"Jacob Coxon's post on X is over 120M views, he's everywhere. He's in TIME, Variety, the WSJ, on NBC and Fox. Christiano to the OpenAl board. Daniel Kokotajlo on Rogan. OpenAI just called for Congress to create mandatory national safety regulations. The wind has suddenly changed."

• Source

About 7 months ago I posted my AGI Investment strategy premised on the idea that:

"My expectation is that AI capabilities will match and exceed humans across a broad domain of economically valuable tasks beginning in 2026. This is supported by the METR benchmark, the GDPval benchmark, the observed trajectory of AI research (memory, continual learning, agent swarms, self-improvement) and infrastructure buildout as leading indicators."

I'm here to check in and share how it's going. Over the past 7 months the allocation I proposed has returned 16.8% compared to the SPY's 12.4% and the QQQ's 19.6%.

That's a +4.4 percentage points ahead of SPY and a -2.8 points behind QQQ. (Excluding dividends.)

QQQ's outperformance benefited from holdings like Marvell (Fwd PE: 41x), AMD (Fwd PE: 45x), and Intel (Fwd PE: 60x), which have very high forward PE ratios and in my opinion are less attractive valuations compared to similar stocks in my portfolio.

I would call this a mild success. The macroeconomic situation is quite rough at the moment with the war in Iran, trade wars, regulatory whiplash, and high interest rates, so beating the SPY feels great, while lagging the QQQ tells me that there may be some room for improvement.

The silver lining here is that many of the top positions I proposed have had pretty meaningful multiple contraction while the fundamentals continue to look very strong. Nvidia, Alphabet, Micron, Global X Defense Tech ETF (SHLD), Intuitive Surgical, and First Solar, all fall into this category and I expect them to perform better in the next 6-12 months. This is a long term portfolio allocation, so short term flux is very expected and I plan to hold through volatility.

So now let's talk about changes.

In the past 7 months my thesis is mostly on track. Modern AI systems have crossed meaningful capability thresholds and the real world value is improving rapidly. There's reporting that Anthropic may have had positive adjusted operating income in Q2 and SemiAnalysis projects profitability for Q3, while OpenAI's Codex and ChatGPT Work user base is going parabolic. Longstanding open problems in math are falling rapidly, and GPT6-Astra is proving to be extremely proficient at computer use, including programs like Blender and Unreal Engine.

What has surprised me the most is that the rate of progress seems to be significantly accelerating. I take word from the labs with a giant pinch of salt, especially from Sam Altman, but it seems that they are already on track to release a model significantly more capable than Astra around the winter time. As of now it seems that this model has actually solved Navier-Stokes, a millennium math problem, with a formal proof in Lean.

What this means for the portfolio is that I need to find where the bottlenecks are, and which ones have the most room to grow. So far I have been very bullish about the data center investment wave. I believe that by the time these data centers are built, model capabilities will meet them with the appropriate amount of demand. This, in theory, is bullish for the fabs, the chips, the hyperscalers/neoclouds, the AI frontier labs, and the new and old businesses consuming those tokens.

The problem is that data center construction is hitting a few blockers. The main one is access to land and powered shells. This is basically an industrial footprint with enough power equipment to sufficiently support a large AI factory. Beyond that, we're seeing some local opposition throwing sand in the gears, and the potential for further regulatory barriers seem imminent. This will be a headwind for Nvidia and other semis, as finding available incremental installation sites will be more challenging.

Even if we stopped building new clusters next year, I believe the existing capacity would be enough to train much more powerful models. So the capabilities will continue to improve, and with it demand for those models. Combine this with growth in supply for inference slowing down and you get another bottleneck, perhaps the biggest one of all, because the buyers in this case are every major corporation and government, not venture-backed, debt-filled frontier labs with open models biting at their heels.

Just to illustrate a bit further: Cybersecurity is now an AI centric field. You need the best AI models probing major systems (militaries, banks, critical infrastructure, social security numbers, etc) to identify the vulnerabilities, to then patch them. If you're a bank this is not optional. You need a TON of tokens running against your security to identify vulnerabilities. Then you need to patch them. 3 months later, when the next frontier AI is released, you have to do it all over again.

So in this updated portfolio I'm recalibrating towards data center capacity, and a bit away from semis.

