
Dad, Dev, CEO, 4x Founder. Building @HelloUntangle - AI for family law
Connecticut, USA
Joined November 2006
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Ryan Carson retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
Ryan Carson retweeted
We independently benchmarked Devin Fusion for its release today - this is the first time a multi-model coding agent has been included on the Artificial Analysis Coding Agent Index, and it effectively retains Claude Fable 5.1 and GPT-6 Astra performance while reducing costs
Devin Fusion runs a frontier lead model with a cost-efficient sidekick. We tested configurations from Cognition combining frontier models from Anthropic and OpenAI with their new SWE-2 (medium) as a sidekick model. Configured with Claude Fable 5.1 (xhigh) + SWE-2 (medium), Devin Fusion scores 62 on the Coding Agent Index v1.5, while with GPT-6 Astra (xhigh) + SWE-2 (medium) it scores 59. The Fable configuration has the higher score, while the Astra configuration is 43% less expensive and completes tasks 31% faster.
Congratulations to @cognition on the release! See below for our results and analysis 🧵
Ryan Carson retweeted
Woohoo! Codex mobile is here, update your ChatGPT app and Codex macos apps, and you can now remote control your Codex on the go!
It's really well executed, I've connected both my laptop and my mac mini and I can control them separately! 🔥
I just asked Codex on mobile to check my messages from Imessage and it worked 🎉
@ryancarson just said on the show today that he replaced his OpenClaw with Codex, and I bet he's happy now that he can control his Codex from his phone!
P.S - there's another tab there that allows you to control other devices using Codex, but it doesn't seem to work yet, maybe they flipped on one too many feature flags and it's an upcoming feature?
Ryan Carson retweeted
Karpathy described the self-improving loop. This week OpenAI AND Anthropic shipped it.
@ryancarson and @yampeleg break down Codex /goal vs Claude Outcomes on ThursdAI
You don't have to build your own automation loops anymore. The agents are doing it for you.
Ryan Carson retweeted
BREAKING:
GPT-5.5 "Spud" is out and it is a BEAST
We've been testing it @every for the last 3 weeks on everything from coding, to writing, to knowledge work. Here's our day 0 vibe check:
- It's a step change in coding AND it's easy to talk to. It's fast and friendly and quickly became my daily driver. But it's also a coding powerhouse—a really rare combination.
- It scored 62/100 on our Senior Engineer benchmark. Opus 4.7 scored only a 33/100. (But GPT-5.5 performed best when using an Opus 4.7 plan). @naveennaidu_m used over 900 million tokens during testing—and it let him ship production features for @usemonologue at both high speed and quality.
- It has serious conceptual clarity. It can hold a complex plan in its head over hours of work, without getting distracted by existing code. This makes it the first model that we've tested that can perform well on complex refactors requiring deleting and reimagining an substantial existing codebase.
- It's a very good writer. This is the first OpenAI model in about a year that got our writers @every to switch away from Claude. 5.5 has @kplikethebird's seal of approval—not an easy task. Its writing feels more organic and it's better at mimicking a writing style without going overboard.
- It's great for agentic knowledge-work. This is the first OpenAI model that manages to be both a stellar senior engineer AND that can be used for everything from spreadsheets to research. It's crazy fast, and it's amazing inside of the Codex desktop app, and got much of our team to switch away from Claude Code and Cowork during the testing period.
However, it's not a perfect model.
- 5.5 still loses to Opus 4.7 on plan quality. It's plans are extremely readable but Opus has better attention to detail and sharper insight.
- 5.5 still loses to Opus 4.7 by a bit on front-end and full-stack product work. @kieranklaassen found that it wasn't quite as good when full-stack thinking and design are involved. And it's not great writing Ruby.
- 5.5 is a great vibe coder but if you're vibe coding without a plan it's worse than Opus. @hammer_mt found that Opus is better at reading in between the lines on underspecified vibe-coding tasks.
