But, does it really feel like that anymore? To me, it feels closer to 2008, where the institutional backing behind Bitcoin makes it “too big to fail”. Today, centralized exchanges handle ~86-90% of BTC volume [5]. And 40% of miners, which are the backbone of the network, are operated by publicly listed companies [6]. It doesn’t feel so peer-to-peer to me anymore.

Worse yet, the unit economics of mining bitcoin in the United States look bleak. With the surge in demand for AI inference, every Watt of power is a scarce resource that the capitalist machine needs to fight over. Theres a finite amount of power in the United States and it’s shifting away from Bitcoin and towards GPUs. In the last couple of months, how many times have you seen the headline: “US-based Crypto Mining Company XYZ has secured a deal with <Insert Hyperscaler> to repurpose their data center for High Performance Computing”. It’s become a trope at this point, but it’s a leading indicator of why cryptocurrency infrastructure is at a delicate point in time.

In this essay, we compare the unit economics of mining Bitcoin with serving AI inference, outline the seemingly inevitable migration of mining to emerging markets, and identify the companies positioned to benefit. Finally, we consider a strange but non-negligible possibility: that the United States subsidizes miners to keep the infrastructure securing its strategic reserve on American soil.

Show me the incentives

There’s few things that I believe in more than Charlie Munger’s famed quote: “Show me the incentives and I’ll show you the outcome” [7]. If I told a Meta software engineer that they could make $500K selling lemonade, I have a peculiar feeling that they would be on the corner of Broadway & Canal come Monday. So, to understand where Bitcoin mining goes next, we have to start with the incentives: what a megawatt earns today, what it could earn serving AI instead, and who captures the value when it moves.

As of July 12, 2026, the price of Bitcoin is roughly $64,129 [8]. Here’s what CoinShares estimates it currently costs top miners to mine it [9].

In the last 12 months, the following companies have begun complete transformations, working with hyperscalers to shift their data centers away from mining and towards AI inference [10][11][12][13][14]. Fun fact: In Q1 of 2026, the Bitcoin network’s hashrate dropped 4%, the first quarterly drop in 6 years [6]. CoinDesk explicitly tied it to miners pivoting to AI [6].

Cost per MW

Here is the nomenclature that you need to get familiar with and we’ll use throughout the remainder of the essay:

• Watt (W): The rate at which an entity draws energy. For example, a light bulb utilizes 10W while a blow dryer needs 1500W. Watt is a way to measure Power [15].

• Watt-Hours (Wh): The energy used when one watt is drawn for one hour. A 10W bulb running for an hour consumes 10Wh [15].

Watt-hour measures Energy (the amount), versus the Watt measures Power (the rate) [15].

• Kilowatt (kW): 1,000 Watts. For example, an average household draws 1kW on average [15][16]

• Megawatt (MW): 1,000,000 Watts. A single data center or bitcoin mining facility runs in the tens or hundreds of MW. This is the unit of measure that’s most useful to us.

• Gigawatt (GW): 1B Watts. A large nuclear reactor draws ~1GW and the entire Bitcoin network draws ~20GW [15][17][18]

The cost of a Megawatt-hour is not the same across the continental United States [19]. It’s not a coincidence that the largest Bitcoin mining facility in North America is Riot’s 700MW facility in Rockdale, Texas [20].

Becoming a hash-slinging slasher

A mining hash is one double-SHA-256 (SHA-256d) calculation over a block header, whose Merkle root commits the block’s transactions [1][21]. Mining is nothing more than computing these values over and over again, trillions of times per second, hunting for the one hash whose output falls below a target value set by the network [21]. Whoever finds the first valid output “earns” the block subsidy, which currently pays out 3.125BTC, plus transaction fees [22]. This is a random process, so the best way to increase your chances of finding the right output is to increase the # of guesses [1][21]. This is often referred to as the hashrate [21]. One TH/s = 1 Trillion Hashes per second and a modern bitcoin-mining ASIC can typically generate 100-500 TH/s [21][23].

So, how much Bitcoin can you mine with 1 megawatt-hour?

I apologize for the math ahead of time. You can either follow along or jump ahead (it won’t hurt my feelings), but it wouldn’t feel right if I didn’t outline it.

One of the best ASIC chips on the market is Bitdeer’s SEALMINER A4, which does 886 TH/s on 8373 Watts, which translates to roughly 9.5J/TH [23]. But, that is a frontier ASIC, not the average machine operating across the network. The Cambridge Centre for Alternative Finance estimated a blended fleet efficiency of 28.2 J/TH in 2024 [18]. To account for subsequent hardware improvements, we use ~20 J/TH as our current blended-fleet assumption.

