We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Businesses in ChatGPT: an agent talks to your customers on OpenAI's platform
Mind the Product's Now Shipping covered OpenAI's beta, launched November 10. Any business can claim a verified profile, and businesses that sell can add a feed with current pricing and availability. Shopify merchants can connect through Shopify Catalog . A user who opens a business's profile can start a separate, labeled chat with that business's agent. The agent answers from guidelines and product and policy information the business supplies . At launch, agents are limited to eligible US e-commerce and online-service businesses . Businesses can also sponsor their agent as a destination for ChatGPT ads .
The host argues the agent is a product experience and shouldn't be handed to marketing by default. Product should decide what the agent can promise on price and availability, what it says when it doesn't know, and when it passes a customer to a person . There is currently no handoff to a human . Measurement is limited. Businesses see mentions, clicks and "discovery topics," but not individual conversations, and clicks don't confirm website visits or conversions . The host's practical steps:
- Ask ChatGPT five questions a buyer would ask about your product, record which answers are wrong, and repeat the check in a few weeks .
- Keep catalog, pricing and policy facts clean and portable, because the hub has no described export .
- Check that your on-site support bot and the ChatGPT agent give the same answers .
The same day, Reddit launched AI-summarized reviews for about 10,000 products, and businesses can't pay to be featured there. The result is one channel where you can pay for presence and one where you can only earn it .
UX is becoming the bottleneck in AI-accelerated delivery
In r/ProductManagement, a product leader said engineering and product teams using AI now see UX as the bottleneck. The org is date-sensitive and historically finished designs to 100% before handoff . Their proposal: the PM generates reference UI from the design system, UX and engineering agree the workflow, and then both work in parallel instead of in sequence . One lead designer says their team designs all features with AI, using a tokenized design system plus quality "skills," and tracks every feature for impact . Others push back. Experienced designers still need to steer AI prototypes , and setting up an agentic SDLC is complex and costly .
Leaner evals and research loops
- Evals: Aakash Gupta quotes Cat Wu, who ran product for Claude Code and now leads core product at OpenAI: 10 great evals matter more than hundreds. Once the goal is quantified, evals show the team what's missing .
- Customer research at scale: Former Bolt CPO Greg Greiner contacts users where they drop off and asks one question rather than sending a survey, which he says gets up to 40% response rates. He uses AI to resolve simple concerns. In client work, conversion rose from 13% to 18% for users who got an AI follow-up versus a holdout .
PM jobs are concentrating in the Bay Area
Lenny Rachitsky reports that one in four open PM and design roles is in the Bay Area. That share has nearly doubled since 2022 . Only 14% of tech roles offer a remote option, a record low, while PM is steady at about 21% . Bay Area PM, engineering and design openings have now overtaken remote roles worldwide . On Reddit, a final-year CS student with a PM internship got no interviews from 50+ applications , and a PM with two years' experience said they weren't getting calls either . A hiring manager for product marketing roles said AI came up in every interview over the past 12–16 months, specifically Claude, ChatGPT, Claude Code and Codex .
Briefly
- PMF keeps moving: In an interview, Hunter Walk agreed that product-market fit is something you keep rediscovering rather than a single event . He also warned that fund-return pressure leads investors to push founders unnaturally fast in years when they should be figuring out what the company is .
- Paytm UX: A UPI payment entered without connectivity confirmed later, after the user had already paid again, so the user was charged twice. The poster's suggested fixes: an immediate no-internet notice, a clear pending state and a duplicate-payment warning .
- AI-built code costs time: One builder said a Codex task meant to take an afternoon took three days, two and a half of them spent fixing bugs and security issues it introduced . Another shared a 30-minute prelaunch checklist: search JS bundles and git history for keys, rate-limit routes that call paid APIs, and test every route without auth .
- API economics: April Underwood wonders how Google is coping with Gmail and Calendar API requests that she presumes will spike, many of them more expensive than Google's own apps support .
- Re-org limbo: A PM at a company moving all product under one central leadership says many PMs are now competing for a limited set of projects .
- OpenAI’s Businesses in ChatGPT beta lets any business claim a profile, but agents were initially limited to eligible U.S. e-commerce and online-service businesses. Businesses can provide product feeds with current pricing and availability, including through Shopify Catalog; their agents answer customers in separately labeled chats using business-provided guidelines and product and policy information.
