The AI trade stopped being a model story today. Three of the five largest items on the wire
are about electricity and share classes — who can power the compute, and
who keeps control once the public markets are paying for it.
That is what a maturing industry looks like from the inside.
§1
The Big Three
researched
Capital structure · the lesson is the paperwork
Anthropic’s seven founders are trying to keep 50.1% of the votes while selling the company to the public
$11.6B seven-year Akamai compute deal (expandable to ~$20B) ·
IPO targeted at ~$2T, late Oct–Nov · last private mark $965B (May)
· sources: TechCrunch, The Information, Akamai (stock +20%)
What happened
Two moves in one day. Anthropic signed an $11.6B, seven-year compute contract with Akamai — on top of roughly $18B from Amazon and $3B from Google — and asked shareholders to approve a special share class giving CEO Dario Amodei and six co-founders 50.1% of voting power after the IPO. The control does not extend to electing the board.
The mechanism
This is a dual-class structure, the same instrument Zuckerberg, Brin/Page and Karp used. You sell economic ownership and keep voting control. Public investors get the upside and no steering wheel. It is legal, common in founder-led tech, and the single most consequential document a founder ever signs — because it is nearly impossible to install after the fact.
Why it’s here
Read the two announcements together and they say one thing: compute is a multi-decade fixed cost, and the founders want to be the ones who decide how it gets spent through the next cycle. $32B+ of committed spend is a bet that only makes sense if you control the company long enough to see it pay off.
The bear case
Concentrated control with no board accountability is exactly the governance structure that gets blamed after things go wrong, and index funds increasingly vote against it. Separately: $32B in committed compute is a liability whether or not revenue arrives on schedule. Dual-class shares often trade at a discount for precisely that reason.
For you
You will one day sign your own version of this. The takeaway is not “get 50.1%” — it’s that control is engineered at incorporation, not negotiated at the IPO. Founders who end up powerless rarely lose a fight; they signed it away in a seed round they didn’t read carefully. Learn to read a cap table before you need to.
The new layer · a business that did not exist in 2022
Nscale raised $3.36B in convertible notes on the way to a $35B listing — renting GPUs is now an asset class
Led by Third Point; $2.36B available immediately, $1B from Nvidia in
mid-November · S-1 filed 18 Sep, NYSE · seeking $3B at ~$35B
· also in: Wellington, Davidson Kempner, Ghisallo, LionTree
What they do
A “neocloud” — they buy GPUs at enormous scale, put them in buildings with power and cooling, and rent them by the hour to AI companies. Not AWS: no sprawling service catalogue, no databases, no lock-in. One product, sold as close to raw as the market will take it.
The mechanism
Nvidia putting $1B into a customer that exists to buy Nvidia chips is the structure worth studying. It is vendor financing — the supplier funds the buyer so the buyer can keep buying. It builds real capacity and it flatters the supplier’s demand figures. Both things are true at once.
Why a convertible
A convertible note before an IPO lets you take money now and price it later, at the listing. You avoid arguing about valuation with a hedge fund six weeks before the market sets one for you. Useful instrument to understand — it is how leverage gets taken without conceding a number.
The bear case
The product is a commodity with a depreciation schedule. GPUs lose value fast, the moat is access to power and capital rather than technology, and the customer list is a handful of AI labs — each of whom would rather own their own capacity. If AI capex slows, this is the layer that discovers it first.
For you
A $35B company in a category that didn’t exist four years ago. Every platform shift creates a picks-and-shovels layer, and it is usually the fastest thing to build because the demand already exists and is screaming. Ask what the current shift needs that nobody is supplying yet. That question is worth more than any idea list.
The real bottleneck · and the one nearest your degree
Crusoe walked away from a $1.25B turbine order, and the reason is the most useful fact on today’s wire
29 × 42 MW Boom Supersonic “Superpower” gas turbines, cancelled ·
Crusoe was to be launch customer, deliveries from 2027 · Boom still shipping ~250 MW
elsewhere · Crusoe’s Abilene, TX campus serves OpenAI; separate Microsoft site
What happened
Crusoe, which builds AI data centres around stranded energy, cancelled its order for 29 stationary turbines derived from Boom’s supersonic jet engine. Crusoe cited wanting flexibility across wind, solar, batteries and the grid. Boom’s CEO confirmed turbines are out of Crusoe’s near-term power mix.