Overall GPT-5.5 is a massive achievement from OpenAI and it deserves a serious look as your daily driver.
Read our full vibe check on @every here: every.to/p/gpt-5-5
Ryan Carson retweeted
Ok last one: the rarest solar eclipse of all time. Only 4 people have seen this with their naked eyes. The sun is fully behind the moon. The only faint light hitting the near side is reflecting off of earth, 250,000 miles away. And the stars and galaxies in the background, sheesh
Nikon Z9
f/2.0
2 second exposure
ISO 1600
@NASA: flickr.com/photos/nasa2explo…
Ryan Carson retweeted
Software horror: litellm PyPI supply chain attack.
Simple `pip install litellm` was enough to exfiltrate SSH keys, AWS/GCP/Azure creds, Kubernetes configs, git credentials, env vars (all your API keys), shell history, crypto wallets, SSL private keys, CI/CD secrets, database passwords.
LiteLLM itself has 97 million downloads per month which is already terrible, but much worse, the contagion spreads to any project that depends on litellm. For example, if you did `pip install dspy` (which depended on litellm>=1.64.0), you'd also be pwnd. Same for any other large project that depended on litellm.
Afaict the poisoned version was up for only less than ~1 hour. The attack had a bug which led to its discovery - Callum McMahon was using an MCP plugin inside Cursor that pulled in litellm as a transitive dependency. When litellm 1.82.8 installed, their machine ran out of RAM and crashed. So if the attacker didn't vibe code this attack it could have been undetected for many days or weeks.
Supply chain attacks like this are basically the scariest thing imaginable in modern software. Every time you install any depedency you could be pulling in a poisoned package anywhere deep inside its entire depedency tree. This is especially risky with large projects that might have lots and lots of dependencies. The credentials that do get stolen in each attack can then be used to take over more accounts and compromise more packages.
Classical software engineering would have you believe that dependencies are good (we're building pyramids from bricks), but imo this has to be re-evaluated, and it's why I've been so growingly averse to them, preferring to use LLMs to "yoink" functionality when it's simple enough and possible.
Announcing TERAFAB: the next step towards becoming a galactic civilization xitter.cf/i/broadcasts/1yKAPMzlv…
Ryan Carson retweeted
Replying to @RhysSullivan
I'm not very happy with the code quality and I think agents bloat abstractions, have poor code aesthetics, are very prone to copy pasting code blocks and it's a mess, but at this point I stopped fighting it too hard and just moved on. The agents do not listen to my instructions in the AGENTS.md files. E.g. just as one example, no matter how many times I say something like:
"Every line of code should do exactly one thing and use intermediate variables as a form of documentation"
They will still "multitask" and create complex constructs where one line of code calls 2 functions and then indexes an array with the result. I think in principle I could use hooks or slash commands to clean this up but at some point just a shrug is easier.
Yes I think LLM as a judge for soft rewards is in principle and long term slightly problematic (due to goodharting concerns), but in practice and for now I don't think we've picked the low hanging fruit yet here.
Ryan Carson retweeted
At this point I need a skill that reviews all the other skills, figures out which ones are performing, and fires the rest. HR department for skills. I assume someone has built this already?
Ryan Carson retweeted
Every time we've made it easier to write software, we've ended up writing exponentially more of it.
When high-level languages replaced assembly, programmers didn't write less code - they wrote orders of magnitude more, tackling problems that would have been economically impossible before. When frameworks abstracted away the plumbing, we didn't reduce our output - we built more ambitious applications. When cloud platforms eliminated infrastructure management, we didn't scale back - we spun up services for use cases that never would have justified a server room.
@levie recently articulated why this pattern is about to repeat itself at a scale we haven't seen before, using Jevons Paradox as the frame. The argument resonates because it's playing out in real-time in our developer tools. The initial question everyone asks is "will this replace developers?" but just watch what actually happens. Teams that adopt these tools don't always shrink their engineering headcount - they expand their product surface area. The three-person startup that could only maintain one product now maintains four. The enterprise team that could only experiment with two approaches now tries seven.
