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Metronome [4] is the platform that leading companies like ANTHROPIC and CONFLUENT use to experiment with and scale billing.
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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.