• Over an entire day, the Bitcoin network grinds through 8.6 x 10e25 hashes (1ZH/s x 86,400 seconds per day)

• The network produces an average of 450 BTC in block subsidies per day (3.125 BTC approximately every 10 minutes)

• So, 1 Bitcoin ~ 1.9 x 10e23 hashes

• One MWh = 3.6 x 10e9 Joules & that amount of energy buys you 1.8 x 10e20 hashes

Therefore, 1MWh represents 0.0009375BTC, which is $61, assuming the price of BTC is $65,000 [18][22].

Using statewide industrial electricity averages, if you mine that in Texas, it will cost you $63 in electricity, resulting in a loss of $2. If you mine that in California, it will cost you $199, a loss of $138 [19]. Running continuously for a year, one megawatt generates ~$534,000 of topline revenue [8][18][22].

Note: This doesn’t include land, staff, and other operating costs, which is why the cumulative costs to mine a Bitcoin are significantly more than just the cost of energy. See the first graphic above.

Follow the Money

Now that we’ve established the rough economics of mining Bitcoin, let’s look at the economics behind inference. At the moment, there are two main business models that miners are shifting to [10][11][12][24]. While both of them rely on taking GPUs and plugging them into the existing power infrastructure, the main question to ask is:

Who owns the GPUs?

Colocation

In a colocation arrangement, the Bitcoin miner supplies the scarce physical infrastructure: land, a grid interconnection, substations, transformers, a building, cooling, networking, and an agreed amount of energy capacity [24]. The tenant, usually a hyperscaler or a neocloud, supplies the GPUs and is responsible for operating them [24]. The miner is not responsible for inference. It is not training models. It does not own the GPUs [24]. It has effectively leveraged its existing power supply and transitioned into a highly specialized landlord.

Here are three of the disclosed contracts [11][12][24].

This gives us a defensible public-market range:

One megawatt of gross site power allocated to AI colocation can generate approximately $1.0–1.5 million of annual revenue, which is ~2-3x more than Bitcoin mining [11][12][24].

The contracts are usually quoted per megawatt of critical IT capacity, which excludes cooling and other facility overhead, while our Bitcoin figure uses total electrical input. Normalizing the disclosed contracts to gross site power lowers Core Scientific from $1.46M to approximately $1.04M per MW-year and TeraWulf from $1.85M to approximately $1.48M [11][12][24].

Even after normalizing the denominator, this topline comparison understates the economic difference between the two scenarios. With mining, the operating costs are higher given the miner has to also pay for electricity and refreshing ASIC servers [9][18]. In the colocation model, the contracts specify that electricity costs and capital expenditure are (partially) passed onto the tenant, with revenues contracted 10-15 years in advance [11][12][24]. Meanwhile, mining revenue is tethered to the price of Bitcoin, which could go up. Or down, on any given day [22].

Neoclouds

The neocloud model is far more ambitious, difficult, and financially lucrative. $IREN is currently the leading public Bitcoin miner pursuing this model at scale, where the miner purchases, maintains, and operates the GPU infrastructure itself [10]. This removes a significant pain point for the hyperscaler, whose focus now shifts to orchestrating the workloads running on top of the GPUs. More specifically, $IREN will spend approximately $5.8B purchasing GPUs and deploying them across four data centers representing 200MW of capacity [10]. In exchange, Microsoft will pay $IREN approximately $9.7B for dedicated access to that compute over an average five-year term [10].

We can construct a rough comparison by subtracting the cost of the GPUs from the contract’s topline revenue:

$9.7B of revenue − $5.8B of GPU capex = $3.9B of remaining spread [10].

Across 200MW and five years, that equals approximately $3.9 million per MW per year [10]. This is still not an apples-to-apples comparison, as $IREN’s remaining spread still needs to take into consideration electricity, data center construction, financing, networking, and expert staff to make all of this work [10].

But the rough #’s are still compelling:

• Bitcoin Mining: $534K per MW [8][18][22]

• Colocation: $1.0-1.5M per gross MW [11][12][24]

• Neocloud, after GPU capex: $3.9M per MW [10]

Another fun fact. The next Bitcoin halving is in mid-2028, which will decrease the block subsidy from 3.125BTC to 1.5625BTC [22]. If the price of Bitcoin remains the same, revenue is cut in half. If the price of Bitcoin doubles, the revenue remains the same [22].

Offshoring

When I started working on this essay, I wanted to come to the conclusion that electricity was two to ten times cheaper in emerging markets. If miners could pay less than 1¢ per kWh in Paraguay or Ethiopia instead of 6¢ in Texas, offshoring would be a no-brainer. Unfortunately, this thesis was fundamentally wrong & the data does not support that argument. Publicly disclosed mining contracts generally cluster around 4–6¢ per kWh, suggesting a rough floor for industrial-scale power rather than some enormous geographic arbitrage [18][25][26].