The constraint being removed isn't competence but it's the activation energy required to start something new. Think about that internal tool you've been putting off because "it would take someone two weeks and we can't spare anyone"? Now it takes three hours. That refactoring you've been deferring because the risk/reward math didn't work? The math just changed.
This matters because software engineers are uniquely positioned to understand what's coming. We've seen this movie before, just in smaller domains. Every abstraction layer - from assembly to C to Python to frameworks to low-code - followed the same pattern. Each one was supposed to mean we'd need fewer developers. Each one instead enabled us to build more software.
Here's the part that deserves more attention imo: the barrier being lowered isn't just about writing code faster. It's about the types of problems that become economically viable to solve with software. Think about all the internal tools that don't exist at your company. Not because no one thought of them, but because the ROI calculation never cleared the bar. The custom dashboard that would make one team 10% more efficient but would take a week to build. The data pipeline that would unlock insights but requires specialized knowledge. The integration that would smooth a workflow but touches three different systems.
These aren't failing the cost-benefit analysis because the benefit is low - they're failing because the cost is high. Lower that cost by "10x", and suddenly you have an explosion of viable projects. This is exactly what's happening with AI-assisted development, and it's going to be more dramatic than previous transitions because we're making previously "impossible" work possible.
The second-order effects get really interesting when you consider that every new tool creates demand for more tools. When we made it easier to build web applications, we didn't just get more web applications - we got an entire ecosystem of monitoring tools, deployment platforms, debugging tools, and testing frameworks. Each of these spawned their own ecosystems. The compounding effect is nonlinear.
Now apply this logic to every domain where we're lowering the barrier to entry. Every new capability unlocked creates demand for supporting capabilities. Every workflow that becomes tractable creates demand for adjacent workflows. The surface area of what's economically viable expands in all directions.
For engineers specifically, this changes the calculus of what we choose to work on. Right now, we're trained to be incredibly selective about what we build because our time is the scarce resource. But when the cost of building drops dramatically, the limiting factor becomes imagination, "taste" and judgment, not implementation capacity. The skill shifts from "what can I build given my constraints?" to "what should we build given that constraints have in some ways been evaporated?"
The meta-point here is that we keep making the same prediction error. Every time we make something more efficient, we predict it will mean less of that thing. But efficiency improvements don't reduce demand - they reveal latent demand that was previously uneconomic to address. Coal. Computing. Cloud infrastructure. And now, knowledge work.
The pattern is so consistent that the burden of proof should shift. Instead of asking "will AI agents reduce the need for human knowledge workers?" we should be asking "what orders of magnitude increase in knowledge work output are we about to see?"
For software engineers it's the same transition we've navigated successfully several times already. The developers who thrived weren't the ones who resisted higher-level abstractions; they were the ones who used those abstractions to build more ambitious systems. The same logic applies now, just at a larger scale.
The real question is whether we're prepared for a world where the bottleneck shifts from "can we build this?" to "should we build this?" That's a fundamentally different problem space, and it requires fundamentally different skills.
We're about to find out what happens when the cost of knowledge work drops by an order of magnitude. History suggests we (perhaps) won't do less work - we'll discover we've been massively under-investing in knowledge work because it was too expensive to do all the things that were actually worth doing.
The paradox isn't that efficiency creates abundance. The paradox is that we keep being surprised by it.
Ryan Carson retweeted
My experiment to use @AmpCode full-time and to see how the costs add up is coming to a close. I'm just about out of credits from the batch I purchased for the experiment.
Amp is more expensive than a Claude plan. (I think we knew this already)
Amp charges you for the real tokens. Claude is heavily subidized.
When I'm going hard on a problem, I can push up to $40/day in token spend. Some of the edge is taken off with turning on ads. And I don't go hard every day. Some days have lighter usage as I'm planning my next steps. And I'm not running a ralph loop (yet).
