#01 - Sep 14, 2026
State of AI: Ramping Down TDD
Ramp AI thesis, Adam's TDD farewell, Everyone is coding (in smallish teams).
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Our users https://x.com/voidisomorphism/status/2099405491663208859?s=20 can merge their own PRs https://www.linkedin.com/posts/ecura_its-crazy-thats-the-reaction-imagine-activity-7505185512245215232-Dh5s?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAPiJJwB0LmpHdW8bLd4wZ0_Zuy-prVZFNo. agent-swarm.dev http://agent-swarm.dev has over 100 forks. Here’s what else happened. ---------------------------------------- Cracking the AI Thesis tl;dr: Ramp Economics Labs sounds like a cautionary tale for the late adopters. 1 month https://econlab.substack.com/p/ai-index-august-2026 later https://econlab.substack.com/p/ai-index-sept-2026, Ramp published a part 2 from the Cracks in the AI thesis series, what you should read between the lines: * 500x spend for the top 1%. The median employee spends <14USD a month in AI, the top 1% is north of 7k. You need to figure out if this is an adoption, optimization or accessibility problem, and find a middle ground that works for your team. The top 10% spend is ~700USD, which sounds much more reasonable. * 33% increase in Open Source Adoption since Jan. The number is still low, 6.4% total, but we are starting to see a trend when discussing with other companies. On our end, we are digging into SetFIt first, and considering Tobi’s route https://x.com/tobi/status/2056198717225464307?s=20. * They only measure spending. This is the biggest flaw in the report, because they only use one source of information: expenditure. We don’t know from these reports what optimizations and self-hosted solutions are out there. We do hear about them more frequently now. Did you get your own Spark https://www.nvidia.com/en-us/products/workstations/dgx-spark/? Anthropic & OpenAI are concerned about a model race https://x.com/sama/status/2098811563415150910that will kill us all https://x.com/EvanHub/status/2097497037956891126, begging for regulation, and publishing reports on “bad players” https://www.anthropic.com/threat-intelligence-report-september-2026. This, mixed with the news that they won’t IPO https://fortune.com/2026/09/12/sam-altman-openai-ipo-delay-ill-advised-moment-safety-concerns/ this year, paints a complex scenario. Remember SWEs were doomed a year ago? → Challenge. Interiorize the major trends, not the specifics. After all, this smells like accounting trying to tell you how the business is doing. ---------------------------------------- TDD BDD XOXO tl;dr: Adam Tornhill walks away from TDD. https://adamtornhill.substack.com/p/practices-i-abandoned-with-agents That’s the tip of the iceberg. A nice short read on the history and motivation behind TDD, and how it is not worth it with the new AI SDLC strategies. We’ve seen it in neighboring situations: * TDD, BDD, DDD, Hexagonal, Onion, Clean, CQRS, whatevs. Humans weren’t good at coding (nor fast, but cheap compared to Fable). So we needed frameworks to make sure we were less worse than we could be. TDD is an example of that. You need to distance yourself from the code enough that the inners start being secondary. * Proof of Work. Adam argues his focus now is in requesting e2e tests. He doesn’t need more granularity than that. Check Human / Agent boundaries https://adamtornhill.substack.com/p/controlling-the-uncertainty-machine?open=false#%C2%A7tests-as-humanagent-abstraction-boundaries. As a result he spends most of the time in the validation step, and the requirements include tests for verification (green means yes). * So now you are QA? Nope. Taras shared our Semantic Distance https://www.tarasyarema.com/blog/2026-02-18-introducing-semantic-distance framework months ago for this specific reason. Your engineering team job is helping shifting left, ie. making more of the validation steps become simply verification “scripts”. → Adopt. Is your team focused on building the machine that builds the machine? If not, read the next piece. ---------------------------------------- Everyone codes. tl;dr: After our Capchase Case Study https://www.agent-swarm.dev/case-studies/capchase, CampusIQ shares how they did it. https://campusiq.com/blogs/everybody-ships This time it is close source. CampusIQ software and team seem ideal to experiment with this AI native approaches, and they did great: * No pure ICs, but Managers. They recognized early on that the first AI Native Employee would be a manager https://www.pleasedontdeploy.com/p/your-first-ai-native-ic-will-be-a, and that was at the core of the hiring process. Focus on getting people with the culture of business results, able to work in multiple areas at the same time, and used to coordinate. Basically, you need jugglers, AI would provide the grunt work. And… they were willing to pay for it (not everyone has the cash). * Let them come. Two things worth reading in the article: Second Brain high level architecture & how they onboarded the team. Both resonate with how we have seen. Bring people together, get leadership buy-in, push the vision from the exec team, and build the gates that will ensure your business can scale. * Closed source. Well, this is where we don’t agree. We believe if your team is just doing this you either have a quite sizable team, or you are not moving fast enough. The motivation seems to be competitive advantage, avoiding vendor lock-in, or lack of knowledge. Ask yourself this question: is Linux, macOS or Windows your competitive advantage? → Wait. 3 failure modes you need to avoid: find dedicated resources, strengthen your quality gates (see above), and spread the news: a demo is not a product. ---------------------------------------- Are you here for the AI Summit Barcelona? Let’s have a coffee! Or keep reading newsletters https://www.desplega.sh/newsletters. Your call. Best, ---------------------------------------- Sent with ❤ by desplega.sh http://desplega.sh unsubscribe .