The real advantage abroad is not necessarily cheaper electricity. It is less competition for the same electricity. A megawatt in Texas is increasingly contested by Bitcoin miners, hyperscalers, neoclouds, and enterprise data centers [27]. A megawatt of surplus hydroelectricity in Bhutan, Ethiopia, or Paraguay may have fewer comparable bidders [25][28]. Many enterprise AI workloads also carry latency, security, and data-sovereignty requirements that favor American infrastructure [29]. Bitcoin does not. The network treats a hash generated in Ethiopia exactly like one generated in Texas, and the resulting Bitcoin can be transferred anywhere in the world [1][21].

The international beachheads already exist. $BTDR, a Singapore-based company, operates 600MW in Bhutan and 50MW in Ethiopia [28]. $HIVE operates 300MW of hydro-powered mining capacity in Paraguay [25]. These are not empty facilities waiting to absorb America’s miners overnight. They are existing grid connections, operating teams, regulatory relationships, and import channels that give their owners a head start when the economics justify further expansion [25][28].

Today, the United States is estimated to control approximately 37.8% of global hashrate, or 389 EH/s [9]. We predict that AI will displace 50–75% of that capacity, creating approximately 195–292 EH/s of geographic rebalancing. In our default 50% scenario, 194.5 EH/s and approximately $1.99 billion of annual gross mining revenue are exposed to geographic reallocation away from the United States [8][9][22]. That does not mean all $1.99 billion automatically moves internationally. Once mining difficulty adjusts, the revenue is redistributed among the miners that remain online, wherever they are located [21].

Legislation solves everything

I hope the sarcasm in that title is obvious. Imagine the following headline appearing in 2028:

WASHINGTON — The Treasury Department today announced the first payments under the American Hashrate Security Act, establishing a minimum revenue guarantee for Bitcoin mined on American soil. “The infrastructure securing a strategic American asset cannot be allowed to migrate overseas,” the Energy Secretary said. Critics describe the program as a taxpayer-funded bailout for an industry that’s struggled to prove its utility to the average American household.

The American Hashrate Security Act does not exist. But the incentives that could produce it are beginning to form.

In March 2025, the United States established a Strategic Bitcoin Reserve [30]. That decision created a strange contradiction. The government declared Bitcoin strategically important [30] at the same time that markets began paying American miners to do something else. If AI continues absorbing the country’s data-center capacity, Washington may eventually discover that owning the asset and controlling the infrastructure underneath it are two different things.

We have seen the United States make the same mistake before. Semiconductor fabrication moved overseas because manufacturing was cheaper [31]. And then chips became strategically important and Congress passed the CHIPS Act [31]. Rare-earth processing had consolidated in China [32]. And then the Department of Defense began funding domestic production and guaranteeing minimum prices [33]. The government repeatedly allows markets to offshore critical infrastructure & then spends billions attempting to bring it back [31][33].

Bitcoin mining could follow the same playbook. A future subsidy might take the form of a production tax credit for domestically mined Bitcoin, subsidized power contracts, accelerated depreciation on American mining equipment, or a minimum hash price guaranteed by the government. Any of these policies would improve the economics of mining relative to AI without requiring the government to explicitly nationalize the industry.

Whether that would be good policy is an entirely different question. Subsidizing Bitcoin mining would divert scarce electricity from other uses, reward companies that willingly converted their facilities to AI, and force taxpayers to support an industry specifically designed to operate without government support. It would be an extraordinarily ironic conclusion to Bitcoin’s institutionalization.

References

[1] Satoshi Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System” — https://bitcoin.org/bitcoin.pdf

[2] Brian Armstrong, “How crypto enables economic freedom” — https://www.coinbase.com/en-ca/blog/how-crypto-enables-economic-freedom

[3] Coinbase Advanced, derivatives trading and leverage disclosures — https://www.coinbase.com/derivatives-trading

[4] Coinbase Q3 2025 Shareholder Letter — https://investor.coinbase.com/files/doc_financials/2025/q3/Q3-25-Shareholder-Letter.pdf

[5] CoinGecko, “CEX & DEX Trading Activity Report 2026” — https://www.coingecko.com/research/publications/cex-dex-trading-activity-report-2026

[6] CoinDesk, “Bitcoin hashrate posts first-quarter drop for first time in 6 years as miners pivot to AI” — https://www.coindesk.com/markets/2026/03/30/bitcoin-hashrate-posts-first-quarter-drop-for-first-time-in-6-years-as-miners-pivot-to-ai

[7] Mungerisms, “Charlie Munger’s 25 Cognitive Biases” — https://www.mungerisms.com/cognitive-biases