That said, cost is not the only factor in choosing the harness.
The Amp experience feels better than Claude Code. It's tools for managing and interacting with threads are excellent. The team is super helpful and responsive in DMs and on X generally. I think they are near the front of the pack with their understanding of the problem. The Chronicle is a great read. @ryancarson's shows are packed with insights. Thorsten's sage advice as a super senior abounds. I can say good things about each team member I've interacted with.
I'm a massive fan.
As budgets allow, I will continue to use Amp as much as possible. Repos will be setup to allow either Amp or Claude Code to be configured out of the box.
And when the token subsidies end. I'm all in on Amp.
Ryan Carson retweeted
If you think AI companies are overspending on data centers, you don’t understand math or business.
Employees (at least digital ones) are about to be 1/10th the cost of the worlds most talented humans.
As businesses wake up to this, there will be near endless demand for this technology in the coming years.
As of Sonnet and Opus 4.5, we have crossed the chasm.
Forget a country of geniuses in a datacenter (for now - that’s coming soon though).
You can now purchase a genius level programmer’s time, 24/7, for hundreds of dollars a day.
That will soon drop to a few dollars per day.
The amount of economic value and innovation that is about to be unlocked in software alone, is unprecedented.
Anyone who has their hands on the tools knows this.
Anyone who denies it, either doesn’t understand how good it is, or is in denial.
“Never expect a man to understand something that his paycheck depends on him not understanding” —Upton Sinclair
Long $IREN: xitter.cf/awilkinson/status/1887…
I think I found a Picasso at a garage sale.
But not the kind you’d think…
Lately I’ve been thinking about how to capture some of the upside of this AI boom in my stock portfolio.
But it’s hard. I’m a value investor.
The idea of paying 30x earnings for NVIDIA almost physically pains me.
Here are a few of the trade ideas I’ve sketched out:
Self-driving vehicles: self-driving is already here, it's just not evenly distributed. I use it 90% of the time in my Tesla and it's phenomenal. Between their robotaxis are coming later this year and Waymo rolling out to more cities, it seems like an obvious assumption that in the next ten years most cars will be self-driving. Trades I've considered: Long Tesla for robotaxi and Optimus upside, short Uber/Lyft as their networks become obsolete. Risk: rollout could take longer than anticipated due to huge capital costs and regulation, Uber/Lyft could pivot.
Compute and inference: The obvious plays - buy NVIDIA, ASML, and TSMC. Great companies but expensive multiples and premiums. Years of excess demand. Risk: geopolitical conflict in Taiwan, moat erosion as China and other competitors innovate.
Frontier models: You can buy secondary in Anthropic, x.AI or OpenAI, but the valuations are huge, positions are difficult to come by, and you're also betting on a winner (this is notoriously hard to predict). Risk: the value doesn’t end up accruing to any of them/models become commoditized.
Google: Another play is simply to buy Google, which owns DeepMind/Gemini and has a massive data moat. Risk: the innovator's dilemma, bungled AI rollout, ads/search business gets decimated by ChatGPT.
SoftBank: The final idea I've considered is buying Softbank. It holds some OpenAI and other AI businesses, is investing in data centers via Stargate, owns 90% of ARM (whose chip designs are a small part of many critical AI components and GPUs), and is trading for roughly ⅓ of NAV. Risk: volatility/debt, as Masayoshi Son is known for wild bets.
While these are all interesting potential options, I recently found an investment that offers a particularly interesting risk/reward...
Last month, I got a call from my friend @kashramki, a value investor based in Toronto.
We had gotten to know one another over a decade ago, when he worked at BCI, a large pension fund in my hometown. Since then, he had gone on to lead dozens of multi-hundred-million and even billion-dollar investments into infrastructure deals (mostly pipelines, datacenters, and renewable energy).
He told me he was doing something radical:
"I'm selling my entire personal portfolio and putting it all into one stock."
When a smart investor says that, I lean in.