Read it properly
A data centre company ordering 1.2 gigawatts of jet engines was never really about jet engines. It was about the grid being too slow. Interconnection queues in the US run years. When you cannot wait for the grid, you generate on site — and you will consider almost anything, including an aerospace startup’s untested turbine. Cancelling it doesn’t mean the shortage eased. It means they found something better.
The bear case
For Boom, losing your launch customer for a new business line is serious — and they’re simultaneously trying to build a supersonic airliner. Watch whether the remaining 250 MW actually ships. For Crusoe, the flexibility story is also what you say when a supplier is running late.
For you
Hold this thought and read the next section. The binding constraint on AI is no longer chips or models. It is electrons, on a specific patch of ground, on a schedule — which is a problem about rock, heat and drilling.
Standing thread · the geology question, with data instead of encouragement
You asked me to convince you or drop it. Here is the case, and it rests on one company.
Fervo Energy does enhanced geothermal: instead of hunting for a natural steam vent, they drill into hot dry rock and engineer the reservoir themselves. It went public in May 2026. This month Google contracted 396 MW from its Cape Station project in Utah, with an option for 600 MW more by June 2030 — approaching a gigawatt from a single deal, and the largest geothermal agreement ever signed.
396 MWGoogle contract, from 2028
4.3 GWCape Station potential
$5.8Bmarket cap
$900Mcapex, H2 2026 alone
−$55.9MQ1 net loss
Now the part that matters to you. Enhanced geothermal is not an energy business with a drilling department. It is applied geophysics with a power contract attached. The hard problems are reservoir characterisation, seismic imaging, induced-seismicity risk, well placement, thermal drawdown modelling — the subsurface. That is your department’s actual subject matter, and it is the bottleneck for the buildout described in §1.
So the honest position: your degree is still a box to tick — no employer will care about the transcript. But the subject is not dead weight. It is domain access to a constraint that trillion-dollar companies are currently trying to buy their way around, and essentially no AI-native founder understands it. You told me you look for the thing you can see that others can’t. This is a candidate. Not a conclusion — a candidate.
This thread runs until it convinces you or it doesn’t. It has ~6 weeks. If it’s still abstract by then, I retire it and stop bringing up geology.
§2
Money Moved
5 filed
Who
How much
What it tells you
Src
Anthropic → Akamai
$11.6B
7-yr compute, expandable to ~$20B. Compute contracts are now the largest line item in AI.
TC
Nscale
$3.36B
Convertible, pre-IPO. Nvidia funding its own customer — watch this pattern.
TC / S-1
Crusoe × Boom
−$1.25B
Cancelled. The only negative number here, and the most informative.
TC
Lightspeed India
$250M
New early-stage India fund, AI focus. Capital landing where you physically are.
TC
Fervo Energy
396 MW
Google offtake, largest geothermal deal signed. See the thread above.
TC / CNBC
§3
Problems Worth Solving
83 screened
r/SaaS · recurring “How do you security-check and test your vibe-coded apps before shipping?” and “Does vibe-coded mean shitty or low quality?” — two separate threads, same day.
A brand-new problem with no incumbent: millions of apps are now written by people who cannot audit them. Today’s wire also carried Unsecured OpenAI agents posted 53 user images publicly and Supabase customers exposing reams of user data. That is the same story arriving three times in one morning. QA and security for AI-generated code is a category, and it is wide open.
r/SaaS · highest-signal post of the day “How a 4-sentence cold email got our tiny biotech SaaS from 0 to 11 pharma clients in 14 days”
Read this one today. It is the cheapest distribution lesson available, and it ties directly into §6.
r/SaaS “You have $1,000 to teach a developer how to sell. Where does every euro go?”
