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LinkedIn. Crunchbase. CRM. Inbox. Last transcript. Fifteen minutes before every call, hundreds of times a year. "There should be an AI for this" - well, now there is.
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LinkedIn. Crunchbase. CRM. Inbox. Last transcript. Fifteen minutes before every call, hundreds of times a year. "There should be an AI for this" - well, now there is.
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Many AI startups are crushing revenue records because they are using a dishonest metric. Companies are reporting inflated ARR due to long-term enterprise deals they can report now but also opt out of after 12 months. On top of this, some companies are bundling full-time forward-deployed engineers into deals that massively reduce margins and sometimes produce negative margins. A wave of enterprise AI companies will collapse if customers start triggering their opt-out clauses or aggressively negotiate down long-term pricing.
The types of roles at startups are changing. Keeping up with the industry now requires adapting your skillset and evolving. This post puts together a list of some of the hottest roles in tech right now. They include internal automations and efficiency, founding salesperson, labs teams, forward-deployed engineering, creative direction/content, go-to-market engineering, and chief of staff.
Companies need to have a continuously updated, source-grounded, conflict-resolved understanding of who they are, what they're doing, and how they work. This should be delivered as files that any agent can read. This understanding will compound and create value that no amount of money can buy later. It will create a new moat that's proprietary and grows over time.
The hiring problem nobody talks about is that you can't evaluate domain expertise you don't have. You know you need someone sharper than you in a given area, and that's exactly why you can't tell which candidate actually is. The test that works is whether they generate ideas that reshape how you think about the company and whether you'd learn from them in meetings. The sharpest gut check is simpler, which is that if you feel it's easier to just do the work yourself, you hired the wrong person.
Vertical AI startups can compete with legacy incumbents by building customer-centric "systems of love," ensuring standalone product paths, and partnering strategically with non-dominant ERPs. Founders should avoid costly litigation and consider giving up margins if necessary to maintain growth momentum. Structural inertia and poor responsiveness of incumbents create opportunities for new platforms.
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Open-source video rendering framework that lets you create, preview, and render HTML-based video compositions.
Run a self-hosted AI assistant that autonomously plans and executes tasks on a local machine.
Evaluate an app idea by scoring it across key dimensions through an online assessment.
The 2% inflation target shapes the cost of capital for every founder raising a round, so it's worth knowing where the number came from. You'd assume rigorous economic research. Nope. A New Zealand finance minister pulled it out of the air during a 1980s TV interview to influence public expectations, and every central bank on earth just copied it. The broader argument is that good deflation (prices falling because entrepreneurs found cheaper ways to produce) is actually the definition of economic progress, and Moore's Law is the cleanest proof since nobody waited until 2024 to buy a computer despite knowing prices would halve every 18 months.
When software was expensive to build, commitment was cheap, and changing direction was the crisis that demanded a name. Now that building is cheap, the economics invert. Commitment becomes the expensive thing, and iteration is the default mode. The companies winning now look less like ships changing course and more like test kitchens running permanent R&D, which is why calling any given change a pivot has started to sound like a word from a different era.
Founders in the AI sector currently prioritize investors who offer specialized operational expertise and industry connections rather than simple capital deployment. Emerging professionals can bypass personal capital constraints by joining venture capital scout programs, which provide institutional funding allocations in exchange for a percentage of future returns. Successful angels build their portfolios by focusing exclusively on domains where they can provide unique strategic value and by restricting their investments to intimately known peers or exceptionally proven founders.
Open source has always licensed its code freely, but if you couldn't program, that freedom meant nothing.
Linear's CEO pushes back on the current wave of AI design tools, which all assume design is the act of producing an interface.
A new MCP tool integrates with Claude.ai or ChatGPT to streamline fundraising processes for founders.
The entire Salesforce, Agentforce, and Slack platforms are now exposed as APIs, MCP, and CLI.
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Most founder events are all pitch decks, but this one isn't.
Jack Dorsey's recent 40% staff reduction at Block signals an early shift toward "Dorsey Mode," a proactive organizational redesign anticipating that AI will significantly automate middle management and context-carrying roles over the next three years. This model advocates flattening hierarchies to two or three layers to increase agility while shifting resources downstream into sales and distribution channels as AI commoditizes software creation.
The AI industry has been talking about agents since 2025, and now it is talking about harnesses. The technology seems to be consolidating into a single interface that handles all AI-related tasks. The next level will involve a digital assistant device that can see the world and assist humans with their needs. Digital assistants will become the preferred interface between humans and the world in a disruptive and foundational way.
