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Here's a quick litmus test: can you launch a new AI feature tomorrow - with pricing that varies by tokens, model, region, and complexity - without creating new SKUs, without touching CPQ, and without manually adjusting invoices?
Anthropic announced Claude Code Security on Thursday. By Friday close, the Cyber ETF dropped 5% and Qualys fell 12%. The panic is teaching us more than the product itself. Okta and SailPoint cratered 10-11% despite Anthropic not mentioning "identity" once. Check Point and Fortinet barely flinched. The market is pricing in fear, not facts. If you're building in security, here's the cheat sheet: app-layer testing is about to get steamrolled by the foundation model labs, but categories with real infrastructure moats remain wide open for the next $10B+ cyber company.
The first principles of building a great company hadn't changed. Solve a real problem, deliver value that compounds, build something that's hard to replicate, and earn customers' trust. What's changed is the operating environment in which those principles must be applied. Founders who build enduring AI companies are the ones who have the discipline to hold on to first principles. These founders are expanding the size of their categories themselves rather than just building companies.
Here's a good test for whether you've actually made a strategic decision: if the opposite of your position sounds dumb, it's not a real choice. "We put customers first" doesn't count. This piece runs through 12 choice pairs that actually matter - bootstrapped vs. funded, platform vs. point solution, sales-led vs. product-led - and shows what you're giving up on each side. Most founders never explicitly pick. They just end up somewhere and tell themselves a story about why.
Tech companies are increasingly prioritizing distribution over product, with costs appearing in unexpected areas like COGS, as seen with AI companies treating inference costs as marketing. R&D is being leveraged to create self-sufficient products, evident in Atlassian's PLG model, spending more on R&D than S&M. Rising acquisition costs, indicated by longer CAC payback periods and decreasing NRR, emphasize the need for CFOs to focus on efficient distribution strategies.
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After moving to open source models such as gpt-oss-120b, Sully.ai's [12] inference costs dropped by 90%, while response times improved by
has now returned over 30 million minutes to physicians.
Hyperagent is an agents platform where every session has an isolated, full computing environment in the cloud. It provides real browsers, code execution, image/video generation, data warehouse access, hundreds of integrations, and the ability to learn any new API as a skill. The agents follow conversations, understand context, and act when relevant. Hyperagent can be deployed into Slack with one click.
Personal AI agent for everyday tasks
Long-running autonomous research agent.
This thread contrasts two founder archetypes: mercenaries, who optimize for valuation and exit, and missionaries, who optimize for solving a problem they deeply identify with. Prioritizing trust and mission over short-term economics ultimately saved the company. Missionary founders reveal themselves in how they pitch (problem-first vs. money-first), how they act under existential pressure (rule-breaking decisiveness like Steve Jobs at Apple), and how they compound advantages over time through customer obsession and reinvestment (as seen with Jeff Bezos).
No database. No API. Just 80+ markdown and YAML files running a full personal operating system for AI agents - and it works better than the fancy alternatives. Agents pull only what they need. A voice encoding system keeps your tone consistent. Memory logs build context over time. The takeaway for anyone building AI-native products is surprisingly simple - your file system might be the best data layer you already have.
Figma IPO'd at $33, hit $143, and now sits at $24.
1,700+ founders surveyed across stages and geographies in the most complete benchmark yet for what you should actually be paying yourself.
Software will become cheaper to produce, more powerful to use, and responsible for orchestrating and accelerating more GDP over the next decade.
You can now go from rough idea to working prototype in a single sitting using LLMs as a sketching tool.