1. US AI policy for builders: open source affirmed, national standard sought. The White House OSTP director confirmed the administration "strongly supports" open-source and open-weight models, citing the US AI strategy (released last July), whose chapter one commits to a "vibrant closed- and open-source ecosystem" as necessary for US AI leadership Y Combinator. The same week, Y Combinator signed a letter from large companies and helped organize another letter to the White House opposing restrictions on open-weight models, driven by rumors of an executive order Y Combinator. On regulation, he argued a patchwork of state AI laws is survivable for big tech (which can hire "an army of lawyers") but not startups, so the US needs "one national standard" for AI; regulatory clarity would be "the biggest boon" for startups, and he urged Congress to pass federal preemption legislation Y Combinator.

2. Regulatory philosophy: avoid firm red lines for fast-moving AI. Fixed thresholds "do not stand the test of time": the EU AI Act was finalized before ChatGPT existed yet now governs LLMs, and the prior administration's hard compute-cap disclosure thresholds are another example; once government sets a line it is very hard to reset, so policy should move with the frontier Y Combinator.

3. Framework: "born free" vs "born in captivity" technologies. Technologies with no existing regulations ("born free," like the early internet) should be preserved by avoiding new regulation, since anyone can build in them; "born in captivity" technologies require government approval to commercialize — e.g., commercial drone operations need an FAA waiver, and supersonic flight needs a noise limit instead of a speed limit — and policy work focuses on removing those barriers Y Combinator.

4. AI risk assessment: capability trade-offs, bio risk "overblown." On frontier-model releases, labs weigh guardrails to limit dangerous capabilities; the same model capabilities that enable misuse also help harden systems — an inherent trade-off Y Combinator. The biological-risk threat has been "a bit overblown" and has not been an issue for three years, though test/eval infrastructure is needed as models pass the frontier Y Combinator.

5. Opportunity areas: autonomous science infrastructure and quantum. The administration's "Science: A New Golden Age" report sketches AI agents posting boundaries for experiments, contracting robotic cloud labs, and settling results on a ledger, with the government's job being "to shape the arena, not direct discovery" Y Combinator; the OSTP director described the needed robotics and software ecosystems for autonomous experimentation as nearly "infinite category of work" for builders Y Combinator. He also named quantum computing the next wave — with a just-signed executive order and a Department of Energy goal of a scientifically relevant quantum computer by 2028 Y Combinator.

@lennysan launched Lenny's Jobs (lennysjobs.com), a curated tech job directory focused exclusively on four builder roles—PM, engineering, design, and growth/marketing—with every company vetted and ghost roles/staffing-agency posts filtered out @lennysan. He built it after concluding that career advice means little without a job you're excited about @lennysan. Features include: unlisted roles sourced from audience polls and the private reader Slack community @lennysan; the Lenny 100, a curated list of top-100 companies with high talent density, ambitious problems, and active hiring @lennysan; per-job AI tools for fit rating, interview prep, outreach plans, and resume customization @lennysan; an AI interview/career coach for pitches, mock interviews, debriefs, compensation talks, and offer negotiation @lennysan; an AI agent emailing matching new jobs (paid-subscriber exclusive) @lennysan; rich company data (funding, layoffs, headcount, comp, sentiment) plus 100+ tech-specific filters @lennysan; and curated third-party interview/career resources @lennysan. The site is free; paid Lenny's Newsletter subscribers unlock higher AI limits, unlimited personalized results, and the proactive AI agent—built with Amit Taylor of TrueUp @lennysan. Future plans include white-glove recruiting, personalized career coaching, and data-backed state-of-the-job-market reports @lennysan.

AI lets anyone on a product team build almost anything, but that doesn't make the team obsolete — it makes it more important. The hidden change: AI makes collaboration optional, removing the checks and balances that slow handoffs once forced product, design, and engineering to work together Ravi on Product.

Case study: For a large nonprofit replacing its membership/fundraising platform, Matthew Mamet produced a North Star doc and rough wireframe in two days with AI (previously a designer and at least a week). At review, a membership operations leader found one feature already live and another impossible to build; AI had invented two features. Trust collapsed in the wireframe and the entire process — a step that was once taken for granted (deep system knowledge catching hallucinations) had disappeared Ravi on Product.

