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Some founders are living on the edge of what's possible with AI models, shipping new features whenever model improvements enable them. Others bet their companies in public and tell stories about their ongoing transformation. A third group turns their hires into headlines. The rule underlying these three ideas is that you only earn the right to build in public if you're doing something worth talking about.
Mitchell from Shown Media argues the invisible pattern behind Claude, OpenAI, Google, and Higgsfield is not researcher pay or fundraising but a release cadence that turns every ship into an event, executed through a system most companies do not have. Step one is the viral launch, and step two flips the typical model where marketing receives finished features and ships a blog post: top companies give marketing a seat at the monthly roadmap meeting to decide what ships against what is trending in the zeitgeist, with Anthropic and OpenAI running 10+ small releases per month that kill entire industries on launch.
37signals runs almost entirely on writing instead of meetings, and Jason Fried just published the full guide to how. The bet is that long-form, written, async communication beats talking for nearly everything that matters. You can feel the philosophy in the rules of thumb: meetings are a last resort, writing solidifies while chat dissolves, a one-hour meeting with five people is really a five-hour meeting, and ASAP is poison.
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Later this month, Plane co-founder Staszek Kolarzowski (YC '17) will give a founder-to-founder webinar [13] showcasing the AI engine he built to run sales targeting, call coaching, and commissions that scaled to $Ms in ARR. Join to see what's in production and the hard-earned lessons that shaped his systems. Register to watch live or
OpenAI's Codex agentic AI platform recently received an update that introduced domain-specific workflows, a web hosting feature called Sites, and an in-place editing tool named Annotations. Sites can convert static data inputs or text documents into functional, web-hosted internal applications that users can share with their colleagues. Annotations enable more precise agent AI spreadsheet edits and updates. It eliminates full-document regeneration, which often results in broken formatting or introduced hallucinations.
OpenAI will start offering early access to conversion-optimized campaigns on June 5. Accounts that have conversions set up by June 1 will have access to the feature. A screenshot of what the option will look like in the settings is available in the article.
Workday spent nearly two decades getting HR and finance to speak the same language, then AI came along and changed the climate. The company spent $1.1 billion to acquire an AI interface layer to adapt to the change. Every control point in enterprise software is now operating in an environment changed by AI. Companies need to look at which points weaken in the new climate, and which ones thrive.
Conviction's Mike Vernal argues the canonical enterprise software playbook is dead because the framework implicitly assumed calendar time that founders no longer have. Statsig wedged into experimentation before adding feature flags, session replays, and product analytics, and Rippling wedged into onboarding orchestration before stacking HR, benefits, and recruiting. Each previously needed 3-5 years to clear Act I and another 3-5 to layer the suite.
Some customers only stay because there aren't any alternatives.
A full overview of the latest batch of startups in Y Combinator's Spring 2026 cohort.
This post looks at the patterns an investor saw inside a startup that led them to believe that investing was the right choice.
Garry Tan thinks most of the code in your AI app exists for one reason: you don't trust the model yet.
A poll tells you what people say, a market tells you what they'll actually put money behind.
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So many founders do most of the legwork but never reach the finish line. You know the list, you've nailed the messaging, but you can't find the four hours it takes to build the sequences, find the contacts, and send them.
If AI made your main feature free, what would be left of your company? Apps that just wrap a model are easy to replace. The ones that last own a number their customer cares about and keep making it better. Like spreadsheets, cheaper AI will create more work, not less. Labs will build a generic support agent that handles 30 to 40 percent of tickets, but you win above that by knowing the messy details labs won't bother learning. Meta is proof that advertisers pay for sales, not for the dashboard.
The platforms that win the next era will look far more like hyperscalers than the SaaS platforms of the past. Companies that centralize important data are in the running to be a platform in the AI era. The last generation of winners are committing the cardinal sin of starting with the platform before they've built the killer app. Doing this almost guarantees they will get the abstractions and interfaces wrong because they're optimizing them against a usage pattern that doesn't exist yet.
The best B2B software companies of the next decade will build distribution from day 0. AI's development capabilities mean that the durable advantage has moved to audience and the distribution built to reach that audience. Markets are being won earlier and more convincingly than ever. A business' go-to-market is what builds their moat and dictates their growth trajectory.
Counting tokens measures effort, not results, just like counting hours worked. Companies are done experimenting with AI for free and now want proof it pays off. The first phase was cheap because labs ate the cost. Now, they charge real money, and CFOs are asking hard questions at renewal. Smart buyers use cheaper models for easy work and save the expensive ones for tasks tied to revenue. AI usage will keep growing fast, so the real question is who actually makes money from it.
Your best channel might already be getting worse. Treat marketing like investing. Every channel works for a while, then slowly stops paying off as you spend more. The real question isn't, "Is this channel working?" It's, "Is the next dollar still worth spending here?" The biggest wins are usually small and don't scale, like one newsletter sponsorship that converts at 8x. An "influencer marketing" hire is really three jobs in one: outreach, creative, and budget decisions.
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Headroom compresses everything an agent reads before it reaches the model so that users receive the same answers at a fraction of the tokens. It is suitable for running AI coding agents and working across multiple agents that have shared memory. Reverse compression is always achievable via CCR. Headroom doesn't work in sandboxed environments where local processes can't run.
Deliberate records every option an agent considers, rejects, and executes and provides an audit trail that users can export. It is built for teams running LangGraph or OpenAI Agents pipelines in production. Deliberate wraps agent loops and writes a complete record that compliance teams can sign off on.