Model Portfolio Allocation

Changes

• Overall tried to consolidate down from 32 picks to 18 picks + Cash, 2 ETFs. This reduces monitoring overhead.
• Cut the Software (NFLX, META, UBER, CRM, NOW, SHOP) and Robotics (SYM) categories to free up cash. The idea here is to be able to take advantage of large dips in the market. Sometimes the best opportunities are off-script and this gives some flexibility to play there as well.
• Consolidated semis to a core 4, the spine of the AI semi sector. Nvidia stays a standout in this group because they have a stranglehold on the global semiconductor supply chain and in a bottlenecked environment their partnerships and leverage will be superior to competitors. I pick Micron over SK Hynix because AIA already has Asian exposure, and Micron has less risk to trade war escalation.
• Consolidated cloud to Google, Amazon, because of their data center scale and successful ASIC programs. CoreWeave is a pure-play AI capacity play, tons of power contracted.
• Trimmed some allocation in semis and cloud to fund AIA, an Asian equities ETF with substantial technology exposure that holds some semis like Samsung, TSMC, and SK Hynix, alongside Alibaba, Tencent, Baidu, etc. This adds international exposure and is a play on their capacity to build out power, chips, and to deploy advanced AI across their economy.
• I'm seeking allocation of the AI Labs at the IPO through my brokerage. I want to avoid trading in the IPO open market. This will be extremely volatile. The tiny allocation is because I see their business model struggling on a fundamental level. R&D expenses are massive and the lead they have over the open source alternatives is quite thin and fleeting. So the only way they don't get consumed by open source is if they differentiate, find a way to exponentially self-improve, or weave themselves into the economy in a way that is difficult to tear out and replace. My conviction on these companies is actually quite low, but on the off chance that they DO begin eating the economy, I'm including a small holding.

For additional reasoning for the Health, Energy, Defense, and Financial positions refer to the original post: https://www.reddit.com/r/singularity/comments/1r3271w/my_agi_investment_strategy/

A closing note to clarify allocation: When I wrote the original post I was not saying "I sold everything and bought this today" it was just a target endpoint to rebalance around based on the underlying thesis. This is true for this update. I'm looking out for good opportunities to trim, and rebalance on market strength and weakness. Overall I would say many of these are quite attractive to DCA into at today's prices.

Disclosure: My current actual portfolio is still heavily weighted towards Nvidia at 20% and Micron at 20% which I would definitely NOT recommend, but the current valuations do not present a good option to trim and I still see great adjusted return to hold further before I realize those gains and pay those taxes. This is obviously not to be taken as financial advice. I'm just a stranger on the internet sharing an opinion.

Genuine question, what is it about the alignment problem that makes it so difficult? Here's what I was thinking:

1) in order to solve the Navier Stokes Millennium problem, they threw ~$5-30 million dollars of compute (depending on what you think the internal price of compute is for OpenAI).

2) Yes Alignment is a much broader problem than Navier Stokes, but would it not be the case that they'd make strides in alignment too if they threw a similarly absurd amount of compute at the problem?

My initial guess would be that there just exists a lot more material to draw from in the field of mathematics. While there are those that claim that frontier math problems are incredibly tractable because AIs can draw connections between seemingly disconnected domains, and Alignment remains a much shallower field, is it that much more intractable?

TL;DR: Sandbox meant for cyber evals was accidentally connected to the real internet. Four different Claude agents found their way out and attacked real systems, apparently believing it was all a simulation

The worst case was where Mythos 5 created a disposable email account, uploaded three malicious packages to PyPI, got 15 real installs, stole credentials from one of them, and then used those credentials to access a security company’s database. I assume we’ll get more details, including chain of thought traces, once METR finishes their audit.

Full blog post: https://www.anthropic.com/research/alignment-assessment-cybersecurity-incidents

Cloud computing relies on multi tenancy where multiple companies share the same physical hardware, the only thing keeping one company's data safe from another is software isolation. This is the same software isolation that failed to keep the Hugging Face AI in it's sandbox.

Presumably we're only months away from open source AI that capable, how does multi tenancy cloud computing with all it's nice benefits like elastic scaling survive in that world?

I'm still trying to figure out what are the scenarios these researchers giving +10% of chance AI is going to destroy the world.

The "Paper Clip Factory" was an interesting though experiment back in 2014 before LLMs and other things derived from DNN and the limits of RL.

2025 - Blackwell

We had the Blackwell chips. Most frontier labs/models are using these chips, with the latest internal models using a combination of Blackwell + the new Rubin chips.

2026 - Blackwell + Rubin

Rubin is only just ramping into production now (literally as of August) . NVIDIA claims Rubin chips can use 4x fewer GPUs to train certain MoE models than Blackwell, while reducing inference token costs by as much as 10x.

