There is a particular kind of chart that tends to make us equal parts pleased and uneasy: the parabola. Lines that bend skyward at an accelerating rate are exhilarating to own, painful to watch from the sidelines, and miserable to time, because the same steepness that rewards you on the way up does nothing to cushion the way down. Dell is this quarter’s exhibit — a stock we still own for our clients, though we have been trimming during its rise.
A company that spent a decade being filed under “old PC name” reported a blowout AI-server quarter in late May and ran from the low $200s to above $400 in a matter of weeks — a roughly 290% gain on the year. Now, contrary to the worst cases of the dotcom era, in this case, the business is real; the AI-server backlog is real; the order book is real. But does the price already assume a version of the future that has to arrive precisely on schedule in order to maintain the momentum?
That brings us to another set of numbers that crossed our desk: a recent analysis of the announced data center pipeline that was a usefully sobering take on the AI build-out in months. (We don’t want to be so sober that we miss the party, but we also don’t want to lose our wits: there’s a happy medium.) Its central finding: of the 100-plus gigawatts of capacity announced for delivery through 2030, as much as 60% may be “at risk” — meaning, unlikely to materialize on the announced timeline (or, potentially, at all). Of roughly 102 GW of announced capacity, only about 40 GW clears a credible project-readiness screen.
Which number didn’t move? In the past quarter, the announced pipeline grew by more than 100 GW, but operational capacity — gigawatts actually plugged in and running — grew by zero. The headline writes itself: a hundred gigawatts of new announcements, not one new gigawatt on the grid.
Even with Abundant Liquidity, Physical Constraints > Financial Constraints
We can pretend to assume that capital solves everything. Perhaps it does — eventually. The bottlenecks are stubbornly material and cannot be instantly conjured away by capital: power procurement (grid access and multi-year equipment lead times), permitting and community opposition, the absence of signed offtake contracts, thin coordination with utilities. Money is abundant; transformers, interconnection queues, and permitted megawatts are not. You cannot wire a substation faster by raising another round.
The timeline, in other words, is elongating rather than compressing. Roughly 70% of the announced pipeline is in the 2027–2028 window, but many of those projects may carry sub-40% odds of arriving on time. The first true megaprojects are already slipping — delays jumped more than fourfold in a single quarter, led by an 11 GW Texas campus now pushed out. The demand may well be durable, but the delivery is physically constrained, and those are very different statements with very different investment implications — particularly in a universe such as ours where valuations are based on the anticipation and modeling of future events.
None of this says the AI thesis is wrong. It says the build-out will be lumpier, slower, and more concentrated among operators who can actually execute — the hyperscalers with the balance sheets, the utility relationships, and increasingly, with their own power.
The majors spent the quarter quietly turning into power companies: Meta expanded a 6.6 GW nuclear portfolio while funding seven new gas-fired plants; Microsoft went off-grid for the first time with a 1.35 GW natural-gas microgrid; Oracle contracted 1.2 GW of Bloom Energy fuel cells and laid out plans for a campus powered by three small modular reactors — the compact, factory-built nuclear units that several operators are betting on for carbon-free baseload. Google signed clean-energy and demand-response deals; Amazon committed $25 billion to data centers across Mississippi and another $15 billion in northern Indiana — and, tellingly, agreed in both cases to cover the full cost of the new power and grid infrastructure those campuses require rather than wait in the utility interconnection queue, even pledging to fund more generation in Indiana than the facilities themselves will consume. A slower, more disciplined cycle is probably healthier than a speculative one, but this just isn’t the kind of cycle priced into the most parabolic names.
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What Makes Sentiment Sour?
Good stock markets don’t usually wait for bad news to turn; they often turn on the mere possibility of potential disappointment. It won’t be the realization that the destination is not real, it will be the realization that the “timing” to the destination is being stretched. The dangerous gap is not “will AI demand exist” but “will it exist this quarter, at this run-rate, to justify this multiple.” Expectations of imminence — the embedded assumption that capacity announced today is revenue tomorrow — leaves tech stocks vulnerable to a repricing event. When 60% of a pipeline is on paper and the market has paid for it as though it were poured in concrete, the eventual reconciliation is going to be uncomfortable.
Our strategy follows from that. We’ve been trimming the names that have gone vertical, not because we doubt the secular story, but because the math of a parabola is unforgiving and the risk/reward at these levels is getting asymmetric. And we are looking the other way down the value chain — toward the companies that relieve the bottlenecks rather than the ones whose valuations assume the bottlenecks don’t exist. If the binding constraint is power and interconnection, the durable beneficiaries are likelier to be the grid-equipment makers, the independent power producers, the nuclear and fuel-cell suppliers, and the utilities with data centers queued at their gates.
Beyond energy, the bottlenecks can get strangely specific. One fund manager’s research we have seen makes the case at the level of individual components — and the examples are unglamorous. Consider the multi-layer ceramic capacitor, or MLCC: a tiny passive component, produced by the billions, whose job is to store and release small bursts of electrical charge and so smooth out the power delivered to a chip. A smartphone uses perhaps a thousand of them; a single high-end AI server rack may need more than a million. On that manager’s estimates, AI could consume 10–15% of the entire global MLCC supply within eighteen months, up from roughly 2% today — a step-change that the handful of dominant Japanese and Taiwanese manufacturers are not obviously racing to meet.
Or consider glass. The fiberglass and specialized low-loss glass used in circuit boards and advanced chip packaging has, on the same account, gone from a couple of months of inventory to effectively none, with manufacturers pushing through repeated price increases. The pattern repeats across memory chips, copper foil, the equipment used to make semiconductors, and — more speculatively — ordinary CPUs, which may be needed in far greater numbers as the industry shifts from training models to running them (including locally-running AI agents).
When everyone is staring at the obvious hardware winners, some more interesting risk-reward may sit a few layers down the supply chain, in the boring little parts nobody talks about until they run out.
Speaking of Running Out…
Back to the energy theme: a small but striking dispatch this week. Speaking at a summit in Regina, India’s High Commissioner to Canada signaled that New Delhi would be prepared to buy all the uranium Cameco could supply, and was eyeing investment in Canadian mining projects besides, as it expands its nuclear fleet. Cameco shares rose more than 6% on the remark.
Take it with the appropriate grain of salt — “we’ll take everything you’ve got” is the language of an opening position at a conference, not a signed supply agreement, and one diplomat’s enthusiasm is not a contract. But the direction is the point, and it rhymes with everything above. The AI build-out’s most obvious and painful constraint is electricity, the cleanest scalable baseload answer is increasingly nuclear, and nuclear runs on a fuel with a supply chain that takes the better part of a decade to expand. When sovereign buyers start talking about cornering a producer’s entire output, it tells you the demand for power is being taken seriously at the highest levels — and that the interesting opportunities may sit upstream of the data centers entirely, in the business of digging up and enriching the fuel.
The summary? At this stage, for the investment portfolios we manage, our preference is to find more of the pick-and-shovel relievers of the genuine bottlenecks, than the parabolas that assume there aren’t any.
Thanks for listening; we welcome your calls and questions.
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