We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Your next user may be an agent
Executives at Cisco and Vercel made the same argument from different directions: products now have to work for agents, not just people.
Vercel's COO put it bluntly. "Agents don't live in UIs. They call APIs. they hit MCP servers." A product that isn't composable and open to developers is "invisible to agentic workflows" . His advice is to build developer surfaces now: MCP servers, webhooks and APIs . He also said agents built without a structured, queryable model of the business "hallucinate or give generic answers" .
Vercel's own build process is worth borrowing:
- A GTM engineer shadowed a top SDR and turned each tool she used into a workflow step before any AI was involved .
- The agent then ran in shadow mode for six weeks. The SDR reviewed every output until she couldn't improve it .
- The lead-qualification agent costs under $5,000 a year plus about 20% of one engineer's time .
- Every quarter, the team checks whether a vendor has built something better, faster or cheaper .
Cisco's Jeetu Patel (President & CPO) said Cisco just launched a personal concierge agent that brands can give every customer, not just the top 20 or 100 accounts that get named reps . He also explained the shift that set this up. After 250 acquisitions, Cisco worked like a holding company and has now become a platform company. "We don't have any product managers at Cisco. We have feature managers," building features for the platform . Two more lessons from him:
- A second product that doesn't use the first product's route to market will fail .
- When a market looks important, Cisco now asks "what are we going to build" instead of "what are we going to buy" .
On judgment, Patel said: "what you choose not to build is far more important than what you choose to build," and that choice takes taste and an instinctive grasp of the market and customers .
There's a trust problem underneath all this. In a16z's review of about 3,600 messages from AI power-user group chats, trust was the top fear, raised in 22% of them . Their suggested design pattern: scoped permissions and monitoring, with the agent doing the groundwork and the user approving the final decision and its cost .
Connecting discovery to delivery
On Mind the Product, Jenny Martin explained Oopsie, a framework with five layers: outcomes, outputs, process, scenarios and inputs . The idea is to do enough thinking to build the right thing without falling into waterfall-style analysis paralysis .
- Outcomes. An outcome is a real-world change that creates value, usually for a customer. "A number on a spreadsheet is not the outcome" .
- Outputs. Each layer is a chance to prioritize. She estimates that about 20% of possible outputs deliver about 80% of the value .
- AI features. Non-deterministic behavior needs evals with ranges and scoring. Deterministic results need exact expected answers .
Her example: she once showed a retail customer the exact test data the team planned to use. The customer spotted a fundamental wrong assumption, which saved an estimated three or four months of work .
Funding strategy, not projects
Evan Campbell (One Knight in Product) said the most common way OKR programs fail is tying them to rewards, because people then sandbag their objectives . His portfolio approach:
- Rank cost-cutting work by NPV or IRR, but judge innovation only against strategic objectives. "There is no ROI" calculation for that work .
- Set aside a share of investment, such as 10%, for innovation so it doesn't get starved .
- Fund work in stages, and "trim the tail" once another quarter of investment would return less than a new idea would .
Cheap copying and the SaaS question
Casey Winters argues that AI makes copying so cheap that competitors "copy first and find out later." Copying is becoming a better tactic, he says, but not necessarily a better strategy .
On r/ProductManagement, a claim that one CEO used Claude to replace 9 SaaS vendors and raise margins 25% drew pushback. One reply said he built 9 good-enough internal tools, not 9 products, and now has to maintain them . Another said vendors take on risk for compliance, security and support .
Hiten Shah offered a more modest opportunity. Coding agents make the "unwritten backlog" of changes people stopped requesting worth revisiting .
Where AI costs PMs time
A widely discussed thread listed the costs:
- Slop docs. A 20-page AI-written requirements doc where a paragraph would do .
- Surprise errors. Claude helps one PM 95% of the time, but random hallucinations cut the net gain to about 50% .
- Hard-to-trace PRDs. Generated PRDs make it hard to tell where KPIs came from .
- Always-on recording. AI recording every call and Slack message makes search easy, but leaves a record of everything anyone says .
Status reports you can't trust
For a PM whose dev team keeps reporting false progress, the most practical replies:
- Require a working demo on shared staging by day 7 of the sprint.
- Log committed date, delivered date and first-try acceptance-criteria pass rate, then bring three sprints of trend data to whoever owns delivery .
Several commenters said accountability for estimates and delivery belongs with the engineering manager .
Also
Conductor, the AI coding app, is joining Meta to build "the next interface" with Meta Superintelligence Labs (MSL) .