- Reddit introduced AI-organized reviews for about 10,000 products, including sentiment, discussion themes, and products people also consider; it says businesses cannot pay to have products featured. Alongside ChatGPT’s option to sponsor agents, this illustrates a mix of paid and earned product discovery.
- Product teams should treat business agents as customer-facing products: define what they can promise, how they respond when uncertain, and whether they can hand customers to a person. The source reports no human handoff at launch and flags the risk of an external agent contradicting a company’s own support bot; it also recommends keeping product facts and policies accurate and portable rather than relying on the ChatGPT hub as the sole home for them.
- Verified businesses can see mentions, website clicks, and discovery topics, but not individual user chats; clicks do not confirm visits or conversions. Discovery topics may provide research insight, but teams should account for the limited visibility when defining outcomes and attribution.
- Product-market fit is a continuing rediscovery, not a one-time milestone. Walk agreed with that framing and recalled that virtual-office concepts that seemed to have PMF—including expectations for 3D-rendered offices—were gone by 2021, with startups pivoting.
- A startup’s first years should focus on figuring out what the company and product are; pressure to achieve larger outcomes can make investors impatient, encourage unnatural speed, and lead to poor founder advice. The venture business model should not dictate that discovery.
- Seed is better understood as a phase than a fixed funding round: teams may raise in different-sized increments while developing the team and product, and move to a Series A when they have confidence in both and have product-market fit.
- In one date-sensitive product organization, AI-driven delivery speed has made UX feel like a bottleneck. The proposed response is to have PMs generate design-system-based reference UI, review the workflow with UX and engineering, then start engineering implementation while UX continues refining the final design—replacing sequential handoffs with parallel work.
- A practitioner at a large SaaS company reports using a tokenized design system and quality-focused AI skills to generate feature designs with real data and components, making iteration and testing faster; the team also tracks feature impact rather than treating output as the goal.
- Faster prototyping is not evidence that experienced UX can be dropped: commenters argue designers should steer prototypes and that PMs should define problems and requirements rather than prescribe UI. One also recommends checking satisfaction, loyalty, and retention when adding LLM features or materially changing release cadence.
- One team reports that agents configured with repository-specific settings and skills can produce deployable code, enabling a move from HTML/CSS prototypes to the product codebase; another practitioner cautions that setting up and operating an agentic SDLC can be complex and costly.
- Oxynote embeds live Postgres, MySQL, or Prometheus charts in specs and RFCs, and reviews important page changes through PR-style rendered diffs, comments, and approval, aiming to keep product documentation current. It is free, Apache-2.0, and runs as a single Docker image.
- NuHQ is in beta with an AI-supported feedback-to-roadmap workflow: it can triage and group ideas, attach ARR via Salesforce or HubSpot sync, and generate feature briefs, PRDs, and audience-specific release notes. It also lists Jira/Azure status sync and two-way sync with Aha! and Productboard; NuHQ claims it is cheaper than both.
April Underwood asks how Google is managing a possible shift from predictable Gmail and Calendar growth to sharply rising API demand over the next 6–12 months, noting that she expects many requests to cost more to serve than Google’s own apps support; this is her speculation, not a report of confirmed Google activity.
- One commenter proposes an about-30-minute prelaunch security check for hackathon apps: search deployed JavaScript and repository history for exposed keys and rotate anything leaked; test API routes without authentication; and use authentication, per-user rate limits, and spend caps for paid-API routes. Another recommends changing a user ID in a copied request to check whether it exposes another user's data.
- For delivery planning, one builder reports that a Codex-assisted task expected to take an afternoon took three days, including 2.5 days fixing bugs and security issues, with another 0.5–1 day of cleanup still needed.
The Bay Area now accounts for 15% of open tech roles, a record share; one in four open PM roles is there, and the Bay Area share of PM, design, and engineering openings has nearly doubled since 2022. Just 14% of open tech roles offer remote work, a record low; remote options are available for about 21% of PM roles, near their low, and Bay Area openings across PM, engineering, and design have overtaken remote openings worldwide.
For an exploratory call about a startup PM opening, probe whether the product has product-market fit, why the company needs a PM now, its runway and goals, and how involved the founder expects to be. Research the founder’s assumptions and push back; distinguish a genuine PM role from one focused on implementing the founder’s ideas. The commenter also cautions that founders may expect a level of devotion from a PM that is not matched by their potential payout.