'Ship it and see' is acknowledged as a strong AI-native argument but flawed: lower experiment costs don't yield more truth — even ideal 95% significance accepts a false positive 1 in 20 (1 in 10 at 90%), and dashboards only record behavior after launch; quality was decided earlier. It has 'the shape of rigor without the rigor.' Good enough to test isn't necessarily customer-worthy: PMs, designers, and engineers each spot what good enough conceals, and that judgment improves experiments before customers see them Ravi on Product.

Team model: old product team = orchestra (PM conductor, OKRs as sheet music); AI-native team = jazz band where lead passes between players, enabled by a shared standard — a playbook everyone knows cold, with chord changes not renegotiated mid-solo. A shared standard must tell the team what good looks like, what's ready to ship, what everyone else is building, and what gets cut; without it, the room fills with competing solos Ravi on Product.

Full-stack builders need an honest map of where their range ends; specialized judgment remains the difference between capable and exceptional work because customers benchmark against the best software they used that morning. The question to ask: 'Does this deserve the customer's attention?' Ravi on Product.

Practical fix: set the team's launch bar via a product review meeting with a small internal group and technical leads, no business stakeholders, before anything goes to stakeholders. This ten-minute rule restored cross-functional vetting and held for the rest of the engagement. Without a shared bar, the lowest threshold wins — the person most comfortable shipping becomes de facto quality standard Ravi on Product.

Shift from prioritization (choose what to build) to curation (choose what to ship). When building cost approaches zero, 'we can build it' stops being a filter. Strong teams make the filter explicit: 'Every release must materially advance this customer outcome. If it does not, keep refining it—or do not ship it.' Ravi on Product.

Before the next planning cycle, answer three questions: 1) Where has AI removed a handoff that used to bring in another discipline? 2) What can now reach a customer without the benefit of a specialist's judgment? 3) What shared standard tells the team whether the work is good enough to ship? Ravi on Product. After the nonprofit's review-first rule, drafts went back to a smaller room of people who knew the customer and the system; stakeholders saw fewer, stronger ideas while extra AI speed went into more passes backstage — the advantage moves to teams that turn abundant creation into customer-tuned work Ravi on Product.

Lenny Rachitsky's podcast episode with Ian Silber, OpenAI's Head of Design, argues that designers are currently the unhappiest people in tech yet this is the best time in history to be a product designer. The episode covers Silber's theory for designer unhappiness, his hot take that AI is already an incredible product designer, how OpenAI designs at two speeds, and his views on craft, taste, and what will be left for humans (https://youtu.be/BV0hy6NET-U) @lennysan.

In a clip from the episode, Silber says: "As more and more software is made, and more and more products are made, I believe that the human element of design will become more and more important" @lennysan — a signal that human-centered design is expected to gain value as AI accelerates software and product creation.

• In his new book Artificial Organizations, Barry O'Reilly argues AI isn't replacing leaders, it's exposing them: the core of leadership is judgment and decision-making, and AI should pressure-test thinking rather than provide answers — deferring agency to machines erodes judgment. The book's starting point is understanding how you do your best work and make decisions, not picking tools One Knight in Product.
• Adoption reality vs hype: 61% of senior leaders in a survey of O'Reilly's mailing list call themselves AI beginners; CFOs report <1% usage relative to Microsoft Copilot license spend (~$1,000/user/month, $1M/month for a 1,000-person company). His advice: you're not as behind as the feed says, but don't freeze — start with small experiments and workflow automation One Knight in Product.
• Anti-slop policy: because AI makes output cheap, people push processing work onto others — CEOs now receive 10–20 documents a day for review. O'Reilly urges leaders to treat generating documents without doing the thinking as disrespectful; instead show up with the work done, options weighed, and a recommendation One Knight in Product.
• Case study at Progyny: CEO Pete Aineski modeled AI experimentation (weekly shares of what worked/didn't) and framed tools as 'elevate not eliminate people.' The CHRO's HR onboarding chatbot — built by an intern with a ChatGPT interface — cut ticket response times from 3 days to <16 hours and fed policy questions back into benefits decisions One Knight in Product.
• High-value use case for PMs: an 'executive GPT' that simulates a time-poor CEO's disconfirming questions helps people prep for one-on-ones; O'Reilly frames the thinking-partner pattern (ask the LLM to attack assumptions and run scenarios before meeting) as a big unlock for product managers advocating at the top table One Knight in Product.
• Meeting discipline: O'Reilly has used a meeting co-pilot since 2016 to capture every conversation as a data asset, send synthesized actions and 24-hour pre-meeting nudges, and show up present — a calm state he says is essential for product leaders facing complex, high-stakes decisions One Knight in Product.
• Getting started: pick one upcoming decision, write out your current decision process (inputs, counter-arguments, data points), codify it in an LLM, and first ask the model how to make the process more robust before filling in the blanks One Knight in Product.

