In 60 seconds

Every AI rack is held together by copper. The GPUs talk to each other over high-speed cable and backplane connectors, and that in-rack wiring is a two-horse market: Amphenol and TE Connectivity. Every accelerator that ships needs it, and there is no third supplier of consequence.

TE holds a strong position in the custom-silicon programs at the major cloud operators, and its data-centre business is growing fast. The other half of the company, mostly tied to cars, is not. That split is the whole story, and it raises a simple question: is the market paying for the car business and getting the AI business close to free?

That last question is the whole case. Amphenol and TE sell into the same racks, to the same buyers, yet Amphenol trades at 32 times forward earnings and TE at 18. The spread has stayed open long enough that the “second-source discount” is starting to look less like a discount than a misprint.

Price targets

On the base case we are close to the Street. Where we differ is the shape of the distribution around it.

The weighted $262 is almost exactly the Street’s consensus: a $262 average, a $190 to $302 range, and a Buy rating. We agree on the base. Where we part company is the spread. We think the re-rating optionality in the bull case ($357) is underpriced, and that the floor sits higher than the Street’s $190 low. The drivers behind these numbers sit in the bridge table in section seven, and they are worth reading before the targets.

“Copper will continue to be the workhorse in the rack. It’s not copper or optical, it’s copper and optical. The orders that we have received are building backlog into 2027”. Terrence Curtin, CEO, TE Connectivity, Q2 FY2026 earnings call, 22 April 2026

That one sentence carries both the bull case and the main risk. (The rack is the cabinet that holds a cluster of GPUs and wires them together into a single machine; copper is the short, fast link inside it.) On the bull side, copper is still the dominant in-rack link, demand is strong enough that the company is already booking 2027, and the orders are real: a record $5.3B in Q2 at a book-to-bill of 1.12. On the risk side, Curtin reaches for “copper and optical” in the same breath, and TE has just bought into leading-edge optical technology to widen its roadmap. The hedge tells you the optics question is live. So the investment case turns on how much of the company is the copper workhorse, and how much has to become the optical hedge.

By the numbers · Q2 FY2026

01 Where TE sits in the stack

AI runs on a physical supply chain. The layers below are the chokepoints atlas: L0 at the base (raw materials) up to L8 (the apps people pay for). Red layers are real chokepoints, meaning one or two suppliers, one region, or multi-year lead times. TE sells at L4, in high-speed connectors and cable. That is a genuine chokepoint, but a shared one: TE and Amphenol both supply it, so TE owns half of the bottleneck rather than all of it.

Top: closer to user and revenue Bottom: scarcer and harder to replace

In-rack copper for scale-up systems is close to a duopoly. Amphenol and TE supply most of it, and you cannot build a dense GPU rack without it. Optics will take the longer links between racks; copper holds the short, fast links inside the rack. For an investor the distinction matters, because TE owns half of a chokepoint rather than a monopoly. Its edge is price, not a moat nobody else has. No third credible supplier has broken in at the leading edge, so the duopoly itself is the protection, and the second seat inside it is what you are buying at a discount.

02 What a connector actually is

The investment case stands or falls on the product, so it is worth understanding how a connector actually works. A connector joins two segments of wire or two circuit boards so an electrical signal can pass between them, reliably and cheaply at scale. The hard part in a modern AI server is that the signals are very-high-frequency analogue waveforms running at tens of gigabits per second, and the connector has to preserve the shape of those waveforms as they pass through. If the signal degrades, the GPU at the other end receives a corrupted bit.

Stopping that is signal-integrity engineering, and it is genuinely difficult: pin geometry, the dielectric around the pins, how the cable is terminated, how the assembly is routed inside the rack. The big AI platforms need three things in particular:

• Backplane connectors that join the main board to plug-in cards in the server chassis.

• Direct-attach copper (DAC) cables that link one GPU directly to the next, or to a switch, in a fixed-length assembly built to tight tolerance.

• High-speed cable assemblies that fan out from a switch to many GPUs, each cable tuned to the same electrical length.

The canonical example is Nvidia’s GB200 NVL72 rack, which wires together its 72 GPUs with about 5,000 copper NVLink cables. The design is copper rather than optical because an all-optical version would draw far more power. Over the distances inside a single rack, copper wins on the things that decide the design: cheaper per port, lower latency, lower power.

Where TE’s connectors and cables sit inside an AI server rack

The three things TE makes for an AI rack, and where each sits: backplane connectors inside the chassis, direct-attach copper between GPUs, and high-speed fan-out from the switch. Nvidia’s GB200 NVL72 is the canonical case, about 5,000 copper cables in one rack. Schematic by chokepoints.ai.

Once a connector is designed into a platform, it tends to stay. The hyperscaler qualification cycle is long, running through electrical simulation, mechanical and thermal testing, pilot builds, and then full production, and it can take well over a year. After that the connector ships for the life of the platform. A customer will not swap a qualified connector for a cheaper one lightly, because the connector is a tiny fraction of the cost of the rack and the cost of getting it wrong is enormous. That is the moat, and it is structural: a multi-year qualification cycle and a track record on the platforms that matter, rather than a brand or a patent.

The argument between bulls and bears is copper versus optics, and really it is an argument about where the line between them sits. Consensus today puts it at the edge of the rack. The risk is that the line moves inward over time, and that risk now has a date on it. Nvidia plans optical scale-up for systems of more than 1,000 GPUs by about 2028, and has invested billions in optical suppliers. Copper is expected to stay the in-rack workhorse through the near-term Vera Rubin generation in 2026 and 2027. TE’s answer is that it is not a pure copper bet; it has bought leading-edge optical technology to strengthen its passive optical roadmap. The hedge is real, and it costs the bull case some of its simplicity.

03 The company, briefly

TE makes connectors and sensors. Those two words cover an enormous range of products: the small terminals inside a car dashboard, the heavy blocks that route power through a factory, and the precision backplanes and DAC cables that link GPUs inside a server rack. The lineage runs back to 1941, when AMP Incorporated was founded in Pennsylvania. It was spun out of Tyco in 2007, renamed TE Connectivity in 2011, and later redomiciled to Ireland, which is where the “plc” comes from. It employs about 90,000 people across roughly 130 countries.

• 1941 · AMP founded in Pennsylvania, building the early connector industry around automotive and industrial customers.