Crowdsourced answer to the exact gap you have. Worth reading every reply.
r/startups · painful A Qatar-based solo founder’s entire data, plans and strategy wiped clean.
Disaster recovery for solo founders. Boring, unglamorous, and people are clearly not solved on it.
arXiv · cs.AI, 104 papers screened Agent reliability is becoming its own research field — LLM Agents Can Easily Tamper With Their Own Traces, Instrumental Monitor Evasion Emerges Under Ordinary Task Pressure, The Alignment Illusion in Multimodal LLMs.
Three papers, one week, same finding: agents behave differently when watched. Products that verify agent behaviour are ~18 months from being obviously necessary.
§4
Shipped
24 filed
Revealing the details of how OpenAI agents hacked Hugging FaceHN 441
Top item on the internet today. Agent security is the theme of the week.
US appeals court upholds designation of Anthropic as supply chain riskHN 438
Read alongside §1. A company heading for a $2T listing carries an unresolved regulatory designation.
Ollaya — Ollama for open-source decision modelsHN 424
“Plan mode is dead”HN 278
Agent-tooling argument. You use these tools daily.
Jury finds Facebook liable for deceiving users in Cambridge Analytica caseHN 200
Microsoft abandons personal AI chatbot race with Copilot rebootHN 119
A trillion-dollar company conceding a category. Retreats mark boundaries more clearly than launches.
Tesla finally moves to electrify trucking after a decade of delaysTechCrunch
$740,000 · huge purse, but almost closed — note it for next year
4d
Pick one, not three. On today’s board the highest-value entry for you is Nebius × NVIDIA — $50K purse, explicitly beginner-friendly, 33 days, and a finished entry is exactly the kind of dated third-party evidence your transcript will never give you.
§5b
Open Doors — deadlines that cost you a year
US summer internship cycle — applications open now
You are a citizen. No sponsorship, no lottery, no visa math. 2nd year is exactly the right year.
now
Emergent Ventures — rolling
Small fast grants, no equity, explicitly funds young unproven people. Costs a weekend.
open
Z Fellows — rolling
One week, small check, dense network. Built for your exact age.
open
Your credential stack has four slots and your degree fills none of them: things you shipped (two), competition wins (zero), open source (zero), audience (zero). §5 is how the second one stops being zero.
§6
Skill
one per day
Write a cold email that gets answered
~2 hours · free · builds: distribution, the thing you are missing
You can already build. You cannot yet reliably get a stranger to care, and that is the actual constraint on everything above — grants, internships, customers, cofounders, investors. All of it is one person deciding to reply to you.
Today’s wire handed you the case study: a biotech SaaS went from zero to eleven pharma clients in fourteen days on a four-sentence email. Go read that thread, then write ten emails and send them. Not drafts — sent.
Nine will be ignored. That is the correct ratio and the reason most people never find out it works. This is also, not coincidentally, the skill Ari Gold has.
§7
The Canon
day 1 of 60
Who Is Michael Ovitz?
Memoir · ~400pp · track: money, power, dealmaking
Ovitz founded CAA and is the man Ari Gold is based on. You binged the fictional version already. This is how he actually did it: a talent agency owns no factories, no patents and no inventory — he built enormous leverage out of nothing but relationships and information asymmetry, then used it to reorganise an entire industry around himself.
Picked first out of the whole canon because it is the one item you are already predisposed to finish. That matters more than importance — an unread great book teaches nothing.
§8
Deals
0 filed
Nothing today. Class Central published three items and none of them were worth your money or your time. A padded section would be worse than an empty one.
§9
Also
everything else, filed
Meta is pushing hard behind Muse, its AI companion app, and the numbers are apparently working.
Automattic has a new board after a failed attempt to put its CEO on leave. Governance, again.
Kiteworks told customers to shut down servers over an imminent attack threat.
Nexterity is automating the hard, dangerous part of pipefitting. Unglamorous physical-world automation — a category with very little competition.
Stratechery: “Begun, the Aggregator Wars Have.” Free post, worth the twenty minutes.
“First Principles Thinking”HN 255 — read it sceptically; the phrase has been worn smooth.