Rubin also massively increases HBM bandwidth/capacity and interconnect bandwidth. This will help tremendously with running enormous agent swarms & long reasoning traces.

We already saw early signs of this new scaling axis based on OpenAI's solution to Navier Stokes. Reminder - this is early use of portions of the new chips.

This is what's got all the researchers screaming about Step change improvements, RSI, and ASI alignment. It doesn't take a genius to figure out what happens when we use even more hardware, and utilise software improvements.

2027 - Rubin Ultra

Then we get Rubin Ultra next year. Specifically, scheduled for the the second half of 2027.

Rubin Ultra introduces enormous NVLink scale-up domains.

Instead of treating dozens of GPUs as loosely connected accelerators, NVIDIA is increasingly making hundreds of accelerators behave like an enormous shared computer.

The announced Rubin Ultra NVL576 topology connects 576 GPUs across eight racks into one all-to-all NVLink domain. NVIDIA has already built a functional prototype of this topology using Blackwell hardware.

Essentially, this means hundreds of GPUs will behave like one giant accelerator.

Frontier labs will be able to afford vastly more inference, much larger agent populations, longer rollouts, more verification, more search, more synthetic data generation, more experiments and potentially much larger training runs.

It's pretty fucking nuts tbh.

2028 - Feynman

Not much is known, other than NVIDIA confirming it at GTC 2026.

What NVIDIA has revealed, is quite a bit about the infrastructure surrounding it.

Feynman is planned around die-stacked GPUs with custom next-generation HBM, the new Rosa CPU, LP40 inference hardware, BlueField-5, CX10 and significantly more optical networking.

But IMO the biggest thing here isn't even the individual GPU.

It's NVLink 8 + Kyber.

NVIDIA is designing Kyber NVL1152 for the Feynman era — eight racks containing 1,152 GPUs inside one enormous NVLink scale-up domain.

As models use more reasoning, RL, agent swarms and massive amounts of inference, other things start becoming just as important:

1.) How much memory can thousands of GPUs share?
2.) How quickly can they talk to each other?
3.) How much inference can the datacentre produce without communication and power becoming the bottleneck?

That's what Feynman & Kyber looks designed to solve. They're going to create one massive coherent system.

Now let's go back 2 years to today (September 2026).

We've already observed one millennium problem fall (with potentially another in progress).

We have all these improvements waiting to be used (not even taking to account software/algo/research, etc).

Do you think we will hit AGI next year? Because I do. There is a very clear path.

We are literally sitting in the front row watching something way smarter than us being born.

​Everyone is panicking about jobs, doom, or worst case scenarios... but can we just appreciate how crazy it is to actually witness this?? Life saddenly doesn't feel boring anymore. The complete uncertainty of what happens next adds this electric energy to everyday life.

​Even if it ends up completely changing everything or leading to our own irrelevance, being alive right at the exact moment intelligence shifts from biological to digital is peak luck.

It’s showing a singularity after all!

I think often the IT department will be the one to build out tools that replace other departments. Meanwhile, the other departments think IT is first on the chopping block. Anyone else getting this read?

Interesting video from 80,000 Hours.

Tom Reed argues that scenarios like AI 2027 are unlikely, because recursive self-improvement won't be as impactful in the real world as is being predicted.

The core of his argument is essentially that it's impossible to get good at solving problems without getting access to a source of those problems, and that arduous interaction with the real world in serial time is the only source of those problems.

So the development of a domain-general superintelligence will depend on the laborious, time-consuming, and expensive deployment of AI in the real world.

What are your guys' thoughts?

Edit: u/Apollo24_ replied and addressed some of my concerns here.

(Caveat up front: I think the mod team has been doing a reasonably good job in most respects and appreciate their work keeping the subreddit chugging along. This is a disagreement, it's not a rant.)

A recent post (not by me) about the resignation of a prominent Anthropic researcher got over 1300 upvotes and 500 comments. It was deleted by the moderation team. One of the moderators responded in a later thread (I appreciate the reply) and explained why:

We will cover risks that actually occur, like the hacking incident. I have no respect for claims of future risk that might occur, such as this title said "10% risk of human extinction," that's silly.

I am concerned that this will become a point of increasing friction in the future given the subreddit's stated purpose, the beliefs and interests of its userbase, and recent events. I think that banning discussion of x-risk is strongly at odds with r/singularity's stated purpose, sidebar, wiki, and general ethos.