[8] Kraken, Bitcoin price page — https://www.kraken.com/prices/bitcoin

[9] CoinShares, “Bitcoin Mining Report — Q1 2026” — https://coinshares.com/no-en/insights/research-data/bitcoin-mining-report-q1-2026/

[10] IREN Q2 FY2026 Form 10-Q, Microsoft and Dell agreements — https://www.sec.gov/Archives/edgar/data/1878848/000187884826000015/iren-20251231.htm

[11] Hut 8, “Hut 8 Signs 15-Year, 245 MW AI Data Center Lease at River Bend Campus with Total Contract Value of $7.0 Billion” — https://canada.hut8.com/resources/press-releases/hut-8-signs-15-year-245-mw-ai-data-center-lease-at-river-bend-campus-with-total-contract-value-of-usd7-0-billion

[12] TeraWulf, “TeraWulf Signs 200+ MW, 10-Year AI Hosting Agreements with Fluidstack” — https://investors.terawulf.com/news-events/press-releases/detail/112/terawulf-signs-200-mw-10-year-ai-hosting-agreements-with-fluidstack

[13] Riot Platforms Q1 2026 results, SEC Exhibit 99.1 — https://www.sec.gov/Archives/edgar/data/1167419/000110465926052943/riot-20260430xex99d1.htm

[14] Cipher Mining, September 25, 2025 SEC Exhibit 99.1 — https://www.sec.gov/Archives/edgar/data/1819989/000095010325012168/dp234624_ex9901.htm

[15] U.S. Energy Information Administration, “Measuring electricity” — https://www.eia.gov/energyexplained/electricity/measuring-electricity.php

[16] U.S. Energy Information Administration, “Electricity use in homes” — https://www.eia.gov/energyexplained/use-of-energy/electricity-use-in-homes.php

[17] U.S. Energy Information Administration, “The United States operates the world’s largest nuclear power plant fleet” — https://www.eia.gov/todayinenergy/detail.php?id=65104

[18] Cambridge Centre for Alternative Finance, “Cambridge Digital Mining Industry Report” — https://www.jbs.cam.ac.uk/faculty-research/centres/alternative-finance/publications/cambridge-digital-mining-industry-report/

[19] U.S. Energy Information Administration, Electric Power Monthly Table 5.6.A — https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_5_6_a

[20] Riot Platforms, Rockdale Facility — https://www.riotplatforms.com/bitcoin-mining/rockdale-2-navy/

[21] Bitcoin Developer Guide, “Mining” — https://developer.bitcoin.org/devguide/mining.html

[22] MARA Holdings 2024 Form 10-K — https://www.sec.gov/Archives/edgar/data/1507605/000150760525000003/mara-20241231.htm

[23] Bitdeer, “Bitdeer Launches SEALMINER A4 Series Bitcoin Mining Rigs” — https://ir.bitdeer.com/news-releases/news-release-details/bitdeer-launches-sealminer-a4-series-bitcoin-mining-rigs

[24] Core Scientific, June 4, 2024 Form 8-K Exhibit 99.1 — https://investors.corescientific.com/sec-filings/all-sec-filings/content/0001628280-24-026483/corzcoreweavehpc.htm

[25] HIVE Digital Technologies FY2026 Form 10-K — https://www.sec.gov/Archives/edgar/data/1720424/000106299326002973/form10k.htm

[26] Bitdeer Technologies Group 2025 Form 20-F — https://ir.bitdeer.com/static-files/9b19f847-0428-4661-b9d7-0e8e29e72ef7

[27] U.S. Energy Information Administration, “Data centers and cryptocurrency mining in Texas drive strong power demand growth” — https://www.eia.gov/todayinenergy/detail.php?id=63344

[28] Bitdeer, April 2026 Production and Operations Update — https://ir.bitdeer.com/news-releases/news-release-details/bitdeer-announces-april-2026-production-and-operations-update

[29] Microsoft Azure Architecture Center, “Multitenancy and Azure OpenAI” — https://learn.microsoft.com/en-us/azure/architecture/guide/multitenant/service/openai

[30] Executive Order 14233, “Establishment of the Strategic Bitcoin Reserve and United States Digital Asset Stockpile” — https://www.federalregister.gov/documents/2025/03/11/2025-03992/establishment-of-the-strategic-bitcoin-reserve-and-united-states-digital-asset-stockpile

[31] Congressional Research Service, “Frequently Asked Questions: CHIPS Act of 2022 Provisions and Implementation” — https://www.congress.gov/crs-product/R47523

[32] U.S. Department of Energy, “Rare Earth Permanent Magnets: Supply Chain Deep Dive Assessment” — https://www.energy.gov/sites/default/files/2024-12/Neodymium%20Magnets%20Supply%20Chain%20Report%20-%20Final%5B1%5D.pdf

[33] MP Materials, July 2025 Department of Defense transaction presentation, SEC Exhibit 99.2 — https://www.sec.gov/Archives/edgar/data/1801368/000119312525157310/d43796dex992.htm

If you had bought $50K of in September of last year, it’d be worth $489,255 today. If you had bought the same amount of SK Hinyx Inc, it’d be worth $521,000. Either you don’t own these stocks and are thinking to yourself “Fuck, am I too late?”. Or, you own at least one of them and are asking yourself daily “Am I about to be the exit liquidity?”.

