Designing for agents as the user

On Y Combinator's channel, Supabase's founder said signups are accelerating, and activation and conversion are rising with them. She said that usually doesn't happen when the top of the funnel grows. She credits models that are now good at building, which is bringing in people without engineering backgrounds Y Combinator. Software engineers are still a large share of users, but this "builder" segment is growing fast Y Combinator.

The product followed the users. After seeing that "no one visits dashboards anymore," she sent the team a memo: the product must be code-first, and anything on the dashboard must also work in code as infrastructure-as-code. The team rebuilt its CLI to match Y Combinator. She describes two stages of winning agents. First the model has to pick your product. Then, when an agent gets stuck, you need to keep it from switching away. So the error messages now tell agents how to fix the problem or which docs to read Y Combinator. Much of the engineering effort now goes into testing specific use cases across models, not into building Y Combinator.

Outcome pricing in vertical AI

On a16z's channel, Eve, a legal AI company for plaintiff firms, said it wants to handle a case "from the very first call to getting them a settlement" a16z. One Los Angeles firm reportedly went from about 100 clients per attorney to 500, using Eve for intake, qualification, handoffs, and settlement prep a16z. Eve started with tiered, Starbucks-style pricing. It moved to outcome-based pricing about a year and a half ago, once it learned firms cared most about serving more clients. It expects billable-hour pricing to fade the way seat pricing has, though not to disappear where value only shows up years later a16z. Eve says it hasn't yet run a fully autonomous end-to-end case. Human touches per case are still needed but are "drastically" falling month over month a16z.

PMMs say AI is creating rework

On r/ProductMarketing, a PMM said she spent about 70% of her time this year editing other people's AI-generated content. Some of it was misleading or didn't match the company's messaging r/ProductMarketing. Replies converged on one rule: whoever generates the copy has to check its product claims, or "they've just moved their work into your queue" r/ProductMarketing. Another PMM says AI gets 80–90% of the way and makes people check with them before using AI assets r/ProductMarketing. One commenter said they're measured on how much they use AI and fear being cut if they don't use it enough r/ProductMarketing.

The strongest workflow shared in the thread watches 30+ cybersecurity competitors. When a relevant breach happens, it drafts a point of view for a human to approve, and only then generates posts, emails, and discovery questions. The author calls the results "a pretty good draft" r/ProductMarketing.

Win/loss: price objections often mean the buyer couldn't justify the premium

A B2B SaaS PMM found that CRM notes blamed price for lost deals. Buyer interviews told a different story: buyers understood the product but couldn't explain internally why it was worth more. That came up in only two interviews, so the PMM held off on rewriting the battlecard r/ProductMarketing. Suggestions from commenters:

• Ask recent wins how they got the extra spend approved r/ProductMarketing.
• Test a short internal-approval page in the next few deals, showing the price difference next to the cost or risk the product removes r/ProductMarketing.
• Ask buyers who saw it and whether it changed the decision. A forwarded link or a click doesn't show that the objection moved r/ProductMarketing.

On AI-run A/B testing, one reply had a similar view of division of labor. Use AI to design experiments, check variables, and compute significance, but keep a human in charge of decisions r/ProductManagement.

Heuristics worth keeping

• Prompting. Aakash Gupta relays Claude Code creator Boris Cherny's three rules: say what you want, set the effort level, and define how the work is verified Aakash Gupta. Gupta notes that Opus 5.5 Max costs about 4× more per task than medium and scores 58 vs. 48 on the Artificial Analysis index. He often prefers lower effort with a few rounds of iteration Aakash Gupta.
• Launch discipline (Mark Pincus, via Gupta). Only one new idea per launch, so the launch actually tests it. Users should reach "the good stuff in 3 clicks." Set your click-through bar before you look at results Aakash Gupta.
• Context. Run the Business frames product intuition as compounded context: PMs have to "out-context" their CxO to hold their own in a debate Run the Business.
• Retention ethics. On r/startups, a founder said they design retention around addiction-relapse triggers and streaks r/startups. Commenters called it "engineering a dependency loop" and pointed out the post gave no churn numbers r/startups +1.