Sequoia's "Services: The New Software" Thesis Will Mint Billionaires and Bankrupt Copycats đ
The autopilot playbook, the margin trap, the $0.03 problem, and a practical framework for AI builders, investors, and operators who need to decide what to do next đ§
đ Hey, Linas here! Welcome to another special issue of my daily newsletter. Each day, I focus on 3 stories that are making a difference in the financial technology space. Coupled with things worth watching & the most important money movements, itâs the only newsletter you need for all things when Finance meets Tech. If youâre reading this for the first time, itâs a brilliant opportunity to join a community of 370k+ FinTech leaders:
For every $1 companies stop spending on humans, they spend $0.03 on AI.
That number should haunt every investor who read Sequoia partner Julien Bekâs âServices: The New Softwareâ essay and felt a rush of conviction.
Bek argued that the next trillion-dollar company will be a software company masquerading as a services firm. And the argument is clean: for every dollar spent on software, six are spent on services. AI can now do the work, not just sell the tool. So the real money is in capturing the labor budget, not the software budget.
Every major VC firm seems to agree. YC wants AI agents that replace service workers. So does a16z. Sequoia wants autopilots selling outcomes. So does Bessemer. The consensus is so complete that you could swap the logos on their published theses and most readers wouldnât notice. On top of that, AI is expected to capture roughly half of all venture dollars this year, while the 10 largest VC firms now account for a whopping 43% of total capital raised đł
The money is enormous, and itâs all pointed in the same direction. That should make founders nervous, not excited.
The autopilot thesis correctly identifies a real structural shift. AI has crossed capability thresholds that make autonomous execution feasible in narrow domains. Companies like Crosby, WithCoverage, and Anterior are building real businesses by selling outcomes instead of tools. But the trillion-dollar framing requires something that early evidence contradicts: it requires labor budgets to redirect to AI vendors at comparable prices.
They donât. They evaporate đ«§
The most consequential insight in the AI economy right now is not that machines can do the work. Itâs that when machines do the work, the work gets repriced at machine rates. And machine rates are 97% cheaper đ€
Below is a practical breakdown of where Sequoiaâs thesis is right, where it breaks, and how to use the framework to make actual decisions about what to build, what to fund, and what to avoid in the Agentic AI Age. We name specific companies, map the biggest & most promising verticals right now (along with their pitch decks), quantify the economics, and provide the filters that separate the autopilots that will compound from the ones that will burn through their Series B and stall.
To make this even more powerful & actionable, weâre also sharing how I built an AI operating system to run a startup with Claude, how to turn Claude Cowork into your personal COO that does real work while you sleep, a list of the top 100 Seed Investors from 2025, and 25 most interesting AI startups (& their pitch decks) as extras. It has everything you need to build the one-person unicorn in 2026 đŠ