• 1999 · AMP acquired by Tyco International, folded into a diversified conglomerate.

• JUL 2007 · Tyco break-up. The connectivity business is spun off as Tyco Electronics.

• MAR 2011 · Renamed TE Connectivity. NYSE-listed, with industrial and transportation as its twin pillars. Later redomiciled to Ireland.

• 2010s · The auto franchise. Sensor and connector content rises as cars electrify, and TE becomes a backbone supplier to global auto platforms.

• 2026 · AI acceleration. DDN orders up about 60% YoY, AI applications around 70% of the segment, and the full-year AI revenue target raised mid-year.

Today TE sells into three places: cars (the Transportation segment, mostly automotive sensors and harnesses), factories (industrial automation, energy, aerospace), and data centres (the Digital Data Networks unit inside Industrial). The split between the first two and the third is what defines the investment case.

One half is accelerating and the other is flat. The blended company grew 7% organic, and the market prices the blend rather than the half that is winning. The opportunity is in that gap.

04 How it makes money

The model is simple to describe and hard to compete with.

1 · Design in. TE’s engineers specify a connector, backplane or cable for the customer’s AI server, car or factory system, to electrical, mechanical, thermal and cost targets.

2 · Qualify. The part runs the customer’s qualification cycle: signal-integrity and reliability testing, then pilot builds. Often more than a year for a new AI platform.

3 · Ship for life. Once designed in, it ships in production volume for the life of the platform. Volume is visible, because orders track the customer’s build plan.

4 · Content rises. Each GPU generation uses more high-speed links per rack. Even when the unit price falls, the content per rack rises.

The flywheel is the whole model, and the hard part is the front of it. The segment numbers show the cycle in action: Industrial, which contains DDN, was up 27% reported, with DDN itself up 46% organic, while Transportation was down 0.5% organic.

Dividend up, buyback expanded

In March 2026 TE raised its quarterly dividend to $0.78 from $0.71 and expanded the buyback authorisation by $3.0B, to $22.25B in total. A company asking the market to re-rate it on AI orders is, in the same quarter, handing real cash back while the multiple is still wide. Read that as confidence in the cash flows behind the order book.

05 The AI engine: Digital Data Networks

DDN is the reason to look at the stock now. The unit sits inside Industrial and houses TE’s data-centre and AI business: the high-speed connectors, cable assemblies and backplanes used inside AI server racks.

The macro picture is simple. Hyperscalers are still building AI capacity faster than the supply chain can keep up, and TE is one of only two suppliers they can call. TE raised its $2.4B AI target mid-year rather than setting it at the start, and Curtin said the orders already in hand stretch into 2027. That is more visibility than most AI-exposed suppliers can claim. Orders hit a record $5.3B in the quarter, up 25% on the year and running ahead of sales, so the demand is booked rather than hoped for.

The lever to watch is the mix. AI is only about 13% of TE’s roughly $18.7B in annual sales today. The bull case is that this share climbs into the high teens or low twenties over the next two to three years, the point at which the slow car business stops dragging down the valuation. That share is the single number to track.

06 Cheap against what?

Valuation only means something in comparison, so here are three: peers now, TE against its own history, and the cash flows the market is discounting.

Forward P/E: TE sits with the auto names, not the AI-interconnect premium

TE is priced like the auto-component names rather than the AI-interconnect ones. Yet it just posted 24% adjusted EPS growth, a 46% organic DDN print, and a 1.12 book-to-bill. The peer it resembles on growth is not the peer it is priced like. Source: public market data, 16 Jun 2026; chokepoints.ai. Chart by chokepoints.ai.

TE also looks cheap against itself. Its own trailing P/E sits at 21.9×, against a ten-year mean near 28× and a five-year range of 12.9× to 32.5×. So TE today is roughly 22% below its own long-run average. Amphenol has gone the other way: it re-rated up on AI and still trades at a premium to its own median near 30×. The gap is wide three ways at once, against Amphenol, against the AI-interconnect group, and against TE’s own past.

Putting the methods on one page, the implied price band by approach comes out as:

• Scenario fair value (bear / base / bull): $157, $266, $357.

• Connector-peer forward P/E (17× to 32×) on NTM EPS: $201 to $378.

• TE’s own ten-year P/E (12.9× to 32.5×) on TTM EPS: $126 to $318.

• EV/EBITDA re-rate (14.4× to 25.6×): $218 to $399.

• Street consensus range: $190 to $302, average $262.

Implied value by method: scenario, peer P/E, own history, EV/EBITDA, Street

The $214.50 price sits below the midpoint of every method except the most pessimistic bear case. That is the trade in one chart. Source: chokepoints.ai scenario work on public figures. Chart by chokepoints.ai.

The cash flows tell the same story. At $214.50, a reverse-DCF says the market is discounting only about 4.4% free-cash-flow growth a year for ten years, on a 9% cost of capital and a 2.5% terminal rate. A company with that order book and 46% organic growth in its AI line is not a 4.4% grower, unless the car business drags everything down forever. Re-rate EV/EBITDA to 20×, still below the twin’s 25.6×, and the implied value is about $308, or +44%; at Amphenol parity it is about $399, or +86%. The bet is not on a boom. It is that the discount is too wide.

07 How the three cases add up

The honest way to bridge “should be worth more” and “$266” is to show the maths. The lever is not capacity. It is the exit multiple. AI revenue and the auto trajectory set the EPS, and the re-rating does the rest.

Each fair value is the exit multiple applied to FY27 adjusted EPS, then discounted back about a year at a 9% cost of capital. That is why the base reads $266 rather than the naive $290 you get from 22× times $13.2. The discount keeps the base case honest. Read the table one way and the point jumps out: the swing factor is the exit multiple, not the EPS. Across all three cases the EPS barely moves, from $12.2 to $13.9, while the multiple swings from 14× to 28×. The value is made in the re-rating, which is also why the trade is easy to underprice.

08 Pumps and corrections

This is a re-rating stock, so it trades on the multiple and on expectations as much as on the fundamentals. That makes the tape do counter-intuitive things. The cleanest example is that TE has, more than once, fallen on a quarter that was a clear beat with record orders. The reason was not the print. It was the bar. The market had already priced a strong quarter, so meeting it rather than clearing it was enough to trigger a sell-off.

