulls all the execution data afterward.
• Ginkgo's Cloud Lab offers free design pipelines for protein engineering. You send in the design, Ginkgo runs it on the autonomous lab, and the data comes back.
• The real barriers are cost and access, not lab skill. DNA synthesis is still slow and expensive, and equipment like surface plasmon resonance is rare outside pharma labs.
• Instruments were built for humans. Protocols often live on the machine under a name with no version control, so every change needs review to be sure you get exactly what you asked for.
• Vendor APIs are the bottleneck. Agents reviewing public APIs keep finding bugs and documentation gaps, such as a sleep function where the examples say milliseconds but the underlying code counts seconds.

Notable Quotes:

"I've run an experiment on the system from 30 miles away." — Keith Robison

"You no longer need to be skilled in the lab, and you can switch places very quickly." — Keith Robison

Connect with Keith Robison:

LinkedIn: https://www.linkedin.com/in/keithrobison

Ginkgo Cloud Lab: https://cloud.ginkgo.bio

Company: Ginkgo Bioworks — https://www.ginkgo.bio

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Every AI company is racing to stay private as long as possible and IPO at the highest number they can justify. Lyle Pratt did the opposite. Jonathan Green talks with the founder and CEO of Vida — an AI agent operating system that listed on NYSE American in May 2026 at a fraction of the usual size — about why the standard advice to stay private is VCs talking their own book, what public-company transparency does to enterprise sales, and why he thinks the money in AI is in outcomes rather than tokens.

Key Takeaways:

• The advice to stay private is not neutral. A VC earning two-and-twenty on a large portfolio position loses that carry at IPO and gets marked to market for the first time, so the incentive is to keep you private, and the "staying public is too complicated" argument is increasingly a view of the past.
• Going public early is also a fairness argument. Most ordinary investors are locked out of the fastest-growing companies because they are not accredited, so the whole agentic boom happens where they cannot participate.
• Transparency became a sales asset. Customers evaluating a long-term partner can read the filings, see the cash position, and check the quarterlies, which removes the doubt that normally sits under an enterprise deal.
• Customers do not want a chatbot, they want an expert who will deploy an AI employee against a specific process end to end and keep it working. Running hundreds or thousands of agents needs the same things as running hundreds of employees: guardrails, observability, compliance, multi-tenancy.
• Tokens are a commodity, outcomes are not. There are roughly 400 million IVRs still waiting to become agents, US call centres run about $1.80 per handled minute, and globally every dollar of software spend sits beside six or seven dollars of services spend. That is the gap agents are moving into.

Notable Quotes:

"There's a lot of fear mongering from VCs about you can't go public — and it's more and more VCs talking their own book." — Lyle Pratt

"People want to pay for outcomes. They don't want to pay for tokens." — Lyle Pratt

Connect with Lyle Pratt:

• LinkedIn: https://www.linkedin.com/in/lylepratt
• Company: Vida — https://vida.io

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For twenty years the rule was simple: get more people into the top of the funnel. AI broke that rule. Jonathan Green talks with Tanner Sherman (Principal & Managing Broker, Top Tier Investment Firm, Omaha) about what happens when inquiring costs nothing — 150 "is this still available?" messages a day, 1,500 inquiries per rental, and a signed-lease rate that fell from one in 80 to one in fifteen hundred. The fix is not a better funnel. It is a deliberately worse one.

Key Takeaways:

• AI did not create more demand, it created more noise. Tanner's team went from roughly one signed lease per 80 inquiries to one per 1,500 once "I'm interested" became a single tap.
• Deliberate friction is the filter. An auto-reply that asks people to text the office cuts 1,500 inquiries down to 100–150 actual humans, then to 10–12 showings, then to one lease.
• Stop optimizing for engagement and start optimizing for proof of commitment. Reach, clicks and inquiries are exactly the numbers AI inflates fastest — the number that matters is who took a costly step.
• Referrals repriced overnight. When résumés, portfolios and proof can all be generated, a person somebody actually knows beats a better-qualified stranger.
• Delegate to AI the way you delegate to people, using the four levels: investigation, informed progress, informed results, ownership. Most people never leave level one and then blame the tool.

Notable Quotes:

"It's become too easy to inquire. We get spammed with probably 150 'is this still available's a day." — Tanner Sherman

"Whether you're hiring a VA, a US-based employee, or delegating to AI, the intention of the employer needs to be clear as to what level you're utilizing that skill." — Tanner Sherman

Connect with Tanner Sherman:

• LinkedIn: https://www.linkedin.com/in/tanner-sherman-707529222
• Website: Top Tier Investment Firm — https://toptierinvestmentfirm.com
• Podcast: Freedom Fighter Podcast — https://podcasts.apple.com/us/podcast/freedom-fighter-podcast/id1765821493

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