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Every B2B company hits the same inflection point — enterprise customers show up and they need SSO, directory sync, audit logs, and role-based access before they'll move forward. Most teams lose months building that infrastructure. It doesn't have to be that way.
When a key employee leaves, their judgment and pattern recognition usually walk out the door with them. Digital twins are being repurposed from manufacturing (where you build a digital copy of a physical asset) to capture that kind of workflow knowledge, things like quote-to-cash, onboarding, any process that lives in someone's head, so an agent can run it.
The next $1 trillion company will be a software company masquerading as a services firm. In 2025, the fastest-growing AI companies were copilots. This year, many will try to become autopilots. This will result in a dilemma where selling the work means cutting their own customers out of doing it.
Companies move fast or slow based on information flow. Hierarchy and middle management impede information flow. For thousands of years, there was no alternative to hierarchical systems, and layers were needed. AI removes the need for humans operating in these layers.
Storytelling is a skill that compounds. It starts with articulating your story to yourself, then editing and clarifying it until the story becomes both simple and specific. As your story gets clearer, everything downstream gets easier. This takes time, so find opportunities to practice.
Most teams treat GTM like a junk drawer of sales scripts, one-off automations, and whatever worked last quarter. Teams should treat GTM like an actual product, with memory, workflows, guardrails, and version control baked in. That's where "vibe marketing" needs to end up if it's going to last.
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Most startups outgrow their CRM before their Series B. Salesforce Launchpad [11] gives VC-backed startups free and discounted access to Slack, Starter Suite, and Agentforce. Founders also get GTM coaching and an exclusive community of like-minded peers. Join Launchpad for
Stripe Projects lets you or your agents provision multiple services, generate and store credentials, and manage usage and billing from the CLI.
Build AI receptionists that handle customer support, sales, and scheduling.
Integrate web search, Q&A, and model access into applications.
The "Trader Mindset" suggests a cultural shift towards short-term gains over long-term investment, driven by trends like crypto and day trading. This approach explains job-hopping, multiple VC investments in similar spaces, and the quick pivot from web3 to AI. While it can lead to financial success, true greatness and enriching relationships come from sustained commitment and belief.
AI is compressing the value of "hard to do" but not "hard to get." Five moats that still hold up: compounding proprietary data, network effects, regulatory permission, capital at scale, and physical infrastructure. If your moat is complexity or execution speed, it's shrinking. If it's access to something scarce, it's probably getting stronger.
Not the polished "every no leads to a yes" version.
AI coding agents produce features faster than teams can evaluate them.
Consultants should guide clients to solutions by understanding their actual needs, not just executing tasks.
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Most tools are still locked to their own database, blind to everything users already have in Slack, GitHub, Salesforce, Google Drive, and dozens of other apps. That's the ceiling on what you can build.
190 companies presented this week. 85% were AI-first. 14 hit $1M ARR before demo day, the highest ever for any YC batch. The bigger signal here is that the bar for what counts as "early stage" keeps moving. $1M ARR used to be a Series A milestone. Now, it's table stakes for a demo day presentation.
The AI market isn't the same as the Dotcom bubble of the past. The technology is still early, and demand is pulling spending forward. The software selloff is real, but it won't hit every layer equally, as each has its own moats. Across the last few technological revolutions, the companies that became durable winners were predominantly founded in years four and five of each platform shift. We are now in that window for AI.
Every major tech shift creates a new growth channel. AI is becoming a decision-maker - they're starting to choose products. This new growth channel is embedding products directly into the AI tools that people use. This impacts strategy, product, and engineering. The companies that will be marketing to the interface layers that make decisions on users' behalf.
The hardest problem in AI right now isn't the model, it's the pricing. In traditional SaaS, you could underprice a seat, and it didn't matter because the marginal cost of serving another user is basically zero. Now every agent action, every "magic" feature triggers real inference costs on the backend. Price too low and you're literally paying customers to use your product. Price too high and you lose to the competitor who figured it out first. Per-token and credit-based pricing models are about to become the default because they're the only ones that align cost structure with value delivered.
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Littlebird [10] observes your screen and meetings, building a private memory that grows with you. No setup, no catching it up. This is what
Mistral open-sourced a text-to-speech model that outperforms ElevenLabs on benchmarks.
Verify Humans with Short Video.
Practice and improve startup pitches with feedback.
Antifragility is the premise that some things, including people, improve from stressful situations. Disorder and uncertainty are the rule at startups. Over time, antifragile people will consistently improve their value/skills due to the continued exposure to a startup's disorder.
Software companies have three paths right now: accelerate revenue 10%+ YoY through AI-native products, get stuck in the messy middle, or rebuild to 40-50% operating margins. Even the profitability path is deceptive. Most companies that look profitable on FCF margins are still in the messy middle once you account for stock-based compensation. The only real option is to accelerate. If you're not rewriting your product around AI, you're choosing a slow death.
There is surprisingly little written about working with large organizations in the age of AI - this post attempts to fill that gap.
Heidi Sturrock's broad match campaign mistake unexpectedly became an advantage by turning competitor calls into sales leads.
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Metronome [4] is the platform that leading companies like ANTHROPIC and CONFLUENT use to experiment with and scale billing.
Intercom's Fin agent is objectively the highest performing, fastest, and cheapest model for customer service. The model resolves almost two million issues per week, a number that is growing exponentially. It resolves customer issues at a higher rate than any other model available. The model has already grown to nearly $100 million in recurring revenue in its short life.
The gap between the early adoption and maturation phases is closing fast. The early adopters of AI have gotten the industry to where it is now. The next phase of growth will come from users who are curious but skeptical, willing to try but not interested enough to figure out how to work around the rough edges of the technology. This audience will determine whether AI delivers on its productivity promises.
AI features introduce variable costs that scale with user engagement. This disrupts the near-zero marginal soft model of traditional subscription apps. The most successful businesses are designing their entire systems around the economics of usage. Teams need to manage AI the same way they manage acquisition spend or infrastructure. Consider whether the AI feature is worth the cost - the improvements from implementing AI need to be measurable.
Most industries still can't define what an "agent" actually is. You can't build what you can't picture. This post lays out the six things still missing before agent-to-agent economies work: a communication layer (MCP/A2A), vendor-agnostic infra, dynamic auth, a payments layer, a marketplace, and real benchmarks. OpenClaw expanded imagination, but it's still the first 1%.
Founders in AI face heightened competition from incumbents, startups, foundation models, and empowered customers, risking obsolescence if their products remain at the workflow level. Companies building horizontal applications for high-agency, high-capability customers are particularly vulnerable, as these customers can easily replicate such tools. Founders must prioritize deep domain mastery, addressing human-centric problems, and developing chassis products to maintain a competitive edge.
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Your AI skills observe their own failures, figure out what broke, and fix themselves.
Create and manage task-specific AI agents for workflows.
The SaaS playbook was simple - own one workflow, expand from there. AI companies are doing the opposite - they're expanding into adjacent categories almost immediately because the cost of building is near zero. Models change every 42 days now, and buyers would rather trust one platform for 3-5 years than stitch together a best-of-breed stack that's outdated by next quarter.
Companies under $100M ARR are accelerating again, likely driven by AI-native companies growing abnormally fast. CAC payback period is the metric to watch - revenue durability is uncertain, and shorter contract lengths are becoming the norm. GTM efficiency on AI products with 30% gross margins looks very different from that of traditional software with 75% margins.
Elon Musk succeeds because he commits early in markets where GPUs, energy, capital, and manufacturing are constrained.
Storytelling is a crucial leadership skill that ensures team alignment and clarity.
The next era of product development is built on context and agency.