This essay makes the argument that memory stocks can still 10x from current valuations, if one’s reasoning is not rooted in the psychological constraints of historical performance and all-time highs. Instead, we conduct a pseudo-technical, bottoms-up analysis that demonstrates that the demand for memory is several magnitudes of order greater than what the world can currently produce.

A Single Chat Message

Let’s start off with the basics, which hopefully everyone is familiar with, and then we’ll work our way down to get a deeper understanding of how much memory is needed on a per-chat basis. When you send a message in ChatGPT, this is the rough outline of what happens underneath the hood. As your dopamine-addicted mind awaits a response from the appropriately named “Token Casino” [1], your message is tokenized, scheduled, and routed to an available GPU. At that point, the translated vector is run through billions of matrix multiplications and out comes the most likely next word. This is repeated over and over again, usually in pieces until the response is completed, i.e [“It’s 72F”, “and sunny.”].

If you’d like a more detailed understanding, the following article has a great breakdown: https://www.0xkato.xyz/how-llms-actually-work/

Now, smaller models (Llama 3.1-8B or Mistral-7B) can fit onto a single GPU, meaning there’s no cross-GPU communication required to generate a response. The singular GPU has a copy of all of the weights and all of the high-bandwidth memory (HBM) is localized on the server. But, frontier models, with billions, or even trillions, of parameters, need to be served with a group of GPU(s), often times known as a cluster. More specifically, it requires what’s called tensor parallelism [2], where the weight matrices are split across GPU(s) and every forward pass requires them to exchange intermediate activations over NVLink. This is mandatory, not optional. For simplicity, depicted below is an 8-GPU cluster serving a single request.

The Memory Budget

Each GPU ships with a fixed amount of high-bandwidth memory (HBM). This memory is soldered on to the chip and it is not expandable. For reference, the standard H100 SXM5 that sits inside every DGX server only has 80GB [3]. That’s it. Now, of course, newer generations push this limit higher, but there’s still a ceiling when it comes to how much memory a GPU has access to.

• H100 SXM5: 80GB

• H200: 141GB

• B200: 192GB

• Upcoming B300: 288GB

When most people think about memory, their mind jumps to model weights, which are the fixed set of parameters that define what a model knows. But, the other part of the equation is the Key-Value Cache, a per-session memory store that grows with every token generated. Importantly, model weights are fixed in size. But, the KV Cache scales linearly with the context length and becomes concerning when the # of concurrent sessions grows.

I introduce the concept of a “Session” here purposefully. For most people, a “Chat” is equivalent to a Session. But, in the world of agents, the vast majority of sessions are not controlled by or even visible to humans via a chat interface. Instead, they are programmed by software engineers, who are able to spin up and orchestrate thousands, if not hundreds of thousands of sessions in parallel. This is one of my main qualms when people try to anecdotally reason through how many GPU(s) or how much memory the world needs. They fixate on the # of chat sessions that they or their friends create. But, they remain oblivious to the # of sessions that their favorite products will run on behalf of and unbeknownst to them.

Now, let’s do some math with Llama 3.1-70B [4], which is a real model with published specifications. The calculation below shows that 1 token consumes 160KB of memory.

KV Cache Per Token = 2 x Layers x KV Heads x Head Dimensions x 1 Byte
                   = 2 x 80 x 8 x 128 x 1
                   = 163,840 Bytes
                   = 160KB Per Token

And if you extrapolate that out, a single session with 128K tokens of context requires ~20GB of memory. That’s just for one user. Four concurrent sessions at max context and you’ve already exhausted an entire H100.

Now, frontier models, like Anthropic’s Opus-4.8 or Open AI’s GPT-5.5, are larger but the KV cache does not simply scale with the # of parameters. Instead, it scales with the model’s attention dimensions, which is typically 2-5x larger, meaning it can require 40-100GB of memory to serve a 128K token request. This is why every additional gigabyte of HBM matters and why memory suppliers like Micron and SK Hinyx can’t build this shit fast enough.

Not All Sessions Are Equal

Not every session is a quick “What’s the weather” query in ChatGPT. And the distribution of complexity matters enormously when trying to calculate how much memory is needed.