Illustrative single-day reactions: what pumps the stock and what corrects it

What actually moves the stock, in rough order of magnitude. Illustrative; chokepoints.ai. Chart by chokepoints.ai.

The practical lesson on entry is to size and time around the expectations bar, not the print. A good quarter after a strong run can still correct, while a good-enough quarter on soft consensus is often where the multiple re-rates. The fundamentals here are solid. The work is in the timing.

09 Re-rating needed to make the upside real

Here is the tension we will not paper over. At a conservative 22× exit, the base-case upside (+24%) roughly matches the bear-case downside (−27%), so on the multiple alone the trade is close to a coin flip. It turns clearly asymmetric only once the exit multiple clears about 24×, which is still below both TE’s own history and Amphenol’s multiple. The call therefore rests on the re-rating, and the reverse-DCF is what tips the odds in its favour: at today’s price the market already assumes almost no growth.

The other thing we will not pretend away is that the gap to Amphenol is not random. Amphenol leads on speed, holds the larger Nvidia position, and earns its higher multiple for those reasons. The 43% discount is a real second-source discount, not a misprint. Our case is that the gap is too wide for what is, at the platform level, a very similar product. The bear case is that the gap is simply fair.

10 The two-sided case

11 Scorecard

12 What moves the signal

The one number we track from here is AI revenue as a share of the group. It is in the low teens today. When it clears roughly 15%, the auto drag stops setting the price, and the re-rating case stops being a forecast and starts being a fact.

Not investment advice. For information and discussion only, and not a personal recommendation, offer or solicitation. Capital is at risk, and investments can fall as well as rise. The price targets here are the publisher’s scenario-based fair-value estimates, not a forecast or a guarantee. Do your own work.

All figures come from TE Connectivity’s public disclosures, peer public market data, and the publisher’s own calculations. Broker research informed our internal conviction but is not quoted here. Sources: TE Q2 FY2026 results (PR Newswire); earnings-call transcript (Motley Fool); Stock Analysis and Companies Market Cap; The Register on Nvidia optical scale-up. © 2026 chokepoints.ai · Issue 003 · TE sits at L4 (compute hardware, high-speed interconnect) in the chokepoints AI-compute map. All issues

Read the full interactive atlas of the AI compute stack at chokepoints.ai.

We like SK Hynix. We think memory is one of the genuine chokepoints of this AI build-out, and we hold that view more firmly than most of the desks now upgrading the stock. We are still not buying it at ₩2,521,000. It is up 248% this year and 587% over twelve months, and the easy money has been made.

Both of those things are true at once. The demand for high-bandwidth memory is real, it is locked in under contract, and it is sold out for 2026. None of that is in dispute. The question we actually care about is narrower: at this price, what is the market already paying you to expect? Our answer is that it is paying for the peak: peak earnings, peak valuation, and peak market share, all in the same quarter.

So here is what we would do instead of buying it today. We are not chasing it, and we are not shorting a business this good. We wait. Section 06 sets out the price at which we become buyers; Section 07 sets out the two things that would make us sell rather than buy. Until one of those prints, the position is simple: add on weakness, not a fresh buy at today’s quote, and not a lazy HOLD.

New to memory? Start here

Every computer chip does two jobs: it calculates, and it remembers. The remembering is handled by memory chips. The everyday workhorse is DRAM, the same broad type that has been in PCs and phones for decades. Three companies make almost all of it: SK Hynix, Samsung, and Micron.

AI created demand for a premium version called HBM: high-bandwidth memory. It is DRAM stacked into tall towers and bonded right next to the AI processor (the GPU), so data can move between the two at enormous speed. Every high-end AI chip needs a block of it, and it sells far above ordinary DRAM. This is the product the whole note is about.

It matters because modern AI is held back as much by memory as by raw calculating power: a GPU starved of fast memory simply sits idle. SK Hynix makes more HBM than anyone else today, which is how a memory company became one of the most important names in the AI supply chain. (NAND, which appears later, is the other big memory type, the flash storage in drives, and it is a sideshow to this story.)

In 60 seconds

The short version: SK Hynix makes the specialised memory that sits next to every AI chip, it dominates that niche today, and it is having the best year in its history, which is exactly what makes the stock risky from here.

SK Hynix holds an estimated ~62% of the global HBM market, has effectively sold out HBM3E for 2026 at a ~50% price premium over conventional DRAM, and supplies roughly two-thirds of NVIDIA’s HBM4. Q1 2026 was the most extreme quarter in memory history: revenue of ₩52.6tn, operating profit of ₩37.6tn at a 72% margin, net profit of ₩40.3tn at a 77% net margin. The single quarter nearly matched all of FY2025 (operating profit ₩47.2tn at a 49% margin).

The bear case is just as real. NVIDIA certified all three HBM vendors for the Vera Rubin platform on 5 June 2026. Samsung retook the overall DRAM share lead in Q1 2026 (38% versus SK Hynix at 29%). The HBM lead is on a documented downslope: 62% in Q2 2025, ~57% in Q4 2025, with three-vendor HBM4 capacity landing through 2026 and 2027. The forward P/E of ~6.6× sits on a cycle peak. The whole trade is a referendum on whether HBM has structurally de-cyclicalised memory, or whether 2026 is the top of a wave that reverts as three-vendor capacity lands at once.

We believe the first. We are not buying the second.

01 We believe memory is the chokepoint of the build-out

Every note we write reasons from the same prior: the AI build-out is a default trajectory, not a forecast. Effective compute (very roughly, the useful computing power thrown at AI) is rising about half an order of magnitude a year. Annual AI investment is climbing fast toward a $7.6T baseline over 2026 to 2031, with hyperscaler capex alone guided to ~$700B in 2026. The surge is front-loaded into this decade. We pass through more OOMs (orders of magnitude: 10× jumps in scale) between 2024 and 2030 than in all the decades that follow. The exponential is in motion, the clusters are already signed, and we put the burden of proof on the sceptic.

The spending behind the compute: hyperscaler capex has run ~4.5× in three years

The spending behind the compute. Combined annual capex at the four largest US hyperscalers (Microsoft, Amazon, Alphabet and Meta) has run roughly 4.5× in three years, with 2026 guided to ~$700–725B. This is the demand curve SK Hynix sells into. Calendar-year capex; 2026 is company guidance. Sources: company filings, Financial Times, Goldman Sachs.

