Slower Than We Want, Faster Than the Internet
By Stephen Manning ·
Google reports Wednesday. Ignore the headline number — here's the one line that actually matters, and the pattern behind the whole AI buildout.
Google reports Wednesday. Ignore the headline number — here's the one line that actually matters, and the pattern behind the whole AI buildout.
Alphabet reports Wednesday after the close, and I care about it for a reason that has almost nothing to do with Google.
The report is really a referendum on the one question hanging over this entire market: all the money pouring into artificial intelligence, hundreds of billions of it — is any of it coming back out as earnings yet, or is it still just going in? Wednesday gives us a data point. The catch is that the headline profit number will not answer that question, and if you only read the headline you will probably walk away believing the wrong thing. So let me show you what I actually watch, because it applies to a lot more than one company.
The Google earnings trap
Wall Street wants about $2.87 a share on roughly $117 billion in revenue. Google has beaten the estimate seven quarters in a row, so another beat means almost nothing by itself. It is already expected.
Here is the part that fools people. Last quarter Google printed $5.11 a share against a $2.63 estimate, and the coverage called it a blowout. But most of that beat came from a paper gain on some private-company stakes Google holds, not from money the business actually earned. Back that number out and the quarter would have missed by a penny. Same company, completely different story, and the only thing that changed was whether you looked at the engine or at the accounting bolted onto it.
So before you react to any big figure on Wednesday, find out where it came from. That habit alone will save you from half the bad conclusions people draw at earnings time.
A few things in this report matter far more than the headline. The one I care about most is Cloud growth. Last quarter Google Cloud grew 63 percent and its backlog of signed commitments climbed toward half a trillion dollars. That backlog is the closest thing we have to proof that AI demand is turning into paid revenue rather than press releases.
Then there is the spending. Google has guided to somewhere between $175 and $190 billion in capital expenditure this year, and its free cash flow fell hard as a result. The entire bull-bear fight is over whether that spending is conviction or recklessness. What tips me one way or the other is whether management ties the spending to that signed backlog. Money spent against real commitments defends itself. Money spent against a hope does not.
Everything else — the model delays, the researchers leaving for rival labs — is worth watching but will not move this quarter's earnings. Cloud and spending will.
An old idea worth stealing
Now step back, because the most useful thing I know for investing in any new technology got its name from a Stanford researcher named Roy Amara, and it is almost embarrassingly simple.
"We overestimate what a technology does in the short run, and underestimate what it does in the long run."— Amara's Law
Both halves are true at the same time, which is what makes it so hard to trade. Early on, the hype gets ahead of the reality. Everyone drags the far-off endpoint into next year. Then reality disappoints, the "where is the return on all this money" complaints start, and people announce the whole thing was overhyped. That moment of disappointment is almost always the exact point where the long run is being badly underestimated, because the technology then spends the next decade quietly working its way into everything, arriving bigger than the hype promised but years later than it promised.
The internet is the obvious example. In 1999 the short run was wildly oversold, and it crashed in 2000, and everyone declared the internet a bubble. They were right about the timing and dead wrong about the destination. Amazon, Google, cloud, mobile — all of it got built on the wreckage over the next twenty years.
This is not an internet quirk. It keeps happening.
Railway mania bankrupted a generation of British investors in the 1840s, and then rail reorganized the whole economy for the next hundred years. Electricity is my favorite case, because the lag had a specific cause worth knowing. Factories electrified early and saw almost no gain, because all they did was rip out the steam engine and drop one big electric motor in its place. The real leap came decades later, when someone thought to redesign the entire factory around lots of small distributed motors, and that redesign is what made the assembly line possible. GPS sat around as a niche military tool for years until smartphones turned it into the invisible layer under ride-sharing and food delivery.
The same three things happen every time. The financial bubble comes early and pops before the technology pays off. The eventual winners turn out to be applications nobody could have named at the start. And the infrastructure everyone overbuilt during the mania becomes the cheap foundation the long run runs on.
Slower than we want, faster than the internet
So where are we with AI?
My honest bet is that adoption will feel frustratingly slow for a few years and then compound faster than most people expect. Slower than the true believers promise, no question. But faster than the internet took, and for one concrete reason. The internet had to build its physical network almost from scratch. AI gets to run on top of cloud, mobile, and connectivity that already reach everyone on earth. The pipes are already in the ground, so the moment the capability actually works, it can spread quickly.