A casual user asking a simple question? Well, it requires almost no memory and you can serve thousands of these requests on a single GPU. An indie developer debugging their React application? Well, that requires ~800MB of memory and now you’re only able to serve ~60 concurrent sessions. What about a lawyer uploading a 50-page contract that they need to redline? Well shit, that requires almost 5GB of memory to handle and you’re quickly down to 10 sessions on a single GPU.

Finally, there’s the agentic workload: the self-looping, context-consuming behemoth that can quickly get to 100K+ tokens, if not more. It turns out that these workloads, promised to us by Sam Altman and Jensen Huang, require a lot of memory. More importantly, this is where the world is moving and the types of sessions investors should anticipate.

The Agentic Effect

Here’s an example of an Agentic session. I’d encourage you to click the link & skim it, because most people are not that familiar with tool calling and they don’t have a great intuition for how quickly it can consume memory:

https://diagrams.thezeitgeistlabs.com/how-much-memory-do-we-need/agentic-session

Look at how fast 100K tokens is needed for a relatively simple workflow that any AE or CSM might run numerous times per day. I don’t know about you, but it’s not that hard for me to envision every knowledge worker firing off dozens of these task, in parallel, over and over again for 8-10 hours a day.

Insatiable Demand

Humans are greedy fuckers. We get used to cool shit quickly. And when we get used to something, we want more. Headlines like this have already started popping up but they will only become more & more frequent (or we’ll get used to overspending headlines and stop publishing them).

Let’s look at the usage for a single knowledge worker. Memory isn’t consumed cumulatively, it’s allocated when a session starts and it’s freed when it ends. What matters is peak concurrent memory: how many sessions are live at the same time.

The quick chats are noise; the entire memory budget is dominated by agentic sessions. And a knowledge worker running ten concurrent agents at 100K tokens each needs ~152GB of memory to support that level of traffic. There are an estimated 250 million knowledge workers globally [6]. Multiply that by the # of concurrent agentic sessions each worker runs, and the memory demand doesn’t just grow, it explodes.

The Current State of Affairs

Based on research, this is our estimate on the current levels of HBM production.

These are estimates that are triangulated from: company bit-shipment disclosures, HBM/DRAM mix commentary, publicly reported HBM-dedicated capacity by vendor, company comments on sold-out supply and pricing/volume agreements, and external hard constraints such as wafer intensity and packaging capacity. The methodology is described in more detail within the Appendix.

If you compare this table against the graphic above, at 100 agentic sessions per knowledge worker per day, the world needs 385 Billion Gigabytes of HMD to serve that level of traffic. Thats ~60x more than the amount that’s going to be produced in 2026.

Algorithmic Improvements

The 160KB/token math is a base case. It assumes multi-head attention on a mid-size open source model. Frontier models already use Grouped Query Attention (GQA) and Multi-Latent Attention, which can cut KV cache usage by 4-8x. And the optimization roadmap won’t stop there. More broadly, frontier AI labs are allocating significant engineering and research resources to lower memory usage during inference. China is GPU-constrained and scarcity breeds invocations: DeepSeek’s MLA architecture is a direct example of this.

So why does the thesis still hold? Because algorithmic improvements buy you constant factors: 4x, 8x, maybe 16 over the next few years. The demand side is still growing by 100-100x as agents replace chat, context windows expand from 128K to 10M tokens, and every knowledge worker goes from 0 → 100 in AI usage. The purpose of this essay is to give you the building blocks to do order-of-magnitude math yourself. Plug in your own assumptions about adoption rates and architectural improvements to see where you think HBM is headed.

Final Words

It turns out that we need a metric fuck ton of memory. Financial analysts are worriedly looking at historical performance or all-time highs as rationale for what to do next. They struggle to imagine a world where LLMs are not just helpful, but are intertwined in every aspect of daily life. In that world, memory is crucial and whatever companies produce it will achieve unprecedented amounts of revenue.

Appendix: HBM Supply Methodology

The estimates in “The Current State of Affairs” are derived from two independent methods, then triangulated against each other. Neither method alone is precise enough to narrow the range, but their overlap gives us confidence in the central estimates.