I was surprised. Kash is a highly conservative and thoughtful guy and I'd never seen him be so "all in" on something.
Usually I was the one telling him about my latest hare-brained investment while he crossed his arms and scratched his head, telling me about the litany of ways it could go wrong.
He went on to tell me about a company called IREN.
Public market investors have boxes in their minds. When they look at a company, they sort it into a box labeled "Good," for later research, or "Bad," which they discard immediately.
Like expert poker players dispassionately folding hands, they have trained themselves to instantly dismiss any stock that comes with certain toxic labels - red flags that trigger an immediate 'pass'.
Words like:
- IPO
- Cannabis
- Biotech
- and the dreaded Bitcoin
IREN is one of these stocks.
And when he explained it, I realized that Kash had found the public market equivalent of a Picasso at a garage sale.
Here’s the backstory he gave me:
In 2018, @danroberts0101 and his brother Will, both infrastructure bankers from Australia, started IREN ($IREN). Their thesis was simple: demand for energy and compute would grow significantly in the coming decades.
They acquired a series of large, high power datacenter sites located near renewable energy plants.
Their first few were in British Columbia, and they went on to acquire two massive sites in Texas, which are coming online in the next year or so.
To date, they've been using the BC-based datacenters to mine Bitcoin. But they are far from Bitcoin bulls.
Every Bitcoin they mine is immediately sold for a profit. They hold near zero Bitcoin long term and are simply doing arbitrage, making hundreds of millions of dollars from Bitcoin sales each year.
The stock is currently trading at a market cap of around $2.3 billion with no debt. That is an illustrative adjusted 2024 run-rate EBITDA multiple of ~4.6x based on disclosures from the company (from page 16 of management's November 2024 presentation: illustrative 2024 EBITDA at ~$435m at $90k/Bitcoin).
As of today, Bitcoin is at $99,480, so we can assume that number is a little higher.
Their growth plans for this year, if executed as planned, could result in an even lower forward multiple. In the range of ~2.5-4x.
Ok, so it's a potentially cheap stock (CC: @dirtcheapstocks). But how does this relate to AI?
Two key things fuel AI: energy and compute.
Both are in limited supply. We've all seen nuclear, semiconductor, and datacenter stocks go parabolic over the last year. That is because AI will require an almost unimaginable amount of energy and computation.
Training AI models requires huge clusters of chips, typically Nvidia GPUs, and generally requires them to be in one location.
These huge datacenters are already in thin supply and take time to build and permit. They are heavily regulated.
IREN's new Texas facilities are significantly larger than industry averages in both land size and power capacity. For context, their Childress facility alone (750MW) is nearly four times larger than what is considered a normal modern data center (200MW), and their Sweetwater Texas site is almost double that at 1.4GW.
I believe that IREN trades at a low multiple for two reasons:
1. They are investing massive amounts of capex in building out their two huge Texas datacenters (coming online in 2025/26) and those earnings haven't hit the P&L yet.
2. They are dismissed and miscategorized as a Bitcoin miner. Others are starting to wake up to this (see: Softbank/Cipher).
If we simply value IREN as a traditional datacenter business and compare it to similar companies, we'd expect this business to generate datacenter free cash flow of ~$2 billion a year from their Sweetwater, TX 1.4GW site that is primed for AI compute starting April 2026.
That is ~$2 billion per year from this one additional site.
Added to its existing earnings, that's more than what the entire company is worth today.
If we apply industry multiples to their long-term projected earnings (once the Texas sites come online), the math becomes almost laughably compelling.
Using typical free cash flow multiples in the datacenter space, that gets you an enterprise value of ~$20-40 billion. 10 to 20 times where the company is currently trading. And that’s without valuing their Bitcoin business, which could be worth several billion on its own.
And yes, I just heard you yell “BUT WHAT ABOUT DEEPSEEK???” through a mouthful of potato chips.
Many point to DeepSeek's claimed $6M model training costs as evidence that compute demands might be cooling.