There will also be a reckoning, and it is not a maybe. The buildout is running well ahead of near-term demand, which is exactly what the pattern predicts — hundreds of billions in spending against revenue that is real but far smaller. Some companies are not going to survive the gap between the two. I would watch the leveraged, no-moat, pure-buildout players most closely, the ones financing hardware with debt and planning to sort out demand later. When money gets tight or the mood turns, those go first, and the survivors buy their capacity for cents on the dollar.
Two signals tell you roughly where you are on this curve, and I pay more attention to them than to any price chart.
The first is the gap between spending and revenue. When it narrows, adoption is catching up, and that is healthy. When it widens while the financing gets more aggressive, you are late in the overbuild, and the wreck is getting closer.
The second is the quality of that financing. Early in a buildout, companies fund it out of cash flow. Late in one, they start reaching for debt and clever off-balance-sheet structures. When you see that shift, the shakeout is usually not far behind.
How I am positioning, near and long
Here is the move, and it is the reason I sleep fine not knowing the timing. You do not have to predict when any of this pays off. You have to own the things that keep earning across whatever timing actually shows up.
Own the rails, not the train.
For the near term, the most dependable winners are the ones who get paid through the whole buildout no matter which app or model wins. That is the physical bottleneck — the companies that make the chips, the machines that make the chips, the custom silicon, and the memory. Everybody building AI has to buy from them, full stop. This is the picks-and-shovels layer, and it is the least dependent on guessing which application ends up mattering.
Sitting right next to it is the constraint almost nobody talks about until they trip over it: power. AI data centers eat staggering amounts of electricity, and you cannot magic electricity into existence. New generation and grid capacity come online slowly, gated by permits and construction crews. So the companies that generate and move power benefit directly, and the constraint itself acts as a brake on how fast the whole thing can overbuild. I spent a good chunk of this year moving weight toward power in my own book for exactly that reason.
For the long term, the winners are the applications built on top that we genuinely cannot name yet, plus the survivors who scoop up the cheap infrastructure after the crash. You are not going to pick the next Uber-on-GPS today. Nobody is. What you can do is own the layer every future application will have to run on.
The winners nobody files under "AI"
This is the part I find most interesting, and it gets almost no airtime.
Everyone argues about the chip makers and the model labs. Meanwhile there is a quieter, sturdier way to win from AI, and it belongs to companies that already dominate their markets and simply use AI as a tool to widen the moat they already have. What makes this so appealing is that the benefit does not require the company to win a new market, launch a product, or outspend anybody on capital. It just makes a business that already prints money a little more efficient and a little stickier.
Take a warehouse membership retailer. Its real moat is not the pallets of paper towels, it is the renewal rate on the memberships, and the fee income that drops almost straight to profit. AI does not change what that business is. It makes the company better at spotting which members are about to drift away so it can pull them back, and it sharpens the brutally efficient buying operation that lets the retailer beat everyone on price in the first place.
Or take a consumer-products giant with dozens of category-leading brands. The moat there is brand and scale and shelf space, and AI mostly shows up as lower cost, smarter marketing and a leaner supply chain, which lands as margin rather than as some new revenue line. That is about as low-risk as a benefit gets.
A cloud-and-logistics conglomerate gets it from both ends at once. Its cloud arm sells the compute AI runs on, and AI simultaneously tightens its own enormous logistics and inventory operation. Two payoffs from one technology, and only one of them makes headlines.
The best version of this whole idea is the proprietary data moat. AI is only as good as the data feeding it, and companies sitting on unique, hard-to-copy data watch that data become more valuable without lifting a finger. Financial data. Legal and professional data. Medical and scientific records. These firms do not have to spend to capture the upside, because they already own the asset, and AI just repriced it higher.
Tying it together
Here is the shape of a portfolio built for all of this, and notice that none of it asks you to call the timing.
That mix does not care much when AI arrives. If it comes faster than expected, the infrastructure and the moats compound faster. If it disappoints for a few years first, which Amara's Law says it probably will, those same holdings keep earning while the overextended players get flushed, and you are already positioned for the long run when it finally shows up.
One last discipline. Watch that spending-versus-revenue gap and the quality of the financing, and treat a widening gap funded by fresh debt as your cue to get more defensive. A falling stock price on its own does not tell you much. It is just weather. The financing going bad is the thing that turns an overbuild into a wreck, and that is the signal worth acting on.
Google on Wednesday is the next data point. I will be reading the Cloud line and the way management explains the spending, and mostly ignoring the headline. You should too.
— Cash Flow University
Educational writing about market patterns, not investment advice, not a recommendation to buy or sell anything, and not tailored to your situation. Every company and pattern here is an illustration of an idea. Do your own work, and talk to a licensed advisor before you make any move. Assume I hold positions in some of what I have described.