Dillon Mulroy, Rhys Sullivan, and Sunil Pai open The Next Token with a question about results. Models write more code. Does the software get better? The Cloudflare-sponsored conversation was recorded June 15, 2026.

A stronger model can fit the wrong workflow

  • 0:56 — Mixed feelings about coding by hand. Dillon misses parts of the old craft while enjoying development more than ever. Rhys wants Fable back.
  • 4:16 — Small tasks and large runs need different behavior. Sunil describes a model contemplating a universe of options when he only wants a layout change. It fares better on a large pull request.
  • 6:16 — Dillon did not try Fable before access disappeared. His objections to its long-running workflow are expectations, distinct from the other hosts’ hands-on reports.
  • Their opening news concerns what they call Fable/Mythos and a US access suspension, as reported by the hosts at recording time.

A $250 run asks two different economic questions

  • 8:25 — Affordability depends on where you work. Sunil says one $250 experiment matched his monthly salary when he started around 2005–2006.
  • The description says his run involved roughly 10,000 lines, testing, and intervention. The price bought supervised work, not a finished feature without human effort.
  • 16:39 — Rhys counts delivered value. Spending $200 can make sense if the result is worth $400. Dillon questions whether ordinary teams can imitate token-rich model labs.
  • 17:00 — Split planning from implementation. Rhys sees promise in using an expensive model for reasoning, then handing scoped tasks to cheaper models. That is a proposed cost strategy, not a measured saving.

Architecture sets the ceiling on local improvements

  • 11:41 — A domain-system rewrite offers a concrete test. Rhys thinks agents could have shortened the project. Dillon doubts they would have invented its streaming-search and caching design.
  • 14:38 — Faster experiments are a clearer benefit. Agents could help with prototypes, CPU measurements, and memory benchmarks while people choose the architecture.
  • 12:43 — A renderer can improve inside the wrong design. They recall Mitchell Hashimoto discarding agent-generated optimizations to pursue a better architectural approach. Their retelling is approximate.
  • 13:33 — New connections matter. Sunil invokes Steven Johnson’s Where Good Ideas Come From: cities and diverse networks help people connect ideas. He uses that lens to question model-led invention.

Routing needs an observable change of capability

  • 18:55 — Model selection and silent substitution are different. Dillon accepts routing but worries about a fallback invalidating a cache. He explicitly says his billing interpretation could be wrong.
  • 19:58 — Sunil wants to know who did the work. A weaker model taking over the main thread can change whether he trusts the result. Visible delegation to a subagent is easier to supervise.
  • 20:55 — Safety and control remain in tension. Rhys considers verification for sensitive work reasonable. The others ask whether labs or governments should set access limits.
  • 22:33 — Encryption supplies an analogy. They recall PGP source code distributed as a book during export-control disputes. The comparison is exploratory, not legal guidance.
  • 23:41 — Open weights offer alternatives. Downloadable models, different training histories, and mixed-model teams could reduce dependence on one or two providers. Rhys distinguishes temporary lead time from permanent exclusion.

The last ten percent still needs an owner

  • 28:49 — Career forecasts can change training decisions. The hosts revisit Geoffrey Hinton’s radiology prediction and reject similarly sweeping advice against entering software. Their account of workforce effects is secondhand.
  • 31:58 — Removing easy work can increase fatigue. Sunil says agents leave him continuously handling hard problems. Fewer routine tasks can also weaken the apprenticeship path for juniors.
  • 32:44 — A portfolio is easier to imitate. Rhys recalls standing out through hackathons and game jams. A convincing first version now says less about who can finish the product.
  • 33:19 — Novelty competes with completion. The hosts describe “90% apps” and abandoned repositories. Starting another project can feel easier than making the current one dependable.
  • 35:06 — Experiments remain useful when labeled honestly. Rhys defends early user feedback. A rough Executor demo illustrates why a prototype should not be represented as complete.

Generated code is a measure of authorship

  • 36:19 — The hosts accept a real shift in implementation. Dillon says AI now writes nearly all his code. Rhys still handwrites important types and interface surfaces.
  • 37:44 — Human direction remains substantial. They supply context, architecture, constraints, and review. A high generation percentage does not measure unattended engineering.
  • 39:48 — Sunil asks for a user-visible result. His example is Gmail catching recurring account-warning phishing messages. More generated lines do not answer that request.
  • 40:25 — Product quality still reflects people and priorities. He credits Cloudflare’s improving dashboard to the team doing the work, not a line-count statistic.

Name the loop before predicting its future

  • 42:38 — “Write loops, not prompts” becomes the next receipts claim. The hosts plan to revisit statements from Boris Cherny and Peter Steinberger over six months.
  • 46:28 — An inner loop differs from the whole business. Reproducing a bug and testing a fix is narrower than taking customer complaints all the way to production.
  • 47:26 — Rhys uses Factorio as a metaphor. Find a bottleneck, improve it, and inspect what limits the system next. He counts saved human testing time alongside token spend.
  • 47:44 — Their own bets stay conditional. Dillon expects more looping if the economics work. Sunil expects one or two background tasks. Rhys is bullish but worries that removing friction also removes learning.

“What level of loop are you in?” — Rhys Sullivan, 46:58.