While they've achieved big efficiency gains, this isn't the paradigm shift some claim it to be. As Anthropic's CEO Dario Amodei explained in a recent blog post, we've historically seen a ~4x per year decrease in AI training costs.
DeepSeek's improvements are roughly on this expected curve, not dramatically below it.
But more importantly, these efficiency gains don't reduce total compute demand—they get immediately reinvested into training even more sophisticated models. The path to AGI and ASI will still require massive amounts of compute, both for training increasingly complex models and handling the explosion in inference demands as AI applications proliferate.
There’s a reason that Microsoft, Meta, and Google recently (post DeepSeek) announced they would collectively spend $220 BILLION on capital expenditures this year. The large majority of which will be spent on AI infrastructure.
Back to IREN…
So, we have a well-run company with no debt, ample cash flow generation, in an industry with huge excess demand, and a dirt cheap valuation.
This is my kind of garage sale Picasso!
As my friend @mohnishpabrai describes his favorite type of investment:
“Heads I win, tails I don’t lose much."
This feels like one of those.
Even if I’m completely wrong, and AI compute isn’t the hot commodity I’m anticipating, the underlying datacenters should still be valuable. Even before AI came onto the scene, the US datacenter market was expected to grow at 9-10% CAGR through 2030.
But, if AI does play out in a big way, those same datacenters could become Crown Jewels that could spit off huge amounts of cash from training and inference compute. Toll roads en route to the AI revolution.
So, all this is to say, I bought the stock.
A relatively small position for me. A few % of my portfolio mixed with a few other bets on frontier models, Google, and semiconductors.
Of course, like all investments, this has risk.
Here are a few of the best arguments for why IREN doesn’t pan out as I’m anticipating:
• They could dilute shareholders excessively.
• They could fail to capture the AI datacenter opportunities by favouring their past strategy of bitcoin mining, all in the hopes that bitcoin goes to the moon. With an 18-month payback period on their bitcoin hardware, the case for growing their bitcoin operations is very compelling, but it is only as sound a strategy as the price of bitcoin itself. And who knows what it will do tomorrow?
• Will they find a highly creditworthy counterparty for their mega-site in Texas? They could fail to demonstrate to the hyperscaler community that they have sufficient construction or operating experience to build and manage a large AI datacenter, especially in the middle-of-nowhere-Texas, where highly skilled datacenter operators are scarce.
• Bitcoin prices could drop to the point where mining is no longer profitable (around $30,000)—although I would argue the datacenters themselves are still valuable in this scenario. If you wanted to get clever, you could buy puts on a Bitcoin ETF to cover this scenario.
But overall, I think it offers a good risk/reward. I believe the underlying datacenter assets are valuable even if AI plays out very differently than expected.
I welcome any thoughts and critiques :-)
Is this a Picasso or a cheap reproduction? Roast me!
Important Disclaimers:
IREN stock is volatile. It swings around a lot, so if you buy, buckle up and be ready to hold for a few years. The value here depends on management delivering on their planned expansion.
I'm just a guy on the internet sharing thoughts. Not a financial advisor, and this definitely isn't investment advice. Yes, I have met with IREN's IR team, but everything I've shared comes from public sources - SEC filings, presentations, and public statements. Reality might look very different from the scenarios discussed.
Datacenters and crypto are wild markets with plenty that could go wrong - competitive pressures, regulations, market swings, geopolitics, you name it.
I own IREN shares and some of the other stuff mentioned here. Might trade them anytime. Could make me biased.
Do your own research, talk to actual financial advisors, and don't bet the farm. Past performance doesn't predict the future.
Ryan Carson retweeted
Even if current LLM progress hits a brick wall at Opus 4.5 level (and I doubt that will happen) the next 12 months are still going to be a staggering time of change in this industry as decision makers start truly understanding the new reality we live in.
obie.medium.com/what-happens…



























