I was excited to read Investing in AI: Six Mental Models to Tell Moats from Traps, largely because I have been following Rob May for years through his Investing in AI newsletter. What separates Rob May from many investors, in my view, is his willingness to avoid groupthink and reason from first principles. He looks past the prevailing narrative to ask how a business actually works, where its advantages come from, and whether those advantages can survive the next technological shift.
That is where this book shines. It is the first book I have encountered that approaches AI investing quite this way. Rather than simply identifying an exciting technology or a growing market, it asks readers to re-engineer how they analyze the businesses being built around it. The opportunity is enormous, but recognizing a promising AI capability and recognizing an investable company are two very different skills.
When the AI Narrative Runs Into Reality
Rob May grounds the discussion in stories that will be familiar to anyone who has followed the AI ecosystem. Jasper became a media darling and raised $125 million at a $1.5 billion valuation. Then ChatGPT brought powerful text generation directly to consumers, challenging the value of a product built around access to similar underlying capabilities. Rob May uses the reversal to examine how quickly an apparent advantage can become vulnerable.
What interests me about this example is the question it forces an investor or founder to confront: what does the customer still need from your company when the underlying technology becomes widely accessible? What differentiates a business with a defensible moat from just another AI wrapper? An early lead can buy time, but it does not answer that question. Workflow ownership, distribution, specialized knowledge, and customer relationships all become much more important once access to the model becomes commoditized.
DeepSeek presents the other side of that uncertainty. The book discusses the shock of a GPT-4-class model emerging with a reported training cost of $5.576 million. The investment lesson is significant: an assumption about the cost of competing can change much faster than a valuation model anticipates.
Why the SaaS Playbook Needs Rethinking
Rob May revisits the AI winters of the past and argues that today’s breadth of deployment has broken the old pattern of promising demonstrations followed by widespread disappointment. His argument is that useful AI is now embedded in too many real workflows to simply retreat into the laboratory. Nonetheless, even with continued adoption, it can still be difficult to identify the next unicorn that can capitalize on that adoption.
He identifies four fundamental differences that help explain the shift: AI can perform cognitive work, learn from data, change the expectations of the people using it, and become a default part of life for a generation growing up with it. The third point—reflexivity—is especially interesting. Once customers experience a capability, they begin expecting it elsewhere. The market a company is serving changes as the technology is adopted.
The biggest game changer is the speed of scaling. AI businesses can reach substantial size on timelines that previously belonged to exceptional outliers. But evaluating them still requires understanding the business model. Charging per task or per outcome creates a different relationship between value, revenue, and cost than selling a fixed number of software seats.
Some SaaS lessons remain highly relevant. Domain expertise still matters. Vertical-specific tools that understand an industry’s real problems can build meaningful advantages. Proprietary data loops can compound, and deep integrations can create switching costs. The challenge is distinguishing those durable strengths from features a customer could obtain from dozens of other vendors.
Six Mental Models That Make the Questions Better
The book organizes its central argument around six mental models: understanding AI as a collection of technologies; challenging traditional SaaS assumptions; expecting major market dislocations; recognizing changes to markets and corporate strategy; understanding faster scaling; and carefully evaluating AI’s cost of goods sold.
These work well together because each exposes a different weakness in a superficial investment thesis. Calling a company an “AI business” says little about its economics. A dramatic revenue increase says little about durability. A strong gross margin today says little about what happens when customers start asking the product to do more difficult work.
What I value most is the habit of asking a second question. After asking whether the technology works, ask whether it works reliably enough for the customer’s actual workflow. After asking whether customers are paying, ask what they are paying for and whether that spending is likely to continue. This makes the book useful beyond the moment in which its examples were written.
Botkeeper and the Importance of the Cost Trajectory
One of the most useful case studies is Botkeeper, a bookkeeping automation company Rob May invested in. He describes a business that initially relied heavily on human accountants and had gross margins of roughly 20 to 25 percent. That looked unappealing to investors expecting conventional software margins. But the work those people performed also generated data that could help automate the process. Rob May reports that gross margins later exceeded 70 percent as more work shifted to software.
The lesson I take from this is to examine what human involvement is building. If each completed task helps the system handle the next one more efficiently, early service costs may be financing a more scalable business. If every new customer requires a proportional increase in manual work indefinitely, the economics tell a different story.
Rob May also examines the opposite trajectory: inference costs that become more demanding as usage grows. An AI system may need more computation, repeated attempts, or human review to complete harder tasks. A product that looks attractive in a small pilot can become expensive when customers rely on it for complex work. This is why the direction of costs matters as much as the current margin.
Efficiency Is Not Automatically a Moat
Another framework I found useful is Rob May’s triad of AI value: commoditized AI, core AI, and expansion AI. Broadly, these describe capabilities competitors can readily adopt, capabilities that strengthen a company’s existing advantage, and capabilities that open up new business opportunities.
His hypothetical airline example makes the first category easy to understand. An airline introduces an AI support agent and becomes more efficient. Competitors then adopt similar tools. What briefly looked like a differentiator becomes an industry expectation, and some of the economic benefit may be competed away.
This is a helpful antidote to treating every AI announcement as evidence of a stronger business. I want to know what remains distinctive after competitors have access to comparable tools. A company that combines AI with proprietary feedback, deep operational knowledge, or a difficult-to-replicate customer workflow has a more persuasive answer than one announcing the same automation everyone else can buy.
Look for Operators Who Can Put AI to Work
The book is full of practical observations, including the importance of backing operators who can actually absorb AI. A concrete plan for changing workflows and responsibilities is far more compelling than a presentation promising AI throughout the organization.
Rob May’s discussion of revenue quality reinforces this. Paid pilots and proofs of concept can generate impressive early numbers, but they should be separated from revenue tied to ongoing production work. The useful question is how much of the business would remain if customers stopped experimenting tomorrow.
I see this as particularly valuable for founders. Winning a pilot is progress, but building a product that becomes part of how a customer operates is a different achievement. Understanding that distinction early can shape product priorities, hiring, and the way a company measures success.
Final Verdict
My biggest takeaway is that this book should not be reserved exclusively for investors. Entrepreneurs have just as much to gain from understanding what makes an AI company investable, how durable advantages form, and which startup pitfalls can be avoided before they become expensive.
It is best approached as a framework for judgment rather than a list of stocks to buy. Its strongest contribution is helping readers become more precise about why they believe in a business. The examples will inevitably age as the industry moves forward; the discipline of questioning assumptions is what gives the book its lasting usefulness.
Investing in AI: Six Mental Models to Tell Moats from Traps delivers the first-principles thinking that made me a reader of Rob May’s newsletter in the first place. For investors trying to separate a defensible business from a compelling story, and entrepreneurs trying to build the former, it is a thoughtful and worthwhile read.