Inside a build-out this large, the money is made at the bottlenecks. Whenever one component is the thing everyone is short of, whoever controls it earns outsized profits, for exactly as long as they remain the only real source. First it was the GPUs themselves. Now, increasingly, it is the memory that feeds them.

Memory is now a chokepoint, by name. Situational Awareness names HBM and CoWoS advanced packaging as “already key bottlenecks… the primary constraint,” and projects memory rising to potentially more than 20% of cluster capex after the GPU itself. We hold that view. SK Hynix sits at the memory layer, in the HBM sub-layer, and it owns the largest single share of the most strategic product in the AI supply chain.

That is the bull case. The honest question, and the one this note actually answers, is what comes after. A chokepoint is only as good as its monopoly. The rent gets competed away wherever the constraint is contestable. So for any memory name the decisive question is not “is demand strong,” because we assume it is, but “how single-sourced is the specific thing this company sells, and for how long?”

For SK Hynix, the answer is: very single-sourced today, but visibly less so tomorrow. That is the entire note.

02 SK Hynix’s HBM lead is already melting

This is the chart that matters. SK Hynix’s HBM bit-share has already gone from 62% in Q2 2025 to ~57% in Q4 2025, and every force pulling it lower from here is now public. The same sell-side models that have SK Hynix leading today have its share eroding further as Samsung’s HBM4 ramps. The lead is melting on the industry’s own numbers, not on bear-case speculation.

The melt: SK Hynix dominates HBM today, yet the lead is slipping

The melt, in two cuts. SK Hynix dominates HBM today (~62% bit-share versus Micron and Samsung), yet Samsung already leads overall DRAM (38% versus 29%). The HBM lead has slipped from 62% to 57% in two quarters as three vendors ramp HBM4. Source: chokepoints.ai. Chart by chokepoints.ai.

Three things put the melt in motion. First, Samsung began mass-producing HBM4 in February 2026 and has shipped early samples to NVIDIA. Second, Micron sold out its 2026 HBM capacity and is ramping HBM4 a quarter early in Q2 2026. Third, NVIDIA certified all three vendors for HBM4 supply to the Vera Rubin platform on 5 June 2026. The stock fell roughly 10% on the multi-sourcing news.

None of this means SK Hynix is a bad business. It means the lead is a lead, not a lock. The difference matters for valuation. A moat is a permanent feature of a business; a lead is a snapshot, and snapshots decay.

03 The durable rent is migrating upstream, to TSMC and ASML

The lazy version of the chokepoint thesis says: SK Hynix makes the memory, SK Hynix owns the chokepoint, the rent accrues to SK Hynix. The honest version says the chokepoint is a triad, and the durable rent is migrating upstream.

The triad: the most durable rent sits upstream of the part SK Hynix sells

The triad. SK Hynix owns the HBM layer (~62% today, three vendors, and slipping). But the packaging and the base die beneath it are TSMC’s, the lithography beneath all of it is ASML’s, and NVIDIA sets the specification the whole stack must meet. The most durable rent sits upstream of the part SK Hynix actually sells. Source: chokepoints.ai. Diagram by chokepoints.ai.

Here is that picture in detail. HBM is built from three layers. The HBM stack itself, made by SK Hynix, Samsung, and Micron. The CoWoS advanced packaging that bonds the HBM to the logic die, made almost entirely by TSMC, fully booked with 52 to 78 week lead times, scaling from ~35,000 wafers per month in late 2024 to a projected 130,000 WPM by end-2026. And the logic base die that the HBM sits on, also made by TSMC, since HBM4 uses a 12nm TSMC-fabricated die designed by NVIDIA. NVIDIA occupies more than 70% of TSMC’s CoWoS-L capacity.

The rent in the triad splits roughly like this. SK Hynix captures the HBM bit-share and the premium pricing that bit-share commands. But the CoWoS gate, the base-die fab, and the EUV scanners behind all of it sit with TSMC and the equipment oligopoly (ASML, principally). NVIDIA, as monopsonist buyer, designs the HBM4 specification and dictates the base die. The bargaining power is asymmetric and tilts against SK Hynix over time. Downstream, hyperscaler long-term agreements pre-empt roughly half of SK Hynix’s revenue at contracted prices. That is a floor on volume, but it is also a ceiling on rent: contracted pricing renegotiates in the customer’s favour more often than the seller’s.

For readers who want the chokepoint with the least contestable rent, the upstream layers are arguably the cleaner trade. CoWoS and the base die sit with TSMC. EUV sits with ASML. Both have longer lock-in, fewer competitors, and less customer-concentration risk than any single HBM vendor. We hold SK Hynix for the idiosyncratic kicker, the purest HBM read-through, Korea-beta exposure, and a possible US-listing catalyst, with eyes open to share decay.

04 The real risk: memory-per-chip may not keep climbing

The Situational Awareness prior is a compute prior, not a memory-intensity prior. It says effective compute is rising 0.5 OOMs per year. It does not say HBM per unit of compute is rising. That part is a bet.

Two forces pull in opposite directions on how much HBM each unit of computing work needs. The bearish force is algorithmic efficiency: the steady progress that lets each new generation of models do more with less hardware. KV-cache compression, paged attention, multi-latent attention, mixture-of-experts, 4-bit and 8-bit quantisation: each of these is a technique that squeezes the memory needed per unit of work. A model that runs on half the HBM is, from SK Hynix’s point of view, a model that buys half the HBM.

The bullish force is Jevons. Long-context models, agentic workloads, reasoning chains, multi-modal inference. Each raises HBM per query, because the system has to hold more state in working memory while it thinks. The bet is that Jevons wins, that the demand for intelligence per dollar rises faster than algorithmic efficiency per dollar. We hold that bet, with caveats.

The honest horizon is not “structural for the decade.” It is structural 2025 to 2028, with a high-probability air-pocket, then cyclical re-assertion. The sell-side’s own DRAM price models already price the cycle turning by 2029, with DRAM ASPs modelled to fall as three-vendor capacity lands at once. If algorithmic efficiency compounds faster than Jevons, the air-pocket arrives earlier. If a hyperscaler capex pause of more than six months lands on the trip-wire of the ~3x capex-to-AI-services-revenue ratio, the air-pocket arrives in 2027. We are not pricing 2030. We are pricing the next three years.