Freelance/contract PM roles: state of the market and how to break in

• Freelance PM work exists under the labels contract or fractionalr/ProductManagement. Fractional roles are usually more strategic and require domain expertise — candidates need a niche and something concrete beyond product buzzwords like roadmapping r/ProductManagement. Contracting also demands the ability to jump into a new company/product and get oriented fast r/ProductManagement.
• At ~4 years' experience, most PMs are too early for fractional roles: the realistic pool is fractional work in a niche you have 1+ years in, in an org shaped like one you have worked in, and you compete with candidates with 2–5x your experience r/ProductManagement.
• The market is currently weak. A 20+ year freelance PM says rates are down, 4 years is considered junior, and even a resume with three same-industry projects with big names now only gets a first interview (vs. a guaranteed project at above-market rate 10 years ago); long-term freelancers typically get contracted by large enterprises through third-party vendors/body leasing and compete with seniors willing to work for very little r/ProductManagement.
• An industry veteran predicts IT PM has become oversaturated and may never return to prior equilibrium due to AI replacement, M&A-driven consolidation, and market pressure — with the consulting industry also hurting. They expect possible hiring near year-end from companies that tried AI, were unsatisfied, and budgeted to rehire humans r/ProductManagement +1.
• UK-specific caveat: a UK PM contractor calls the PM contract market "very weak," partly blaming the British government and HMRC, and says it may be UK-specific r/ProductManagement.
• Where to look: fractional roles come through your network; contract roles are advertised on job boards like full-time jobs r/ProductManagement. Upwork has some opportunities, but they resemble regular jobs more than freelance r/ProductManagement.
• Anecdotal Upwork path to six figures: start with short-term contracts doing concrete PM tasks (backlog organization, structured PRDs, market research/competitor analysis), then position your profile around one part of the PM process or a niche, eventually becoming a 0-to-1 expert; short contracts can convert into part-time then full-time work r/ProductManagement +1.

Lenny Rachitsky announced the launch of Lenny’s Jobs (LennysJobs.com), a job directory for tech roles focused on four builder roles: product management, engineering, design, and growth/marketing @lennysan. It aggregates open roles from top startups and big tech companies, vetting every company and filtering out ghost roles and generic staffing-agency posts—which he calls 'the highest-quality directory of open tech roles you’ll find anywhere' @lennysan. He chose these roles because they're what his readers look for and because 'these roles are starting to meld together' @lennysan. Launch rationale: after years of career advice, he wanted to help people who are struggling to find a job they love, spending six months thinking about what more he could do @lennysan.

• A user reported a dark pattern in an AI/API product (identified as Claude in a commenter reply): their monthly spend limit was silently raised from $0 to $2,000 during a promo that also granted 2x usage and a $100 credit, after they had deliberately disabled top-ups; the change was not prominently disclosed and they noticed only after $17 of the credit was consumed. Other users reported surprise API charges of ~$40–$50, adding up to $2k+ r/ProductManagement +1.
• A PM who worked on model distribution at big tech explains why transparent billing can lag: billing APIs were immature in 2024/25 and couldn't get funded, so teams refunded customers who accidentally incurred large bills; top-up billing was historically viewed as a UX improvement in enterprise SaaS (IT teams sometimes requested spend limits as a buffer), and accurate token measurement under dynamic pricing is hard, pushing startups to simple billing models or manual processing r/ProductManagement.
• Ownership of pricing/spend limits varies: one PM says it's not handled by their PM org r/ProductManagement, another describes a dedicated GTM team owning pricing, trials, and overages with PMs only partially involved r/ProductManagement, and a consumer PM says they partnered with sales/product/marketing on subscription and promo decisions r/ProductManagement.

Lenny's Newsletter launched Lenny's Jobs, a free job directory focused exclusively on four builder roles — product management, engineering, design, and growth/marketing — aggregating open roles from startups and big tech, with every company vetted and ghost roles/staffing-agency posts filtered out Lenny's Newsletter.