- Frame the product around a latent problem in customers’ existing workflows, not a technology label; then consider a niche product for a specific market segment rather than a universal ERP solution.
- Before committing, map existing alternatives and their gaps, assess how readily incumbents could address those gaps, and keep asking why each differentiator matters to the buyer.
- One respondent recommends seeking customer groups with money and urgent bottlenecks, experiencing the supplier ecosystem as a customer, and testing offers through selling; they caution that the initial idea may need to change and finding scalable product-market fit can take 2–3 years.
- A founder’s B2B experience highlights switching friction: prospects often already use a vendor, defer decisions, or stick with cheaper or outdated products, even after agreeing to meetings.
- For PMs seeking advancement in a difficult big-tech job market, focus on scope and evidence: take an unowned, messy problem through launch and quantify what changed. Build visibility and relationships with promotion decision-makers beyond your direct manager, who may need others to vouch for you.
- One commenter advises demonstrating judgment by saying no to the wrong work rather than simply saying yes to everything.
- One commenter argues that PMs’ value in the AI era is creating positive business outcomes through their products, and suggests entrepreneurial experience—such as creating a monetized app—as one way to build that capability.
Leaders should create environments that enable people to do their best work , aiming to give them the highest possible chance of success rather than making them successful; the outcome remains up to them .
An all-in-one PM tool faces a scope-definition challenge: the post assumes some “obvious critical PM work” still needs a human , while a commenter says both that work and “everything” a PM does need defining, noting that asking two PMs can produce three different answers .
A Paytm payment UX failure illustrates the risk of unclear transaction states: after the user entered a UPI PIN without mobile data, the app appeared stuck; a delayed ₹50 payment to the same onion vendor later confirmed after another payment had been made, resulting in a duplicate that the vendor refunded. The author recommends clearly signaling connectivity loss and pending payments, warning about possible duplicates on retry, and offering contextual options such as recharge or UPI Lite for eligible users.
- A final-year CS student with a three-month PM internship reported receiving no interviews after 50+ off-campus applications; a commenter with two years of product experience also reported getting no calls, though these are individual accounts rather than broad market data.
- Resume advice in the thread: describe specific internship projects and their impact, quantify outcomes where possible, name tools or methods such as Agile and user research, and include relevant coursework or skills such as UX design and market analysis.
- A former bootcamp host proposed running a free PM bootcamp, citing the number of people facing layoffs; the plan was to curate exercises and study material and recruit volunteers to help promote it and manage a WhatsApp group.
A PM is building a free platform for simulated product-management case interviews with feedback, aiming to address the difficulty of finding live practice partners; the builder says existing online options do not provide a sufficiently realistic case experience. The platform is still in development.
- For startups, build security into product delivery from the start: continuously test in parallel with releases and create an automated engineering, testing, CI/CD, and DevOps pipeline that can apply patches; the speaker says this is more feasible for new companies than for legacy codebases.
- For AI products, route tasks through a cost- and capability-aware model pipeline: use deterministic checks and cheaper models first, escalating to more expensive models for harder judgments instead of running the most advanced model on every event.
- For security roadmaps, prioritize continuous scanning of unauthenticated internet-exposed attack surfaces, then layer in deeper penetration and code analysis according to urgency and budget; a one-off code scan is not sufficient.
In one self-reported Fortune 10 case, a move to Global Product amid a shift toward centralized product leadership left a PM with little clarity or future projects, while PMs competed for a limited set of projects; their coach had met with them once in four weeks.
A PM reports setting up Copilot Studio agents for deck building, executive communications, and market research, and asks which other skills or sub-agents work well for PMs; the post offers no recommendations or outcomes yet.
A Fortune 10 company’s reorganization moved toward central product leadership after previously dividing responsibilities across US, International, and Market teams; the poster hoped a move from Market to Global Product would enable end-to-end ownership, but reported little coaching and no clarity on future projects amid perceived competition for a limited project set. A commenter described a separate version of role ambiguity: despite handling three major products, they had two managers and no direction on who had asked them to do the work.
A Fortune 10 PM reports that a reorganization moving them into Global Product amid a shift toward centralized product leadership left them without clarity or future projects, while PMs competed for a limited project pool; their coach had met with them once in four weeks.