This is the part of the chokepoint thesis that the consensus does not write about. The demand for memory is real. The demand for more memory per unit of compute is a separate question, and it is not guaranteed.

05 The price is paying for three peaks at once

The price is paying for the peak on three independent axes.

Peak earnings. Q1 2026 operating profit of ₩37.6tn nearly matched all of FY2025 (₩47.2tn). DRAM ASPs rose mid-60% quarter on quarter in Q1; NAND ASPs rose mid-70%. The Q1 print is the most extreme in memory history, and the entire 6.6× forward P/E sits on a denominator the company itself will almost certainly not repeat. Market cap of ~₩1,786tn implies forward net income of roughly ₩271tn (mcap / 6.6×) versus ~₩79tn trailing (mcap / 22.5×). The price requires net income to roughly triple off the trailing base and hold there. The forward P/E is a peak-earnings denominator, not a cheap multiple.

Peak P/B. ~10.9× trailing book, outside SK Hynix’s historic ~1 to 2.5× band by a factor of four or more. The normalised ROE of 53.18% must persist to justify the multiple, and ROE is a mean-reverting series in this industry. Forward P/B re-rates to roughly 2.1× as the FY2026E book builds (current ~₩232k BVPS plus retained earnings), which is more defensible, but still assumes that ROE persists. Re-rating from trailing to forward is a real effect, but it does not save the entry: it makes the over-statement less severe.

Peak HBM share. ~62% in Q2 2025, ~57% in Q4 2025, with three-vendor HBM4 capacity ramping through 2026 and 2027. The lead is melting. SK Hynix is about to start earning peak margins on a falling share of the most strategic product it sells.

Quarterly revenue and operating profit, Q1 2024 to Q1 2026: the denominator the 6.6x forward P/E sits on

Quarterly revenue and operating profit, Q1 2024 to Q1 2026. The Q1 2026 bar dwarfs everything before it. This is the denominator the 6.6× forward P/E is sitting on. Source: SK Hynix public disclosures; chokepoints.ai. Chart by chokepoints.ai.

Blended scenario fair value: today’s price only clears the bull case, by 9%

Blended scenario fair value. Current price ₩2,521,000. Bear ₩1,003,000 (−60%). Base ₩1,735,000 (−31%). Bull ₩2,740,000 (+9%). Today’s price only clears the bull case, by 9%. Source: chokepoints.ai scenario work on public figures. Chart by chokepoints.ai.

The sensitivity grid is the cleanest tell. Our base case sits ~31% below the current price. The bull case barely clears it (+9%). The bear is severe (−60%). And the bulls’ own published bear scenarios, including the larger Korean and global houses, sit at ₩1.2M to ₩2.0M, all below the current price. You can be right on the thesis and lose 30% on the entry. The asymmetry is the wrong way.

06 We will buy the pullback, not the peak

ACCUMULATE means we have a price at which we will be a buyer, and we have published it.

Trigger to add: a pullback toward the ~₩1.7M base case (a drawdown of roughly a third from the current price), provided two conditions hold. (1) SK Hynix’s HBM4 share is still at or above 40%, indicating the moat has not collapsed. (2) No HBM contract-price cut has printed, indicating the supercycle has not ended.

The next two earnings prints (Q3 and Q4 2026) are the binary read on HBM4 share, not on 2027 numbers. We will be buyers at the price the oligopoly produces, not at the price the consensus is paying. Pay the base case for the structural bull’s upside.

What it is worth now, and what a re-rating would change. On our base case the shares are worth about ₩1.7M today. At ₩2,521,000 the market is paying roughly 45% above that (the same gap the sensitivity grid shows as our base sitting ~31% below the current price), and only the bull case (₩2,740,000) justifies it. A genuine re-rating would move our number, not just the price: if HBM holds its premium, the US listing lands, and the market accepts that memory has partly de-cyclicalised, fair value migrates up toward the bull. We put ~60% on the base-case path (an air-pocket and a pullback toward ₩1.7M inside 12 to 24 months) and ~40% on the re-rate path. At those odds, today’s price is already paying you for the re-rate before it has happened.

07 Two events would make us sell, and one has half-fired

We will change the position the day any of the following fires. We are stating it now, in writing, so that the discipline is public.

• (a) First HBM contract-price cut, quarter on quarter. Not a volume miss. Not a mix shift. Not a guidance update. A real reported HBM ASP reduction, in earnings disclosures or in sell-side channel checks. Price is the cycle.

• (b) Samsung wins majority HBM4 share at NVIDIA. Not a hair over half. A clear majority on Vera Rubin volumes, confirmed in earnings or in disclosed allocation.

The kill-switch has already partially fired. NVIDIA’s 5 June 2026 three-vendor certification and Samsung’s Q1 2026 DRAM share lead (38% versus SK Hynix at 29%) are facts, not forecasts. We are not exiting. We are flagging that the conditions for a re-rating are now on the page, not in the future.

Why hold, and not move to underweight today? Three reasons. First, the business is genuinely excellent and the order book is sold out, so shorting it now is a bet against contracted demand. Second, the timing of the air-pocket is unknowable, and being early on a cyclical short is indistinguishable from being wrong. Third, the two events that would actually break the thesis have not printed: the conditions for a re-rating are visible, but they are not yet facts. Against the other ideas on our sheet, SK Hynix earns its place as the cleanest, highest-share read on the memory chokepoint, with an idiosyncratic US-listing catalyst on top. What it does not earn, at this entry, is fresh capital.

If (a) or (b) prints, we move to UNDERWEIGHT on the position, sell the cyclical, and revisit only when the air-pocket has cleared.

08 The bull and the bear are both right

Both halves are real. The whole call is which half the price is paying for.

09 The scorecard: real chokepoint, contestable rent, peak entry

10 Watch one number: the HBM contract price

HBM ASPs. Not revenue. Not share. Not earnings. The contract price of HBM, quarter on quarter. Volume misses can be explained by mix, customer timing, or inventory. A price cut means the cycle has turned.

