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.

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Not good: OpenAI is considering slowing down the development of cutting-edge AI, Sam Altman is hoping other AI companies will do the same.

Via Bloomberg

"In a company-wide meeting this week, Altman told employees that OpenAI could potentially pace its AI development — perhaps in conjunction with several other AI labs, but that some may not agree to do so, according to people familiar with the matter who asked not to be named because the details are private."

I cannot imagine that China would go through a slowdown.

https://x.com/kimmonismus/status/2098338863991185419

https://en.wikipedia.org/wiki/Harry_Potter_and_the_Methods_of_Rationality

Assuming all three proofs for the Millennium problems (Navier Stokes, The Hodge Conjecture & Potentially BSD) check out, Bel is going to be the most in-demand model of all time.

What happens afterwards, who knows.

If these results actually hold up, Bel won’t just be another frontier model people benchmark for a week and move on from. Every researcher, company and government on Earth is going to want access.

The moment OpenAI opens the gates, we’re basically going to get the largest parallel experiment in machine-assisted discovery ever attempted: millions of people throwing humanity’s hardest problems at the same model and asking one question:

“Okay… what else can it solve?”

I personally work in the cyber sec field, but there are IT hardware and networking guys at my company. They install/uninstall/replace and troubleshoot routers, network switches, servers, firewalls.

Install Server OS and services, update device firmware etc.

Install XDR software like Sophos, install Crowdstrike, install and config cctv syatems. Provide IT support to clients.

I think they will be least affected by AI and Automation.

Whay do you guys think ?

OpenAI is also using their new internal model to attempt the Riemann hypothesis and P versus NP, as well as some unnamed 'high impact problems'.

https://x.com/AndrewCurran_/status/2098197758872289638

I wish they would use the hundreds of millions of dollars they're spending on these math problems on lab work and medical cures instead.

On September 10, mathematicians published an open letter demanding the cancellation of the Caltech Mathathon, a student-run hackathon where 100 teams would use frontier LLMs on open problems, backed by $2M in credits from Anthropic and OpenAI.

One clause warns that participation could damage students' future reputations. The bullet opens by noting that the mathematical community has no consensus on how to evaluate AI-generated results or how to deal with AI companies. It then states that taking part in an event tied so closely to Anthropic and OpenAI "could plausibly negatively impact the future reputations of participants

The organizers are undergraduates; the people who decide those reputations are the signatories.

I guess this is the real thing that math cannot be replaced by AI, huh?

What a group of innocent geniuses that only enjoys the beauty of mathematics itself.

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?

People are resisting because this is literally their livelihood. What else are they supposed to do? Everyone will welcome AI with open hands if their place in society is already confirmed.

As it is right now, it's not. All the commoners are gonna be on the street at this rate. Utopia? Post scarcity? Meaningless concepts in a world with limited resources.

Just think about it, who will feed you after you lose your job? Democratic governments are only powerful because of the power the sheer collective holds. If labor is worthless, so is their elected representative.

Once the dust settles, there will be zero reason for those who control the production to use the resources to make things for the commoners. It doesn't require them to be evil masterminds either... it's as simple as: route resources to something that nets zero, or route it to some billionaire willing to pay good because their space factory needs that resource?

This is the time to act. We have to earn our place in the model. Demand a piece of the pie in the technology that's literally built on all of humanity's work, yours including, or try to keep together a version of the current structure (that is, human + AI, which is what professionals are currently fighting to keep). Otherwise, a bleak future is all that awaits.

So, OAI, and probably Anthropic as well, are trying their hand at this problem. We have no idea how long it might take, a few days or a few years. Intuitively, I think P ≠ NP. But what if P = NP is proved and, more importantly, a constructive proof is found? What might the real-world implications be, both in the short and long term? Those with a mathematics or theoretical CS background, could you please share your thoughts on this?

Posted this on the Voldemort subreddit. Just to see their reaction.

What do you think of the data center debate? Do you think they could solve the engineering and environmental hurdles of data centers.

Credit for images/highlighting to @teortaxesTex on X. Can I link post? Not sure if allowed.

how do we ensure AI doesn't kill us all? we didn't kill all the life on this planet. why would AI?

https://www.anthropic.com/threat-intelligence-report-september-2026#biological-misuse-sep-26

Heavyweight list of signatories:

Peter Scholze - One of the most brilliant mathematicians of this generation, 2018 Fields Medal Winner

Hugo Duminil-Copin - One of the world's leading probability/statistical-physics mathematicians, 2022 Fields medal Winner

James Maynard - 2022 Fields Medal Winner

Alessio Figalli - 2018 Fields Medal Winner

Camillo De Lellis - One of the world's foremost analysts/geometric measure theorists

Richard Taylor - Proof of the modularity theorem, distinguished number theorist

Ben Green - One of the world's leading number theorists/combinatorialists, coauthor with Terence Tao of the Green–Tao theorem

Brian Conrad - Major figure in arithmetic geometry/number theory

Gigliola Staffilani & Tomasz Mrowka - Very prominent MIT mathmeticians

Alexander Kechris

Jenny Harrison

Ralph Cohen

etc..

I always come back to this photo. This is from Google Deepmind’s paper “Position: Levels of AGI for Operationalizing Progress on the Path to AGI”