Method 1: Wafer Capacity → GB Output

Each HBM vendor operates a known number of wafer starts per month, reported by TrendForce and cross-referenced against company disclosures. We convert wafer starts to GB output using a conservative model:

Effective output per HBM wafer: 1.1–1.4 TB (HBM3E generation)

This is derived from:

• Samsung HBM3E 12-high uses 24 Gb DRAM dies, delivering 36 GB per stack [19]

• Micron HBM3E is an 8-high 24 GB stack with 50% higher monolithic die density [18]

• Die size ~121 mm² (estimated), on a standard 300mm wafer

• Conservative all-in haircut for wafer yield + stack/test losses

• Consistent with Micron’s stated 3:1 trade ratio with DDR5 [9]

Capacity Data

Samsung’s 2025 effective average is far below nameplate capacity because its HBM ramp was severely back-end loaded. Q1 2025 was weak due to export controls, HBM bit shipments grew 30% QoQ in Q2 [22], and then surged mid-80% QoQ in Q3 [23]. SemiAnalysis also reported Samsung’s HBM yields lagged SK Hynix and Micron through mid-2025 [21].

Implied Output

Method 2: Revenue ÷ ASP Cross-Check

Micron disclosed the 2025 HBM market TAM as ~$35B [9]. Bank of America estimates the 2026 HBM market at $54.6B [20]. Applying blended ASP estimates of $8–$11/GB (2025, HBM3E-heavy) and $10–$14/GB (2026, HBM4 mix shift):

We then split by market share. As of Q1 2026, Counterpoint Research reports SK Hynix at 58%, Samsung at 21%, and Micron at 21% [16].

Micron-specific cross-check: Micron reported HBM revenue of nearly $2B in fiscal Q4 2025 alone [10]. This implies a full-year 2025 HBM revenue of ~$4.5B–$6B (the ramp was heavily back-loaded), which at $8–$11/GB gives 0.4B–0.75B GB — consistent with the wafer model’s 0.5B–0.85B range.

Triangulation

The two methods produce overlapping ranges for all three vendors. The central estimates are picked from the overlap:

Key Caveats

• “Produced” ≈ “shipped” right now. All three vendors describe sold-out supply conditions. SK Hynix says HBM supply volumes are agreed roughly a year in advance [11]. Micron says price and volume are already set for all of calendar 2026 HBM supply [9]. Inventory swings are much less important than in commodity DRAM.

• The largest uncertainty is effective GB per HBM wafer. Public yield and die-size data are incomplete. The ranges above use conservative assumptions, but vendor-grade yield disclosures would narrow them significantly.

• 2026 estimates assume no demand destruction. If a major customer cancels or delays orders, actual shipments could come in below projections — though current evidence (multi-year supply agreements, sold-out capacity) suggests this is unlikely.

References

[1] https://x.com/vral/article/2064067264991654236

[2] https://people.eecs.berkeley.edu/~matei/papers/2021/sc_megatron_lm.pdf

[3] https://resources.nvidia.com/en-us-gpu-resources/h100-datasheet-24306

[4] https://huggingface.co/meta-llama/Llama-3.1-70B

[5] https://platform.claude.com/docs/en/managed-agents/define-outcomes

[6] https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages

[7] https://platform.claude.com/docs/en/managed-agents/define-outcomes
[8] https://ai.meta.com/blog/llama-4-multimodal-intelligence/
[9] Micron FQ1 2026 Prepared Remarks (Dec 17, 2025) — https://investors.micron.com/static-files/088991c5-a249-4f66-a0a6-258d9b66f3f9
[10] Micron FQ4 2025 Prepared Remarks (Sept 23, 2025) — https://investors.micron.com/static-files/5ea95475-639b-4cfc-91fd-b9b4a2bb5e63
[11] SK Hynix Q3 2025 Financial Results (Oct 29, 2025) — https://news.skhynix.com/sk-hynix-announces-3q25-financial-results/
[13] TrendForce — Samsung Reportedly Plans 50% HBM Capacity Surge (Dec 30, 2025) — https://www.trendforce.com/news/2025/12/30/news-samsung-reportedly-plans-50-hbm-capacity-surge-in-2026-spotlight-on-hbm4/
[14] TrendForce — Micron Reveals Three Culprits Behind Memory Crunch (Dec 18, 2025) — https://www.trendforce.com/news/2025/12/18/news-micron-reveals-three-culprits-behind-memory-crunch-and-why-it-wont-ease-soon/
[16] Counterpoint Research — Global DRAM and HBM Market Share Q1 2026 — https://counterpointresearch.com/en/insights/global-dram-and-hbm-market-share
[18] Micron HBM3E Product Page — https://www.micron.com/products/memory/hbm/hbm3e
[19] TechInsights — Samsung 12Hi HBM from AMD MI350X Floorplan Analysis — https://www.techinsights.com/blog/samsung-12hi-hbm-amd-mi350x-floorplan-analysis
[20] SK Hynix 2026 Market Outlook (cites Bank of America HBM TAM estimate) — https://news.skhynix.com/2026-market-outlook-focus-on-the-hbm-led-memory-supercycle/
[21] SemiAnalysis — Scaling the Memory Wall: The Rise and Roadmap of HBM —
[22] Samsung Q2 2025 Earnings Call — https://www.alphaspread.com/security/krx/005930/investor-relations/earnings-call/q2-2025
[23] Samsung Q3 2025 Earnings Call — https://www.alphaspread.com/security/krx/005930/investor-relations/earnings-call/q3-2025
[24] McKinsey Global Institute — Jobs lost, jobs gained: Workforce transitions in a time of automation (2017) — https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages

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Despite the market downturn since early October, my portfolio has handsomely increased by more than 70%+ over the same time period. Several of my friends have asked me how so I decided to capture all of the details below.