The first house or channel check that reports an HBM ASP cut QoQ is the print we publish a position change on. That is the discipline. The chokepoint is real, the rent is contestable, and the entry is the peak. We will be buyers at the price the oligopoly produces, not at the price the consensus is paying.

Not investment advice. For information and discussion only. Not a personal recommendation, nor an offer or solicitation to buy or sell any security. Capital is at risk; investments can fall as well as rise; past performance does not indicate future results. The scenario fair values and price targets in this note are the publisher’s estimates, not a forecast or a guarantee. All figures are from public disclosures and the publisher’s own calculations. No third-party research is quoted or relied upon.

All figures come from SK Hynix’s public disclosures, peer public market data, and the publisher’s own calculations. No third-party research is quoted or reproduced. Sources: Situational Awareness; SK Hynix public disclosures; chokepoints.ai scenario work. © 2026 chokepoints.ai · Issue 003 · SK Hynix sits at the memory layer, in the HBM sub-layer, in the AI chokepoints map. The chokepoint is real. The price is the peak. We are patient. All issues

Read the full interactive atlas of the AI compute stack at chokepoints.ai.

Jean-Claude-Richard de Saint-Non, Ancient Ruins: Ancient Aqueduct (1756). Cleveland Museum of Art, CC0.

In 60 seconds

Every company building AI, from a two-person startup to Microsoft, needs large clusters of Nvidia chips to train and run its models. Most cannot build that capacity fast enough on their own. Nebius builds the data centres, fills them with GPUs, wires up the power, and rents the capacity out. Think AWS, but built specifically for AI.

It was carved out of Yandex, Russia’s dominant search and technology group, in 2024. It kept the engineers and has scaled fast: run-rate revenue went from $121M to $1.92B in six quarters, and it holds close to $50B of contracts with Microsoft and Meta. The question is not whether AI demand is real. It is whether Nebius can build and fund fast enough to justify a ~$56B valuation.

Each issue of this newsletter examines one point in the AI build-out where demand has run well ahead of supply, and asks which companies benefit. Nebius Group sits on one of those points. It rents out GPU capacity at gigawatt scale, and on its first-quarter call management described the bottleneck plainly.

For every GPU coming online, roughly four customers are competing for capacity. Time-to-power is critical for both revenue conversion and contract momentum. Paraphrasing Nebius management, JMP Securities, 20 May 2026

That quote holds both the bull case and the central risk. Demand is secured: Nebius has signed close to $50bn of contracts with Microsoft and Meta. What remains open is execution: can a company whose last data-centre build finished over a decade ago in Finland turn contracted power into connected , revenue-earning power fast enough, while funding a $20–25bn capital programme without heavy dilution? This issue is our first pass at that question.

01 Where Nebius sits in the stack

AI runs on a physical supply chain. The layers below are the chokepoints atlas: L0 at the base (raw materials) up to L8 (the apps people pay for). Red layers are real chokepoints, meaning one or two suppliers, one region, or multi-year lead times. Nebius sells at L6 (neocloud, segment L6.1.2) and builds down into L3, where it owns its sites as a facility operator. That position shapes both its opportunity and its risk: it depends on every bottleneck beneath it and holds no monopoly of its own.

Stack layers adapted from the chokepoints.ai value-chain map.

02 What Nebius is

Nebius is what was left of Yandex once the Russian search business was carved off; it re-listed on Nasdaq in late 2024 and reinvented itself as a *neocloud*: a specialist that does nothing but rent AI compute, vertically integrated from the concrete and the power contract up through Nvidia GPUs to its own software and inference stack. Growth since the relisting has been rapid: AI-cloud revenue grew 841% year on year to $390m in Q1 2026, 98% of the group total, and annualised run-rate revenue hit $1.92bn, up 674%.

Management guides to full-year 2026 revenue of $3.0–3.4bn and, more ambitiously, an exit run-rate of $7–9bn. That exit number is where bulls and bears diverge: hitting it means compounding the run-rate roughly 54–67% every quarter for three more quarters, the bulk in Q4. The growth so far supports the trajectory, but the implied step-up is far larger than anything the company has yet delivered in a single quarter.

Background

• 2000 — Yandex founded by Arkady Volozh. Russia’s dominant search engine, then a full cloud, ads and ride-hail group.

• 2011 — Lists on Nasdaq as Yandex N.V., a Dutch holding company.

• FEB 2022 — Trading suspended. Sanctions after Russia’s invasion freeze the shares.

• JUL 2024 — The split. Russian businesses sold to local investors. The group keeps its non-Russian assets and over 1,000 engineers, renames itself Nebius Group. Volozh returns as CEO.

• OCT 2024 — Relists on Nasdaq (NBIS), moves fully into AI cloud.

• 2025–26 — Scale arrives. A $17.4B Microsoft deal, up to $27B with Meta, a $2B Nvidia investment, and run-rate revenue through $1.9B.

What the group contains

Nebius AI cloud is ~95% of the story. Around it:

• ClickHouse : ~25% stake marked at ~$15bn round (a ~$780m non-cash gain ran through Q1 2026).

• Toloka : AI data-labelling; Bezos Expeditions invested.

• Avride : self-driving vehicles, partnered with Uber.

• TripleTen : edtech reskilling, ~88% YoY growth.

Headcount ~1,371, HQ Amsterdam, listed New York. Net loss ~$447M in FY2025; Q1’26 profit was a one-off accounting gain, not operating cash.

The bars show the track record; the dashed box is the target, with most of 2026 back-half loaded. Source: Nebius Q1’26 disclosures; broker estimates (Singular Research, JMP). Chart by chokepoints.ai.

03 The backlog: Microsoft and Meta

In eight months Nebius signed two of the largest GPU-capacity contracts in the market. Microsoft came first, September 2025: a five-year deal worth $17.4bn, with options to $19.4bn, anchored on the Vineland, New Jersey site. Meta followed with a small $3bn deal late in 2025, then a far larger arrangement in March 2026 worth up to $27bn. The Meta contract is structured unusually.

The $27bn Meta contract includes $12bn of dedicated capacity from early 2027, plus $15bn that Nebius can allocate to Meta at pre-agreed terms or sell to other AI-cloud customers at market prices. Broker synthesis from broker and expert-network research

Add the smaller deals and the backlog lands near $48–50bn: multi-year revenue visibility from investment-grade counterparties, which is rare at this scale. It is already converting to cash: deferred revenue (customer prepayments) jumped to $4.8bn at end-March from $1.6bn three months earlier. Customers are paying up front to hold their place in the queue.

