Went on one of the biggest AI channels in the world to reveal two things: 1) How we're killing consulting. It needed to be taken out back, and rebuilt from the ashes. 2) The MetaHarness - an AI-native workflow + SDLC we used to 10x productivity Def watch the full video (@DavidOndrej1/@dan_zakon crushed it), but here are the highlights: 1) How to make a slow company AI-native - Meet the company where it actually is: systems, people, and where they sit on the AI journey. - A 2026 consulting business for Fortune 2000s has to be a full-stack partner, not a deck shop. - Old model: consultants take a problem, sit on it for months, deliver a 150-page deck, say peace, leave the client to implement. - Post-AI: you have to touch the whole chain. Strategy, change management around the tech, and forward-deploying into the company. - For a lot of Fortune 500s, the first move is teaching the C-suite Claude CoWork/Claude Code or Codex. - The job is to drive friction as close to zero as possible. 2) How we're killing & reviving consulting - Me and @ArmanHezarkhani did not dream of being consultants. - The vision: modern-day Bell Labs - My bar: if I'm not home with my wife and daughter, the work has to be worth it. Worth it = frontier problems with the smartest, most driven people you have worked with. - Arman read The Innovators in school (Babbage and Ada Lovelace through mobile revolution). The book’s lesson on innovation centers: Bell Labs / the transistor, DARPA / the internet, Xerox PARC. The raw materials are always brilliant people given autonomy, plus direction, distribution, and resources. - We did not want to raise hundreds of millions on day one. Helping the biggest companies solve their hardest AI problems cashflows the business, tells us what actually hurts, and supplies the materials for an applied AI lab that incubates consequential post-AI tech. - Consulting is a dirty word. It's growth & innovation as a service. - Classic consulting pitch: go in, problem, the answer is often firing people, deck, walk away, never see the fruit. That is why great technical talent never went there. - Reposition: messy, important problems, no answer, an A-team for innovation and implementation, actual ROI. - Consulting by-design gives engineers diversity of work without job-hopping. “ADHD is satisfied, same company.” 3) Singleplayer vs. multiplayer AI - Single-player: swap Google for GPT/Claude on the same query. Zero behavioral change. Still valuable. - Enterprise version of that: sign a $5–10M token agreement with OpenAI or Anthropic, get secure Codex/GPT or Claude Code/Co-work, put SOTA models in front of people, teach daily use. Most companies should start here. No compounding. - Multiplayer: reinvent a horizontal process so leverage hits a whole function or the whole company. - Examples: an SDR/sales agent that gives sellers back time from logistics so they talk to clients. Data engineering when an “AI problem” is actually a data-readiness problem. - Exponential, messy, painful value is multiplayer. 4) Breaking down the MetaHarness - Engineers have always been methodical about files. Knowledge workers are playing catch-up. Tenex is taking it further: extremely prescriptive context for coding agents. - Old SDLC: human-to-human coordination via Agile ceremonies (standups, retros, a calendar full of meetings). - New SDLC: humans, other humans, and agents. Agents are the main character doing the work on the ground. - Written-first culture matters more than ever. Markdown briefs humans and agents. - Agents can hold more coherent information than humans, so you can write bigger blueprints further in advance than Agile ceremonies ever allowed. - Concrete markdown artifact types on every project. Agents pull the right context at the right moment and keep it in sync. Dan calls it a “software machine.” - The problem the harness is solving: agents give speed that did not exist. Longer tasks increase entropy and diversion from plan. Minimize entropy without giving up speed.