šŸš€ # of solopreneurs making $10M+ in revenue tripled in the last 2 years

3 trends, 2 theses and 1 tool

Welcome back to ā€˜Tactical Tips’ by Jerel and Shuo at DECODE, the largest founder community co-hosted across Berkeley and Stanford.

We've updated our rhythm to bring you higher-signal content. We’ll be landing in your inbox twice a month, alternating between one of our founders’ top questions on how to build, sell and operate 10x better, and trends on what’s new and next in startups and tech to keep you ahead of the curve.

Today, we’ll be covering trends in solopreneur income, growing headcount in companies using AI, investors focusing on intelligence per dollar, and more.

And ... we’ve curated a YouTube playlist featuring our best founders, operators and investors. 

šŸ”„ Inside this issue:

āœ… 3 trends in startups/tech/venture
āœ… 2 theses on what’s next
āœ… 1 tool to love

šŸ‘‡Let’s dive in.

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3 trends in startups/tech/venture

šŸ“ˆ # of solopreneurs making $10M+ in revenue tripled in the last 2 years

  • Per the latest data from Stripe, the share of solopreneurs at ā€œhigher income thresholdsā€ are seeing rapid growth, with those making >$5M and >$10M per year seeing the largest inflections since 2023 šŸ‘‡

  • The takeaway? Thanks to AI, individuals can now achieve income levels that were once unachievable alone.

šŸ¤– Companies using AI most surprisingly also grew headcount by 10%+

  • Per the latest data from Ramp, companies making the biggest investments in AI are contradicting news headlines and actually expanding their employee headcount, with heavy adopters increasing headcount by ~10% and entry-level hiring by 12%.

  • In fact, these job gains emerged gradually over 6-12 months, suggesting that firms are taking time to integrate AI into their processes and then adding people to take advantage of new opportunities that have emerged as a result of those new processes.

  • Meanwhile low-intensity AI adopters saw no statistically significant change in their headcount, suggesting that firms making the most AI investments are currently growing faster than comparable firms.

  • Overall, this data suggests that AI's early economic impact may be less about replacing workers and more about enabling expansion at companies able to integrate the technology effectively.

šŸ’° Investors optimize for max intelligence per dollar vs intelligence itself

  • More investors and founders have started to focus on how to cut AI costs while increasing token usage to drive overall business growth.

  • For example, Brian Armstrong, the founder and CEO of Coinbase, has posted about cutting internal AI costs by nearly half while token usage grows exponentially — with better 1) defaults, 2) routing, and 3) caching.

    • Defaults: Shifting toward cheaper open-source models for routine tasks, especially in cases where ā€œfrontierā€ models like ChatGPT, Claude, and Gemini aren’t substantially better.

    • Routing: Smarter model routing matches tasks to the cheapest model capable of completing them.

    • Caching: Aggressive caching eliminates redundant outputs for repeated queries.

  • As a result, more investors have started using metrics that measure ā€œintelligence per dollarā€ when evaluating (AI) startups and using such metrics as proxies for how efficiently their capital will be used.

2 theses on what’s next

šŸ¤– The real winners of AI will innovate, not automate

  • The real winners of the AI era won't be companies using AI to do the same things faster. They will be the ones using AI to completely reinvent how business gets done.

    • Today: Every corporate process was designed by humans, for humans. They are fundamentally constrained by staffing limits, 9-to-5 structures, and human burnout.

    • Tomorrow: In an agentic-first enterprise, all of the constraints of today disappear.

  • Shuo wrote more about this topic in this article co-authored with Mark Settle, 7x CIO across companies including Okta and Visa.

šŸ¤” Companies’ private evaluations will become increasingly valuable

  • More and more companies have started to ask the question: if we have our own employees, business processes, workflows, and data, why not our own AI? Or in other words, why give all of this away to improve others’ AI models?

  • Public benchmarks are a commodity, whereas private evaluations leverage proprietary, real-world customer data and edge cases that competitors simply cannot access or replicate. Also, high-quality internal evaluations act as a precise product compass. They allow teams to ship updates rapidly and safely by instantly flagging hidden regressions or broken dependencies.

  • In a world of fast-followers and open-source clones, competitors can mimic features, but can’t replicate the continuous, automated improvement cycle powered by private evaluations. In fact, private evaluations capture years of hard-earned operational history—including past failures, bugs, and user friction points—transforming them into an asset that will becomes more valuable with time.

1 tool we love

šŸŽ“ AI Chief of Staff

  • Chief listens to your calls, captures every decision and commitment, and auto-assigns follow-ups to your team, thereby eliminating the endless admin work that typically follows meetings.

  • Chief now supports Fortune 500 C-suite executives as well as owners of 9-figure businesses. You can learn more from Chief’s founder DROdio here.

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Continue learning šŸŽ“

How to build

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  • Framework to nail enterprise sales (Read | Listen)

  • Qualities to choose in design partners (Read | Listen)

  • Questions to define ideal customer profile (Read | Listen)

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Other resources šŸ“š

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