Nebius is the clear number-two neocloud by backlog, with two customers making up essentially the whole book. Source: company disclosures; Evercore ISI, Wolfe Research, United Securities. Chart by chokepoints.ai.

04 How it makes money

The model is straightforward to describe and very capital-intensive to run: buy chips, house them, rent them. What matters is utilisation, price per chip-hour, and how long the chips stay economically useful.

1. Raise capital ($20–25B). FY26 capex. Funded by equity, convertibles, customer prepayments and, soon, asset-backed debt.

2. Build the kit. About 80% of spend is Nvidia GPUs; ~20% is data centre, power and cooling.

3. Rent it out. Reserved multi-year capacity (Microsoft, Meta) plus on-demand for enterprises and AI-native customers, which carries higher margin.

4. Earn per MW. Management cites up to ~$20M of annual revenue per megawatt on optimised inference workloads.

One number decides whether the model works: GPU useful life. At 5 years , the economics are solid. At 3 years , they shrink fast. Nebius extended its assumed life from 4 to 5 years in January 2026, lifting Q1 profit by about $42M. Hyperscalers use 5–6 years; bulls call it reasonable, bears call it the single biggest lever on reported earnings quality.

05 Contracted power versus connected power

Contracted power is enormous: over 3.5 GW secured by May 2026, with management ratcheting the year-end contracted target between 2.5 and 4 GW. Connected power (built, energised, earning) is a sliver: guidance is 800 MW–1 GW by year-end, and the company exited 2025 with only ~0.2 GW live. Roughly a tenth of what’s signed is generating revenue today.

That gap is the centre of the near-term debate, and broker coverage treats it as the main risk. Nebius owns more than three-quarters of its contracted power rather than leasing, which means higher returns if it works and higher execution risk if it slips, across a pipeline of 16–17 sites. The cautions are specific:

We see moderate risk to the timing of planned builds for five of its largest sites… its last data-centre build completed over a decade ago in Finland. We expect the majority of FY26 capacity from colos, with greenfield primarily impacting the FY27 ramp. Sell-side capacity review, May 2026

The gap: only ~5–10% of contracted power earns revenue today. Watch the gigawatt sites in Pennsylvania (1.2 GW) and Independence, Missouri (1.2 GW, first 200 MW late 2026). Also note: Vineland, NJ (the Microsoft anchor site) is facing a community noise lawsuit. One mitigant is a **328 MW** Bloom Energy on-site fuel-cell deal that lets Nebius sidestep slow grid interconnection. Source: Nebius guidance; capacity reviews; public filings. Chart by chokepoints.ai.

06 Funding the build

Cash plus customer prepayments do not cover the 2026 build. Nebius carried $9.3bn of cash at end-March (up from $3.7bn) and $4.8bn of prepayments, call it $14bn of resources against a capex guide of $20–25bn, roughly four-fifths of it Nvidia silicon. The gap is filled with debt and equity-linked paper, and that’s the dilution overhang.

Already raised: a $2.0bn Nvidia pre-funded warrant (strike $94.94) and $4.34bn of convertibles, both March 2026, plus an at-the-market equity programme (~$2.9bn unused). Risk: if the gap is plugged with parent-level converts and equity rather than asset-backed project debt, shareholders wear the dilution. One bear scenario in broker research puts it at ~17% a year through 2030. Source: Nebius filings; financing notes. Chart by chokepoints.ai.

07 Operating leverage, and a flattered bottom line

The operating story is strong. Gross margin went from 51% to 74% in a year; group adjusted EBITDA turned positive and reached $130m at a 32% margin, with the core AI-cloud segment at a 45% EBITDA margin. When the fleet fills, the unit economics work.

The headline $621m of Q1 net income is mostly an accounting effect: a $781m non-cash gain from marking up the ClickHouse stake (which raised at a ~$15bn valuation in January). Strip it out and the underlying operation ran a $128m operating loss. Two more flags worth noting: the GPU useful-life assumption was stretched from four to five years this quarter (which flatters depreciation), and operating cash flow is driven by customer prepayments, not yet by profit.

Every cost line falls as a share of revenue as the fleet fills. This is the core bull point: unit economics work once capacity is used. Source: Nebius Q1’26 6-K; chokepoints.ai calculations. Chart by chokepoints.ai.

08 Competition, and a closing window

Nebius is the clearest number-two to CoreWeave. CoreWeave is bigger and faster on the top line ($2.1bn Q1 revenue, +112%, on a ~$99bn backlog) but carries over $20bn of debt and a US-centric footprint. Nebius is smaller but better-funded for its size, net-cash-ish, spread across the US, Europe and the Middle East, and pushing into software to make the offering stickier than raw GPU rental.

Nebius is viewed as a key player alongside CoreWeave, Lambda and Crusoe. It differentiates by investing in software and inference platforms to increase stickiness and avoid commodity GPU-reseller status. Expert Insight, 16 Apr 2026

The category’s pricing power has a time limit. It exists because hyperscalers are short of compute, and Bernstein and others expect that shortage to ease toward 2030 as AWS, Microsoft and Google bring their own capacity online. At that point raw compute commoditises, and the neoclouds that survive will be the ones that moved up the stack in time. CoreWeave targets 8 GW of active power by 2030; Nebius is pursuing the same scale with materially less debt.

09 Valuation

At roughly $222 a share the equity is worth about $57bn, already down ~20% from its $279 high. On enterprise value over run-rate revenue, you pay about 29× today’s ARR, or about 7× the exit-2026 target if the build lands on guide. The valuation is a bet that the power gap in section five closes on schedule.

83% of gross receivables sat with Microsoft at 31 March, up from 59% a year earlier. That gives multi-year visibility, but also binary concentration and renewal risk. Source: Nebius 20-F / 6-K; EV from public market data; multiples are chokepoints.ai’s own. Concentration corroborated via broker research.

10 Three scenarios

11 The two-sided case

12 Scorecard

13 What moves the signal

The number we will track from here is connected megawatts.