The directory adds unlisted roles sourced from audience polls on X and LinkedIn and Lenny's private reader Slack community, tagged "Lenny's Community" Lenny's Newsletter, plus "The Lenny 100," a regularly updated list of the top 100 companies Lenny would most want to join, judged on talent density, ambition of problems, upside potential, and active hiring Lenny's Newsletter.

Each job post embeds custom-built AI tools for rating fit, interview prep, outreach planning, and resume customization Lenny's Newsletter, and a custom AI interview and career coach (built on Noam Segal's AI skills) covers pitch building, mock interviews, debriefing, compensation conversations, and offer negotiation Lenny's Newsletter.

Job posts include rich company data — funding, layoff history, headcount growth, compensation, employee sentiment — and 100+ tech-specific filters (investors, valuation, tech stack) Lenny's Newsletter, plus curated third-party interview and career resources Lenny's Newsletter. An AI agent emails newly posted jobs matching your criteria and is the only feature exclusive to paid subscribers Lenny's Newsletter.

The site is free; paid Lenny's Newsletter subscribers get higher AI feature limits, unlimited personalized results, and the proactive job-matching agent Lenny's Newsletter. It was built with Amit Taylor (TrueUp) Lenny's Newsletter; planned next steps include integrated white-glove recruiting intros, personalized career and skills development, and ongoing state-of-the-job-market reports Lenny's Newsletter.

Engineering scarcity used to filter ideas: weak ideas died in backlogs because they required too many engineers, so teams didn't have to decide whether they deserved to exist @hnshah. Cheap software/AI removes that filter — an idea can become a credible prototype before anyone decides whether it deserved the meeting — so product people must make the "does this deserve to exist" call themselves @hnshah.

Two questions to ask before building: (1) "What should somebody do differently if we get it right?" — start with the desired behavior, not the proposed solution, since a dashboard, AI assistant, or notification is already an answer @hnshah; (2) "If this works, what exists tomorrow that didn't exist today?" — look for mechanics that accumulate value with repetition: eBay feedback gave strangers history, Stitch Fix learned taste, and with agents, each delegation should leave context behind and give trust reason to increase @hnshah.

Visible features don't travel between products: Instagram Stories became a massive behavior while Twitter Fleets didn't increase participation. What matters is what had to be true for the behavior to work — graph, habits, frequency, identity, feedback, audience expectations — so investigate that before borrowing @hnshah.

With AI, a convincing prototype no longer carries a hidden signal of commitment ("months of thought or forty-five minutes with an agent"), but it still makes the question real — "behavior wins the argument" @hnshah.

AI also makes complexity cheap to add: an agent can build the sixth way to do a task without feeling the settings page getting heavier. Someone still has to hold the whole product and notice when a locally good idea makes the system worse @hnshah.

The future product work may concentrate on the gap between generated possibilities and shipped features: capable companies will "prototype constantly while far fewer things earn their way into the product," and choose not to ship software that is possible, useful, and completely functional — something can be all three without deserving to exist @hnshah.

OpenAI's Head of Product Design Ian Silber says many designers are "very uncertain" because engineers have 10X'd or 100X'd their productivity while design teams haven't: the design process still takes time and is messy and iterative (you try an idea, it sucks, internal feedback is wrong), and though AI compresses it by letting teams get more ideas out fast, the full feedback loop is still required — leaving designers unclear about what is expected of them @lennysan. In the linked episode, Silber discusses why designers are the unhappiest people in tech, why he thinks this is the best time in history to be a product designer, his hot take that AI is already an incredible product designer, how OpenAI designs at two speeds, and how he thinks about craft, taste, and what will be left for humans @lennysan.

