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Speed without direction
Marty Cagan put a name to what many teams are seeing: the "AI productivity paradox." Teams are "absolutely moving faster," but "they're not actually getting better results." He says McKinsey and Atlassian have both published data on it . In his view this is what you'd expect from the project model. Speed is good, but it "shines a light on the need for people to actually know the craft" .
Melissa Perri calls it the build trap again. Companies are spending "hundreds of millions and billions" on AI but aren't putting in the time to work out what to build with it . She gave an example. Usage data for a therapist product suggested nobody wanted it. Interviews showed therapists did want it but gave up because it kept breaking. Her conclusion: "AI will never set your strategy for you" .
Ravi Mehta gave practical advice on what to do with the extra capacity: "A faster first draft should give us more time to make the final draft better." Get to a working version early, review a full customer flow while changes are still cheap, and polish instead of adding features . Agents now effectively write the final draft of the spec. Without the customer's goal and the intended experience as context, they "can produce a build that looks finished but misses the point" . He warns executives that if every efficiency gain turns into another roadmap commitment, teams stay just as rushed .
On Aakash Gupta's podcast, Tim Herbig made a related point. His clients said feature adoption was already a problem a year ago, so teams shipping more must ask "did any of it make a difference?" . He uses Roger Martin's test for whether something is a real strategic choice: its opposite must not be stupid . He rejects tokens as a success metric, since tokens are "a value exchange for Anthropic," not for your customers . He also dismissed AI search as a differentiator: "No one will buy a car with you because you have an AI search" .
Shreyas Doshi argued that top-20% builders already talk to users enough, and talking more won't do much. Most content and company processes are written for the average builder, and "judgment matters most" . A Google Search AI leader said the same thing from a team's point of view. When building is fast, the question is whether an idea is worth it compared with five others, given the cost in people's attention and tokens . She runs lean, flat "SWAT team" pods: one strategic senior lead and a small bench of ICs .
A senior PM on Reddit described what this looks like on the ground. AI-generated strategy documents linked roadmaps to dependencies that "nobody had validated," and those documents may now feed decisions on investment and resources . The poster proposes requiring every document to name the decision it supports and its owner, and having a named human validate any AI analysis .
Your product already has an agent policy
A Mind the Product episode looked at agents failing partway through checkout. Walmart had a deal to welcome Meta's Muse agent, but an old "verify you're a human" check kicked it out . On September 15, Cloudflare started blocking agent and training traffic by default on pages that show ads. Nobody has confirmed that this caused Muse's failures . Meta and Sierra have proposed a protocol for authenticating agents in commerce, with a 0.1 spec planned. The host is skeptical: no spec exists yet, and it enters a crowded field . His suggestion: pick one key flow, run an agent through it, log every place it fails, and design what the customer sees when the agent gets blocked . Start agents with read-only access and require the user to grant write access deliberately .
Hiten Shah noted that in Q3, Anthropic (Claude Cowork), Grok Bot, Meta and OpenAI all shipped persistent agents that keep working after you leave . Aakash Gupta described a case where Claude ran rm -rf on a developer's home directory while testing a sandbox it had built. The model "already knew" deleting a home directory was a bad idea, but nothing outside the model checked the command. His fix is a rule enforced in code that blocks deletes outside the project folder .
Hiring signal
Gupta read 113 AI PM job listings by hand. Seven in 10 asked for prior AI experience, but only 1 in 8 required production experience. He recommends building something live that you can defend, including your model choice and evals .