There's a strange wedge hiding here, building the hard tasks labs use to test their agents. A convincing fake of SAP, realistic enough to evaluate an agent inside, was rumored at $500,000, and METR says it can barely find long-horizon tasks good enough to use, with decent ones at $20,000 each. Cheap labeling is everywhere, but captured expert work that's realistic enough to fool an agent and clean enough to grade is scarce, so the eval bottleneck is domain realism, not model access.
Eleanor Dorfman, Anthropic's Head of Industries, recently walked through the company's GTM stack at SaaStr AI Annual 2026. The company uses the same six tools that many other leading B2B + AI companies use, as well as Jira, Intercom Fin, Snowflake, BigQuery, and G Suite. This article details what the company does with each part of its stack.
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AI will change the relationships customers will have with financial institutions, whether they decide to build anything with AI or not.
Organizations that have a culture of real writing will outperform those that do not.
AI is scaling engineers' ability to create products and features, but it isn't scaling customer attention.
If your AI bill is growing, the real question is no longer “are people using it?” It is “what business outcome did those tokens buy?” The useful idea here is token-to-outcome tracking: cost per resolved ticket, processed claim, reviewed contract, avoided hire, or dollar of revenue moved. For founders selling AI into companies, usage is not enough. The buyer will soon want proof that inference spend turns into work that matters.
AI is transforming software by replacing static workflows with adaptable intelligence, creating a need for robust AI harness systems. These systems comprise seven components: context retrieval, tool integration, orchestrated workflows, state management, secure computing sandboxes, observability, and cost optimization. Companies effectively managing these elements will dominate the emerging competitive landscape in software.
Gokul Rajaram contrasts his Facebook days where engineers shipped multiple daily updates without explicit QA gates versus Square where the team pushed one app update every few weeks behind human QA, alpha, and beta gates, arguing the difference was a deliberate cultural trade-off rooted in how critical each product was to customers' lives.
A good demo can be the difference between success and failure. This post presents a list of what sets S-tier demos apart from the rest. It covers the basic structure of a demo, storytelling tactics, setup and delivery, and more.
The requirement to understand the user was true when SEO was purely about traditional search. It is even more true now that AI has substantially altered the user journey. Businesses need to confirm whether users who would benefit from what they have to offer are actually searching for it, and whether what they find when they arrive matches the performance the business delivers. If either is not true, they need to redirect their search budget.
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Plane co-founder Staszek Kolarzowski (YC '17) will give live demos [10]of the AI engine he built to run sales targeting, call coaching, and commissions that scaled to $Ms in ARR in this live webinar. Join to see what's in production and the hard-earned lessons that shaped
Anthropic has introduced a new dynamic workflows feature in Claude Code that helps Claude take on challenging tasks end-to-end. Dynamic workflows can handle problems too big for one pass by a single agent, dynamically writing orchestration scripts that run tens to hundreds of parallel subagents. It is available now in research preview for users on Max, Team, and Enterprise plans and on the Claude API, Amazon Bedrock, Vertex AI, and Microsoft Foundry. The feature consumes substantially more tokens than usual, so users should start on a scoped task to get a feel for usage.
Amazon OpenSearch Serverless is a fully managed search and vector engine designed to build agents. It now auto-scales 20 times faster and provisions resources in seconds. The latest update introduces complete decoupling of compute and storage through a new shared storage layer, so customers can scale compute up and down independently. OpenSearch Serverless now features native integrations with AI development platforms, so developers can provision search infrastructure directly from their development environments using natural language commands.
The General Partner model and the startup model are not the same game. Startups are supposed to chase huge, ambitious targets, knowing that most who try will fail. General Partners run diversified portfolios specifically so they don't lose their liquidity providers' money. Many founders grind themselves down trying to get their investors some kind of return when the investors have already written the company off a long time ago. These founders are better off shutting it down and figuring out the next thing.
The defensibility question can become a trap at seed. Most great companies do not start with a perfect moat. They build one by learning faster, hiring better, earning trust, and executing for longer than everyone else. The founder takeaway is not to ignore moats. It is to know when “what stops OpenAI from building this?” is a real risk, and when it is just a lazy way to underrate speed, taste, and customer obsession.
YC CEO Garry Tan reframes what Y Combinator actually delivers by borrowing psychologist Alice Miller's concept of the "enlightened witness," the single strongest predictor of whether traumatized children grow into functional adults: not someone who could fix or rescue them, but someone credible enough to confirm their reality.
Unusual Ventures Director of Programs & Community Niamh O'Donnell shares the seven-step playbook from arriving in San Francisco at 24 on a one-year Irish visa with no tech network to hosting rooms full of founders, investors, and Fortune 500 leaders five years later.
A full-time hire is a bet that the same work will keep showing up.
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Application companies need to ask themselves whether AI agents will still need them in three years. Many companies rely on custom workflows that assemble several pieces together, something AI can easily route around. Companies need to think about the moats that aren't easily copied, like proprietary data that took years to accumulate, regulatory positioning that's expensive to replicate, and relationships with customers. Adapting likely means changing what you sell, who buys it, and how you charge. Changing one of these is already hard, and doing all three is something only a small number of teams are capable of.
There are three real flavors of AI services right now, and the bet to take isn't a body shop of elite generalists or the labs' own consulting arms whose engineers quietly work for their sponsor's lock-in. It's the inverted shape, a product company that delivers a service, where you build an internal agent and context layer and use it to serve one narrow customer cheaper and faster than anyone else can. The tell is whether they'll do anything for anyone, or whether they have religious beliefs about what they won't touch. The next Accenture isn't Accenture with AI on the front of the brochure, it's a company that rebuilt how services get delivered from the ground up.