CHAPTERS
Why big enterprise deals feel like a “black box” to founders
They frame the core problem: most founders understand self-serve SaaS because they’ve bought it, but few have seen six-, seven-, or eight-figure software purchases happen. That unfamiliarity makes big-deal selling feel scary and uncertain, even though it’s required to build truly massive revenue.
How big software is actually bought: shortlists and comparables
Dalton explains that expensive software is typically purchased by comparing a small set of vendors with similar packaging, pricing units, and contract structures. If your pricing looks “normal” next to competitors, buyers can evaluate you without confusion.
Don’t “break the buyer’s brain”: avoid weird pricing schemes
They argue against quirky, founder-invented pricing models that don’t match how the market transacts. Even if a strange model maps to your internal costs, it creates friction and mistrust in evaluation.
Selling outcomes vs. selling tools: who’s the real economic buyer?
Michael highlights a common mismatch: developers like buying tools, but CEOs are paid to deliver business results. Vendors that take responsibility for outcomes can charge dramatically more than vendors that merely ship tools.
Palantir-style economics: pricing by value created, not effort spent
They use Palantir stories to illustrate value-based pricing: customers don’t care how many people it took if the outcome creates massive financial value. Tool-only delivery (“go figure it out”) caps what you can charge, especially if the customer fails to realize value.
How non-engineers buy: outcomes first, internals last
Dalton contrasts engineer curiosity about internals with buyer focus on results and speed. They compare “spec marketing” (megapixels, shutter speed, PC specs) to outcome-oriented marketing (iPhone/Mac-style benefits).
“Stripe and AWS are just tools” is a misunderstanding of enterprise reality
They argue many founders misclassify companies like AWS/Stripe as tool sellers because founders only see the self-serve motion. In reality, these businesses have large sales teams, quotas, and massive enterprise contracts—meaning their enterprise offering is not the same as the startup experience.
AI era framing: code gets cheaper, outcomes get more valuable
They reference a thesis popularized in VC content: as AI reduces the cost/value of code, pure software differentiation compresses. Outcome-delivery businesses can see margins improve as models get better because they use AI internally to deliver the same promised result more cheaply.
How can you promise outcomes without knowing every customer’s business?
Michael surfaces a common fear: scaling outcome-based selling seems to require deep, unique knowledge per customer. Dalton and Michael respond that you can learn outcome criteria directly from customers and use competitor norms; plus, customer needs are often more similar than founders assume.
Your advantage: horizontal learning across customers
Michael argues vendors can sometimes understand outcomes better than customers because they see many implementations across many companies. By observing how different customers use the product to make money, you can develop repeatable playbooks and communicate best practices back to prospects.
Examples beyond Palantir: hyperscalers and enterprise “packaged custom” solutions
They describe how hyperscalers sell differently to large, non-software companies—more as bundled services and solution packaging than pay-per-API. Workday is cited as another example of selling a flexible system aligned to evolving business processes.
The practical path: keep selling up-market until you learn how
Dalton emphasizes that seven-figure contracts rarely happen on the first attempt. Companies win big deals by repeatedly trying, absorbing rejection reasons, improving product/process, and trying again over 1–2 years.
Reconciling fast growth goals with slow enterprise sales cycles
They address the tension between “grow 7% week-over-week” and year-long enterprise cycles. The takeaway: you must do both—keep near-term growth going while running long-cycle enterprise bets—and accept that some efforts won’t work immediately but will compound learning.
Use the slowness as a feature: run many deals in parallel and hedge
Michael reframes slow cycles as an advantage: gaps between meetings let founders run multiple opportunities concurrently. Talking to many prospects reduces dependence on any single difficult buyer and enables comparative learning across deals.
Reality check: big companies win with sales forces—don’t mythologize self-serve only
They close by urging founders not to cling to comforting stories that big winners “just sold to startups.” The AWS/Microsoft anecdote underscores that massive revenue often correlates with massive sales execution, and founders shouldn’t be afraid of that destination.