Not investment advice. For information and discussion only; not a personal recommendation, offer or solicitation. Capital is at risk. The “signal” is an analytical view from public fundamentals and licensed research read in summary, not a price target. Do your own work.

© 2026 chokepoints.ai · Issue 001 · Nebius sits at L6 (cloud & infrastructure software) in the chokepoints AI-compute map. All issues ·

Read the full interactive atlas of the AI compute stack at chokepoints.ai.

The chokepoints.ai atlas: the AI-compute value chain, layer by layer.

When SpaceX listed, up to a third of the float was earmarked for retail. By then the prize had already been settled: early investors and more than 4,000 employees were millionaires before the first public share traded, around 400 were centimillionaires, and a handful were billionaires. That is how most of the obvious AI winners have gone too. The gains were made in private, years before anyone outside could buy in.

Which makes the useful question a different one: where did the prize go for investors who could only buy public stocks? The market answers it cleanly.

01 Where the prize went

If you’d invested in the S&P in 2013, you’d have 5x’d your money in 13 years. Not bad, but a more concentrated NASDAQ bet would’ve yielded 8.1x.

All sounds great, until you see that investing in an index of the top 10 stocks would have yielded 113x, a 40+% IRR, easily beating most top-quartile hedge funds. (Obviously you’d have had to keep tracking the top 10 as it changed over the years, but it’s useful as a thought exercise.)

Almost a dream: 113x on $10k makes you a millionaire in 13 years. That is, unless you’d gone all in on Nvidia and made a staggering 684x in that time frame.

Growth of $100 from Jan 2013 · log scale

The whole market returned 5×; the single chipmaker at the chokepoint returned 684×. Note the log scale: each gridline is 10× the last. Source: public market total-return data, Jan 2013 to Jun 2026; top-10 basket equal-weighted, rebalanced as constituents changed. Chart by chokepoints.ai.

The effect only amplifies from 2025 onwards: investing in the AI picks and shovels has made Leopold Aschenbrenner 12x on his money (1000% net of fees), and a SanDisk pick would’ve yielded 34x in the same period.

Growth of $100 from Apr 2025 · log scale

The same pattern, faster: SanDisk is a holding in Aschenbrenner’s book. Log scale. Source: public market total-return data, Apr 2025 to Jun 2026; fund figure as reported. Chart by chokepoints.ai.

Notice the direction of travel. The whole market made you 5x, big tech 8x, the ten giants 113x, the single chipmaker 684x, and the infrastructure names since 2025 more still. The alpha was never in the apps. It compounded one layer down: in the compute, the power, the memory. When compute was the chokepoint, Nvidia and its virtual monopoly won. When memory became the chokepoint, SanDisk won. When electricity was the chokepoint, Bloom Energy won.

We’re sceptics of our own convictions and quite reserved investors, but even then we’re up 80% YTD on actual portfolio net worth, not just a tiny pot.

The authors’ portfolio, year-to-date rate of return, account value redacted. Brokerage screenshot, 2026. Past performance is not indicative of future results.

Reading Issue 000? chokepoints.ai is a weekly research note on the AI-compute supply chain: where the bottlenecks are, and which companies benefit from them.

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02 What this is

Though the majority of foundation model opportunities have stayed private for a while, there are thousands of public companies further down the AI supply chain that you could’ve bought into, and we’re here to help you find the next batch.

With the chokepoints.ai tool and newsletter, we’ve mapped the end-to-end AI value chain, its 2,376 dependencies, and the 17,500 companies within it, so that you can understand what the core drivers, barriers and opportunities are. More importantly, we’ve highlighted the 95 chokepoints: the areas within AI where the bottlenecks are greatest. Over the next few months, we’ll be dissecting these even further to build a more comprehensive view of both the strength of each chokepoint and its geopolitical status.

Our objective is NOT to

• Help degens yolo into stocks.

• Promise 1,000% returns year on year.

• Give Twitter influencers ammo to copy.

We serve only to

• Educate you on what the full AI stack looks like.

• Open the discussion on interesting investment ideas in the space.

• Give the world a better dataset for the space: which skills, sectors and geographies are worth knowing.

03 Who we are

As co-authors, we’ve been fortunate to live in this world for a while now, one of us as a machine learning researcher at one of the UK’s leading AI labs, the other active in the Effective Altruism and AI safety movements since first year of undergrad at Oxford.

The chokepoints thesis has shaped bigger decisions than where we invest:

• Building and running the AI team at a top-decile private equity fund.

• Getting deeper into the x-risk AI safety movement, working with a range of organisations.

04 Why we bother

A word on why we bother, because this is a pillar of the whole project and not an aside.

There is an evolutionary mismatch between genes that change over generations and the exponential rate of progress in AI, and it is the core driver of humanity losing its status as the most powerful entity in the world. Just as we evolved from primates and eventually overpowered them, AI has evolved from humans and may overpower us.

The chokepoints are where the leverage is: the points at which this technology can still be steered, slowed, or governed. They are also where the returns are, and that is no coincidence. Scarce, hard-to-replace positions are what markets price and what policy pulls on. Understanding them is the best way to make sure that you benefit from the technology rather than lose out to it.

We take that seriously. It is precisely why we would rather you understood this infrastructure than looked away from it.

Whether you’re just getting started in AI or investing, looking to start or buy a business, or managing billions, we’re here to give you a real understanding of the picks and shovels of AI, one level deeper than the chips: the places the market has yet to price properly.

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Disclaimer. Chokepoints is for informational and educational purposes only. Nothing in this newsletter or the chokepoints.ai tool constitutes financial, investment, legal, or tax advice, an offer or solicitation to buy or sell any security, or a recommendation of any investment strategy. Always do your own research and consult a qualified financial adviser before making investment decisions. The authors may hold positions in companies or assets mentioned, and may buy or sell them at any time without notice. Parts of our research and writing are produced with the assistance of AI tools, which can make mistakes. Despite our review, figures, facts, and sources may contain errors, so verify anything material before acting on it. Past performance is not indicative of future results.

About the charts. Growth-of-$100 figures use public total-return market data; the top-10 basket is equal-weighted and rebalanced as constituents changed, as a thought exercise rather than an investable strategy; multiples are rounded. The fund figure is as publicly reported. The portfolio figure is the authors’ own brokerage screenshot, June 2026. Every chart reconstructed by chokepoints.ai.

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