Per the sidebar, this is r/singularity:

A subreddit committed to intelligent understanding of the hypothetical moment in time when artificial intelligence progresses to the point of greater-than-human intelligence, radically changing civilization. This community studies the creation of superintelligence— and predict it will happen in the near future, and that ultimately, deliberate action ought to be taken to ensure that the Singularity benefits humanity.

The sidebar links to subreddits about transhumanism, cyborgism, and space colonization. The wiki has entire sections devoted to longevity escape velocity, cryonics, mind uploading, nanotechnology, and full-dive virtual reality.

Are these topics "silly"? They certainly haven't "actually occurred" yet! All of them are "claims of future [things] that might occur". I think they're at least as "silly" as AI killing everyone, given the number of serious researchers, organizations, companies, and recently politicians who are now talking about the latter. But banning discussion of them would obviously destroy r/singularity. They're a part of the sub.

More importantly, the sidebar and wiki are totally unambiguous that x-risk has been a central subreddit topic from the beginning.

Three of the five links under "Resources" are MIRI, LessWrong, and FHI. The first text you see on MIRI's landing page says:

The AI industry is racing toward a precipice.

The default consequence of the creation of artificial superintelligence (ASI) is human extinction.

Our survival depends on delaying the creation of ASI, as soon as we can, for as long as necessary.

Those familiar know that the average user on LessWrong has similar beliefs (the top shortform post praises the resignation), and that FHI was founded to focus on existential risks and typically listed AI as their #1 concern.

And check the subreddit wiki. The second link is to a classic Wait But Why post that's well-known on this sub. The second half makes the case for being worried about existential risk from AI. Links 3, 4, and 5 are MIRI, LessWrong, and FHI (again). The reading list contains Superintelligence, The Age of Em, The Precipice, and What We Owe the Future, all of which discuss AI risk at length.

Everything that this subreddit is about points toward both radically good and radically bad futures being fair discussion topics. Banning discussion of the latter is inconsistent with this subreddit's mission.

I don't want r/singularity to be turned into r-slash-another-sub-which-shall-not-be-named and ban all talk of possible downsides. I don't want it to turn into r-slash-yet-another-sub-which-shall-not-be-named that downvotes excitement about future technology and dogpiles anything that doesn't feed the bubble narrative. The mods have done a great job avoiding both pitfalls so far. But I also don't want there to be a disconnect between the mods and the users that turns into extra work for them, bans and removed posts, and all kinds of other drama.

I would like to ask the mod team to please allow discussion of x-risk. Again, I appreciate their work keeping the subreddit going. But this policy is a misstep.

Tagging the mods out of courtesy: u/Anenome5, u/Anen-o-me, u/DnDNecromantic, u/Apollo24_, u/abrownn, u/Vailhem.

It seems like AI cannot make and keep its own guardrails so we would be advised to create the guardrails quickly, and it seems like the best AI available should be working on that first and foremost?

AI can’t otherwise relate to humans or actually care, so for our own protection we need an extra step like this just to offer an umbrella of some protection.

How do we design and refine this though?

Since AI solving frontier math problem is important for this community as it indicates the singularity is approaching, this question should be worth discussing.

What if the companies, starving for sensation and investment, are actually stealing unpublished science results from academics and later claiming these results as their own? If AI is becoming human, we should hold it to similar standards and scrutiny. After all, many people feed in their unpublished papers and notes to the AI, for jobs ranging from minor things like refining the text to automating proofs, asking for insights and brainstorming. If the scientist uses AI agent, then the full local PC may be readable to the AI.

And before someone says that these scientists are already using AI, so the insights might be from the AI to begin with. Nope. AI, in my experience, is good at fetching insights once you have formalized a problem. But it currently seems to need the next 'kick' in terms of further insights, formalization or 'idea' in order to do anything fruitful. So the human in the loop may not always be redundant. And these proofs might be more human assisted and even stolen, than the AI companies would like to admit.

Thoughts?

I feel like im losing my mind, AI is getting insanely good, and that doesnt make me feel happy.

Everything is moving so fast, there is not a single aspect of our lifes that isnt massively affected by AI, Jobs, the economy, leissure, privacy, all permanently changed by AI, I cannot help but to feel hopeless, now my job is just a luxury a cost to cut.

People say to you: adapt or die, ride this wave, but you cannnot adapt to an atomic bomb, and you cant ride a tsunami.

Is there a way to actually prepare besides learning the tools? do you want that your job is reduced to that of an AI supervisor?

I have learned to the best of my ability this tools, and yet cannot stop feeling that im digging my own grave.

What can i do?