• Engineering scarcity used to quietly filter mediocre ideas; cheap AI software removes that filter, so product teams must actively decide what deserves to be built @hnshah.
• New first question: before discussing what to build, ask "what should somebody do differently if we get it right?" — start with desired behavior, not proposed solutions like dashboards or AI assistants @hnshah.
• Study teams/products that solved similar human problems (marketplace trust, GitHub async review, social contribution); copying visible interfaces doesn't transfer behavior, as Instagram Stories vs Twitter Fleets shows @hnshah.
• Build enough to make the belief real and observe what people actually do; behavior is the evidence ("behavior wins the argument"), even though AI makes prototypes cheaper and less meaningful as commitment signals @hnshah.
• Second question: "If this works, what exists tomorrow that didn't exist today?" — design for accumulating value (eBay feedback, Stitch Fix taste learning); agents should leave behind better context and increasing trust with each delegation @hnshah.
• Every addition adds complexity; agents can build redundant features without experiencing their cost, so someone must hold the whole product and decide when a locally good idea makes the system worse @hnshah.
• Summarized approach: stay with behavior, find structurally similar problems, understand why their approach worked, build testable beliefs, watch what accumulates, make ideas pay for complexity, and remember the reason for leaving things out @hnshah.
• Expectation: companies will generate far more ideas with agents but ship fewer; product work concentrates on the gap between possible and shipped @hnshah.
• Shah's companion tweet frames this for "anyone who can now build faster than they can learn what people actually want," using agents to make being wrong cheaper @hnshah.

• A founder of a two-person, all-engineer team describes abandoning dedicated PM SaaS: after a previous 10-person startup used Linear, this team skipped it, built an agent-run HTML board, then moved underlying tracking to plain GitHub issues. They argue small teams no longer need paid PM SaaS tools, conceding it may differ at bigger teams r/ProductManagement.
• The custom agent-run board worked for a few weeks, then "quietly stopped working": as specs/tickets grew they lacked structure and a real lifecycle, so outdated docs/decisions/specs accumulated and confused the agent. The team reverted to GitHub issues for lifecycle tracking, keeping the custom HTML board as a synced UI r/ProductManagement.
• Counterpoints: GitHub issues are themselves PM SaaS and the vibe-coded replacement didn't work since tracking ended up in GitHub r/ProductManagement; the OP clarifies he means the paid PM SaaS category specifically — fancy project-management features/UIs — as no longer needed r/ProductManagement.
• Other PMs echo the trend: a PM says small-to-mid teams don't need dedicated PM SaaS if they have good AI tools, GitHub, and Slack/Google Docs/Notion, and that Claude Code and Codex do most of their PM work r/ProductManagement; another says teams really only need PM functionality for visibility and comms, not fancy software r/ProductManagement; OP adds that in the "agent era" even a 10-ish-person startup might not use Linear the same way r/ProductManagement.
• A commenter predicts this is the future of software: products that are just a "fancy UI on top of tabular data" have their days numbered, since users can vibe-code their own UI and pay for tools like GitHub that do genuinely complex work r/ProductManagement.

Lenny Rachitsky launched Lenny's Jobs (LennysJobs.com), a job directory focused exclusively on four builder roles at tech companies: product management, engineering, design, and growth/marketing @lennysan. It aggregates open roles from top startups and big tech, vets every company included, and filters out ghost roles and generic staffing-agency posts @lennysan.

• @mehul of Matic claims that after 9 years, 11 prototypes, and their life savings, they learned "every great product has the same design process"; Matic is now "decisively the best home robot for families" (noting bias) @mehul. He teases a 500-word thread and video on this "Universal Design Process behind every great product" @mehul.
• Hiten Shah amplified the post with Steve Jobs' principle: "Start with the customer experience and work backwards to the technology" — suggesting customer-backwards design sits at the core of that universal process @hnshah.

• An API/SDK-focused founder planned to sign paid LOIs with design partners for a ~3-month build, with a refund if unsatisfied, and asked whether paid LOIs/cold DMs are the right way to recruit partners r/startups.
• Make paid-LOI "satisfied" terms boringly specific: agreed satisfaction criteria (e.g., "measured by hours saved on X per week, self-reported at the 6 and 12 week mark"), feedback cadence, and who on the partner's side actually uses the product; vague terms produce vague enthusiasm and silence r/startups +1.
• To find partners, skip generic cold DMs: start with API-first companies already complaining publicly (docs issues, SDK churn, support threads, Discord/Slack communities) and lead with a 15-minute teardown of their current workflow, not the product; niche communities where you can point at the literal problem convert better, and founder communities/API-SDK Slack groups/warm intros beat pure outbound r/startups +2.
• Advice splits on paid vs free design partners: (a) prove the problem is painful before charging — build closely for free or steep discount with 5–10 teams already struggling (done at georankers) r/startups; (b) never work for free — charge design partners (e.g., below eventual price, with refund if agreed outcomes aren't met) because non-paying users won't pay in the long run r/startups +2; (c) for first partners, free access in exchange for weekly calls and real usage on a live project, charging once it delivers r/startups. Early learning can outweigh revenue: close feedback from the right users can save a year of building the wrong thing; charge once problem and value are clear r/startups.
• A developer-tool founder reports the best feedback came from users who found a rough early version and used it because it solved a real problem: build the smallest thing that solves one pain, put it in the subreddits/Discords/HN/GitHub issues where target developers complain, and let interested users opt in — those who reach back are pre-qualified r/startups.
• For warm leads generally, blogs help credibility/discovery but not as a direct lead engine; targeted founder outreach to a shortlist of clear-fit brands with a specific value proposition outperforms broad outbound r/startups.