Since early October, I have been shorting Rigetti Computing, $RGTI, a Berkeley-based company focused on building quantum computers. As of today, the company is worth $7.78B, while generating $7.49M in revenue for the trailing twelve months [1]. That’s roughly a 1040x multiple on revenue. For context, Nvidia is trading at 23.33x, Google is trading at 9.13x, and even the over-valued & psuedo-consulting software company Palantir is trading at 94.73x. I think that retail investors think these ratios are “nice-to-haves” and optional when investing in stocks. But, revenue multiples are to investment bankers what bytes are to software engineers, they’re a core concept and a stark reminder that companies are supposed to make money.

Frankly, the company doesn’t make much money. The company’s Q3 earnings report showed a ~20% decrease in year-over-year revenue [2], reporting a whopping $1.9M in quarterly revenue as compared to $2.4M in 2024. $IONQ, another popular quantum computing company, reported $39.9M in quarterly revenue, a 222% year-over-year growth, showing that positive and even hyper growth is possible.

Last November, the stock was at $1. Let’s assume that retail and institutional investors missed out on AI, are excited about an emerging technology, and want to get in early on quantum computing. To do so, they’re willing to pay a 2-5x increase in the multiple of these companies. In that world, $RGTI should still only be $2-5. The company’s plan is to build a 150+ qubit system by the end of 2026 and a 1000+ qubit system by the end of 2027. To me, it sounds like they’re going to spend at least two more years burning money & building something that has no clear commercial viablity.

If you have confidence in the stock, that’s good. Because the CEO doesn’t. He exercised the right to purchase 1M shares of $RGTI in early May. In the same transaction, he sold every single share for $12 [4]. Yes, he has more unvested shares (approximately ~1.5M if my understanding is correct), giving him an incentive in the long-term success of the company. But, if you thought your company would be worth 2-3x in the next several years, why would you sell all of your shares?

In general, larger companies like Google and IBM are pouring large amounts of money into quantum with no desire to see returns in the short-term. This makes it harder for me to belive that a smaller company like $RGTI can compete and innovate in the space. $RGTI doesn’t have the same luxury of time & I question their solvency on a 3-5 year time horizon. Both Bill Gates and Google’s Head of Quantum acknowledge that we will see real-world commercial applications for quantum, but that it will take at least 3-5 years to do so. For what it’s worth I’m not the only person exclaiming that quantum is a bubble. You can read Martin Shkreli’s post on r/wsb on why he’s short these companies as well.

I do not recommend shorting or buying put contracts on $RGTI. I actually got a little greedy & bought several thousand dollars worth of short-term contracts that ultimately expired worthless. Instead, I would buy $RGTZ, a Daily 2x Short ETF in the share price of $RGTI. Roughly speaking, for every $1 that $RGTI goes down, $RGTZ goes up $2. I entered the position on 10/23/2025 when $RGTZ was $14.35. I exited the position on 11/17/2025 when $RGTZ was $27.12, representing an 89% gain in just several weeks time. I exited in anticipation of $NVDA earnings, but will be re-entering the position on 11/24+. I do acknowledge that this early success falls on good timing, as I entered the position during a market pulldown. That being said, I fundamentally think this stock is overvalued and feel comfortable holding it for several months until $RGTI hits $10-14. Afer that, I’ll probably sell, despite it being likely that this piece of shit stock could be worth less than a $1.

As a side note, here’s Warren Buffet’s take on diversification. In general, I think if you feel that you see something others don’t, taking concentrated bets on that idea leads to outsized returns, whether it’s in investing or building.

This is not financial advice, but the ramblings of a 27 year-old trying to break out of the permanent underclass.

References

[1] https://stockanalysis.com/stocks/rgti/revenue/

[2] https://investors.rigetti.com/news-releases/news-release-details/rigetti-computing-reports-third-quarter-2025-financial-results

[3] https://ionq.com/news/ionq-announces-third-quarter-2025-financial-results

[4] https://www.sec.gov/Archives/edgar/data/1388191/000106299325009994/xslF345X05/form4.xml

[5] https://thequantuminsider.com/2025/02/05/google-quantum-ai-head-sees-commercial-quantum-within-five-years/