Jonathan Tower 🦄🦄🦄🦄🦄🦄

10/10 Before hunting for bargains, trace where independent customer cash enters, which obligations survive slower growth and whether backstops withstand the same shock. My latest: “The Tech Selloff Has a Circular Financing Problem.” tinyurl.com/ywmedhjx

9/10 An LP can own venture stakes in model developers, supplier stocks, infrastructure loans and data-center real estate. Four allocation categories can depend on the same buyers. Private marks adjusting more slowly than public shares do not make that exposure disappear.

8/10 Those decisions travel through the network. Infrastructure providers face weaker utilization, suppliers receive fewer orders, and lenders reassess collateral. A supplier holding customer equity can suffer twice: weaker sales and a declining investment.

7/10 A falling share price does not cancel purchase orders. Cash-rich companies can keep investing. Pressure builds where expansion requires fresh capital. Lower valuations make equity more dilutive; tougher lending terms can force spending cuts or capacity delays.

6/10 CoreWeave illustrates the overlap: Nvidia is an investor, equipment supplier and buyer of computing capacity. Those relationships can strengthen the business. Their value as protection still depends on how each participant withstands the same industry slowdown.

5/10 Investors can mistake an equity investment, a purchase commitment and rising revenue for three independent endorsements of value. All three may depend on the same forecast of eventual customer demand. That is one assumption wearing three suits.

4/10 A supplier can deliver real equipment and recognize legitimate revenue while financing the customer’s purchase. The valuation question is whether that customer can sustain spending once the supplier’s capital runs out.

3/10 Chipmakers invest in companies that buy their equipment. Cloud providers fund developers that purchase their computing capacity. These arrangements can build useful infrastructure while making revenue harder to underwrite. Both can be true.

2/10 Friday’s tech selloff raised a question that reaches beyond stock prices: how many seemingly separate AI investments depend on the same companies continuing to spend, and capital markets remaining willing to finance them?

1/10 When a supplier funds its customers, it resembles a restaurant where the chef lends diners money to eat, then shows his banker the packed dining room to justify six more locations. AI investors should spend more time asking who paid for dinner. 🧵

As I told Fortune, AI could transform the global economy while delivering spectacular losses to investors who confuse adoption with profitability. With $16.3B in 6-month revenue, up 1,300% YoY, Anthropic is an extraordinary business, but at any price? Frontier AI companies are committing hundreds of billions to compute while model prices fall and competition intensifies. Read the Fortune piece here: fortune.com/2026/10/28/anthr…

10/10 The next great AI company won't win because its demo impresses. It'll win because ripping out its software makes the customer's financial statements worse. The bar tab is on the table, and somebody wants to know who's picking up the check. Link: tinyurl.com/53rp82ab

9/10 For AI founders and VCs, the diligence questions are changing. What's the fully loaded customer payback period? How much value actually reaches the P&L? Do margins survive rising inference costs? And if the customer cancels tomorrow, which financial metric gets worse?

8/10 The more compelling AI economics may emerge in unglamorous places. ▶️Healthcare billing ▶️Insurance claims ▶️Revenue cycle management ▶️Collections Workflows where every error avoided or dollar recovered can be measured. Less science fiction. More cash flow.

7/10 Klarna learned another lesson the hard way. Aggressive AI-driven customer service automation helped cut costs, but management acknowledged quality issues and brought humans back. Eliminating labor is a dubious victory if you also eliminate customer loyalty.

6/10 Consider two AI projects, each generating $5M in annual net savings. Project A costs $20M to implement. Four-year payback. Project B costs $500K. Payback in roughly five weeks. Same annual savings. Radically different economics. Capital efficiency matters.

5/10 And the subscription fee is often the cheapest part. 1⃣ Integration 2⃣ Data preparation 3⃣ Security 4⃣ Compliance 5⃣ Training 6⃣ Human oversight 7⃣ Inference costs. Enterprise AI has a habit of arriving with a modest price tag and an entourage of expensive complications.

4/10 Time saved isn't automatically money earned. Unless management reduces costs, expands revenue, or redeploys that capacity productively, those impressive efficiency gains may never reach the P&L. AI can do its job perfectly while the investment still disappoints.