PitchBook data for Q1 2026 shows 73% of limited partner commitments went to exactly five top-tier funds and 75% of deployed capital funded just five frontier AI companies like OpenAI and Anthropic. Total deal volume simultaneously collapsed to 2016 levels as these mega-rounds absorbed record amounts of cash. Consequently, non-AI founders must navigate a depleted middle market with far less follow-on capital.
Founders should make decisions based on their current reality rather than copying what others have done. Employees should be compensated according to the company's values, as this makes them actually real. Don't just follow what big companies do. Raising money isn't just about funds - it sends a clear message to employees, investors, competitors, and customers that the business is doing well.
Balancing B2B design for both new users and paying customers is challenging due to differing needs. Designers need to define and focus on the new customer experience, leveraging market insights and acquisition data to articulate impacts clearly. By reframing new user issues into tangible business impacts, teams can better prioritize feature development without compromising new user onboarding.
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Capture, transcribe, and summarize meetings with searchable AI-generated notes.
Slide templates for your board meetings.
Make your website work as hard as you do, from ex-Webflow CTO.
Cal AI hit $50M ARR and sold for around $250M, then got delisted for using in-app payment sheets to dodge Apple's 30%. The worse story is that a legal version of the same playbook has existed for years. Charge on the web via Stripe, then deep-link paying users into the app so revenue never touches iOS.
Louis Blankemeier's startup built AI models for interpreting medical images to transform radiology practice. Instead of raising VC, they accepted an acquisition offer to leverage Radiology Partners' resources for scaling and regulatory compliance. This move aimed to enhance healthcare accessibility by integrating AI into real-world clinical settings efficiently.
Autoresearch can be used to improve metrics.
Anthropic's guide recommends treating Claude as a delegated engineer rather than a pair programmer.
Most companies only ask what their engineers are worth after years of hiring around problems and treating velocity as a proxy for value.
Mutiny had Sequoia, Insight, and Tiger money plus 8-figure ARR from Uber and Snowflake.
Build AI automations that make you better, not just faster.
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AI workloads don't simply need scale, they need ultra-low latency at high throughput. Crusoe [4]'s distributed memory architecture behaves like it's local, even at peak demand. The result?
Flatiron spent a month visiting AI-native startups in SF, and the org charts look nothing like a normal company. One PM covers five companies. Engineers talk to customers and own product decisions. Claude Code has replaced Cursor in most shops. Non-engineers shipped production tools, an account manager built a Slack agent to automate uploads, and accounting teams wrote database queries through MCP. Everyone's iterating 3-5x faster, which sounds great until you realize the real danger is saying yes to every feature request just because you finally can.
Stock comp looks the same on a GAAP line no matter what your share price does. The dilution doesn't. When Amplitude traded at $83, a $250K package cost 301K shares. At $5.90, that same package costs 4.2M shares. Same expense, 14x the dilution. That math gets ugly fast as software multiples keep compressing.
AI startups face pricing wars due to market saturation, leading to destructive underpricing. Enterprises have significant AI budgets and often use multiple tools, prioritizing value over cost. Successful companies focus on premium perception, creative pricing models, and strong customer integration to avoid competing solely on price.
Building a small, focused team requires discipline at every layer. You need to have relentless standards in hiring and constantly work to maintain alignment. The team needs to be genuinely obsessed with customers and have zero tolerance for mediocrity in craft. When done correctly, the team becomes a force that builds products that feel different.
Branding is a strategic system, not just design, built on positioning, messaging, visual identity, and scalable assets. Strong brands reduce sales friction, enable premium pricing, and compound over time. Most companies fail by starting with visuals instead of defining clear positioning and messaging first.
Corgi grew fast by fixing the root problem in insurance: slow underwriting and complex workflows. Instead of acting as a broker, it became the insurer and used AI to compress processes from weeks to minutes. Growth came from strong product-market fit and a unique strategy of building community (café, events, and media) instead of relying on traditional marketing.
Most B2B companies have two funnel layers. Generate leads, close them. Then they hit $1-3M and wonder why nothing's moving. ColdIQ built two layers in between and bootstrapped to $7M ARR. One is capture, mini-tools made with Claude Code that grab emails, de-anonymization that identifies anonymous visitors, and video sales letters that do the selling before the call. The other is management, a 10K subscriber newsletter, behavior-triggered email sequences, and 150+ LinkedIn posts a month across 24 team accounts. Prospects who see your content 3+ times before getting a cold email convert at 2-3x the rate.
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Automate regulatory compliance mapping for physical products across global markets.
Provide persistent memory for AI coding agents by loading relevant past project context into new sessions.