Founder customer discovery for a developer-infrastructure product is probing API/SDK migration pain — v1→v2 migrations, major SDK releases, endpoint deprecations, authentication changes, legacy shutdowns — to validate how painful these migrations are for providers r/startups. The target users are providers still hit by painful SDK majors, auth migrations, and legacy endpoint shutdowns where customers must modify code; the core problem is getting hundreds of customer implementations through a migration safely, proving behavior didn't change, and minimizing customer-side work r/startups +1.

Practitioners counter that modern API design largely avoids this pain: protobuf-first contracts with numbered fields and versioned service names (e.g., PaymentV1Service), or REST path-prefixed versions (/api/v2/foo), keep additions backward-compatible; HTTP-header-based version negotiation fell out of favor, and painful migrations stem mainly from breaking changes and legacy workflows r/startups +1. One 25-year engineer says AI makes such migrations 'incredibly easy,' and another commenter predicts Claude/AI will destroy the migration-tooling market r/startups +1. A separate view: no migration runbook can exist because too many implementation details combine to produce the only viable strategy for a given company at a given time r/startups.

Strategy warning for this space: an entire offering around versioning APIs/client SDKs lacks enough value to enter a buyer's critical path, and off-the-shelf options (API gateways like Kong, homegrown openresty) already cover adjacent needs r/startups.

For PM hiring, portfolio medium (Figma vs. coded demo) matters less than demonstrated thinking; choose whatever lets you iterate fast and tell a clean story covering problem, users, insights, options, tradeoffs, and impact r/prodmgmt. Code a demo only if it takes under a day and proves a risk or edge case you found; one candidate saw more traction from a Figma sketch plus a write-up heavily focused on problem framing, tradeoffs, and metrics than from a polished build with no clear story r/prodmgmt. Caveat: the PM job market is flooded, so even strong portfolios don't guarantee landing roles r/prodmgmt.

A Senior PM who spent his career in startups joined a large bank's payment gateway team and, after 2 months, found the role had become planning-only: requirements must be cleared by seniors, then pass through a project management team, business technology, and an external vendor, so "even smallest things take months" to ship r/ProductMgmt. He asks whether this is normal in all large companies and senior PM roles, and reflects that startups deliver a regular dopamine hit from executing and experimenting that large companies lack r/ProductMgmt.

The thread's career advice: don't leave the job until you have a new place to land in today's job market r/ProductMgmt. A commenter who worked both sides contrasts governance-heavy big companies, where everyone could (and did) say "no" to almost everything and it was impossible to be effective, with startups where anyone could say "yes" to anything and there was no process; introducing light process at a startup — enhancement request forms and Kanban-based tracking — drew "big company" pushback but quickly won people over because it delivered tracking, prioritization, and feedback from real customers r/ProductMgmt.

Cold-call discovery questions like "What frustrations do you have, and could I build something in software/hardware?" read as a pitch and get prospects shutting down; the fix is to read The Mom Test and stop "pitch slapping" prospects r/startups. Resistance stems from the shape of the ask, not the region: run a "concrete learning conversation" about one specific workflow — "I'm trying to understand how you currently handle [workflow]; I'm not selling anything; could I ask five questions about what happens when it goes wrong?" — one role/workflow at a time, looking for repeated behavior and costly workarounds rather than general pain, and test only when the same workaround surfaces across conversations r/startups.

Other discovery tactics: get a part-time job or internship in the market you want to serve (the DoorDash team drove and delivered) to surface unknown needs r/startups; find problem areas via local news or trade journals, understand root causes, then talk to businesspeople r/startups; build a list of decision-makers and likely users and ask them directly about their problems r/startups; and discover problems "in the wild" from your own industry/network instead of blindly cold-calling r/startups.