Weekend reading: First they came for the call centres

Reasoning models are dead. Long live reasoning models!:

https://open.substack.com/pub/artificialintelligencemadesimple/p/reasoning-models-are-a-dead-end-breakdowns

Shorter TL:DR to the TL:DR: it’s the wrong architecture, similar to Marcus’s disparaging of the pre-trained, backward propagation, weight adjustment, deep layered, token shape prediction paradigm and advocacy of hybridised neural nets and formal symbolic language overlay / syncretic (deterministic programme like and probabilistic input/output net nodes) approaches.

The longer TL:DR is from the actual TL:DR: “Reasoning models are a dead end because they try to compress a dynamic control process into static weights. Reasoning is not a pattern you can train; it is an algorithm you must run. When you train on reasoning traces, you only capture the final surviving path”.

My views, we need a better model reward function and ability to learn continuously to update a realistic world model.

Honoured by the link to this thread in the Monevator Weekend Reading Robot Overlord roundup 🙂 Thank you @TI.

The financing doom loop (and ‘dark fibre’ parallels: “the fiber optic buildout of 1999 where $500 billion of infrastructure investment produced 2.7 percent utilization and 12.8 percent default rates”):

https://open.substack.com/pub/shanakaanslemperera/p/the-stargate-deception

‘And yet it moves’: only today I got a LRM over half a dozen iterations to produce a 100% correct 6,000 word, original advice on a novel issue in less than a day including iteration, checking, polishing up. Realistically that would take 3 days from scratch without any assistance from ‘AI’ (or whatever it should be called). The hyperscalers probably won’t raise the $3 tn to $8 tn needed for the data centre buildout by 2028 to 2030 nor will OpenAI (or anyone get to 250 Gigawatts data centre electricity use) by 2032 (equal to India’s entire electricity consumption). But that doesn’t mean it wouldn’t be desirable to try go get there given what the technology has already demonstrated it can do. It might be merely mundane utility and in no way AGI itself or even just comparable to meaningful notions of AGI but I can easily see it replacing 50% or more of White Collar / Knowledge economy jobs eventually (maybe sooner rather than later) saving tens of trillions of dollars annually in payroll costs and feeding through directly to the bottom line in the P&L.

Crikey, this is a bit wild as a theory of how to get to AGI:

https://open.substack.com/pub/josecrespo/p/the-math-openai-doesnt-want-you-to

It might, or might not, be b*ts**t, but I do definitely agree with this bit:

“You cannot build AGI if you cannot see what your model is doing. You cannot deploy industrial AI if you cannot audit its reasoning. You cannot trust a system that cannot demonstrate coherence.”

Jevons’ Paradox on turbo boost.

Why ‘AI’ (and by extrapolation and extension AGI) will require more workers (and create more jobs than it destroys) even if (eventually) 5 white collar workers end up being able to do the work of 50 in the pre-LLM era (<2023):

https://open.substack.com/pub/ruben/p/replaced

Intriguing, and at least faintly plausible; but, to begin with, the job losses must surely first occur as the quickest way for shareholder value orientated businesses to boost the bottom line.

In the Industrial Revolution eventually more jobs were created and wealth ultimately cascade.

However, initially, huge numbers of people in crafts were put out of work (the Luddite movement was right in this respect), and people working in factories were paid far worse, and had even more appalling working and living conditions, than even the essentially peasant conditions which they or their parents had lived under before the onset of urbanisation.

The immediate historical precedent is not good.

All these IPOs do seem a bit 1999ish.

https://open.substack.com/pub/aidisruption/p/mega-ipos-flood-2024-investors-cash

Too much supply coming on for the demand for new AI and frontier tech (SpaceX etc) shares? Time as always will tell.

A look ahead for the semis and revised hyperscaler Capex in the years ahead:

https://open.substack.com/pub/techfund/p/ai-and-semis-outlook-2026

This mentions token usage / demand growth coming in much stronger in 2025 than expected beforehand; but the sheer scale of growth in use and decline in prices is not covered but deserves another mention here: one model provider (can’t remember now if it’s OpenAI or Google for ChatGPT or Gemini respectively) has seen monthly token usage go from 9.7 trillion to over 1.3 quadrillion (1,300 trillion) (every thirty days!!), with the price per million tokens down 99.7%, all in 18 months (IIRC mid April 2024 to mid October 2025, I’d need to check though, this is from my memory). One token = 0.7 to 0.8 words.

A engaging take on how AI will, over the next 50 years or so lever the top percentile of workers/ entrepreneurs and businesses:

https://open.substack.com/pub/generativevalue/p/2025-annual-letter

The more I read + learn the more convinced I am that the optimum portfolio is a modestly leveraged barbell with a lower risk underpinning of:
– some low volatility, high moat, low disruption risk surface and intersection of quality + value stocks with a degree of ‘inflation protection’ (especially consumer staples and infrastructure/ utilities);
– plenty of ‘risk off’ assets (gold and precious metals, long duration TIPS, global macro HF strategies);
– some cyclical broad commodity and deep value energy producer and junior miners (for dividends and optionality respectively) and opportunistic exposure to deeply discounted HY REITs and other CEFs/ITs;
– With a bleeding edge overlay, at the other end of the risk / opportunity barbell, of high growth, tech and tech disruption stocks from the mega caps right down the cap weight scale to (at the app/platform end) small/ micro/ nano cap (including fintech like operators);
– Plus some sort of juicing with a low single digit allocation of starting capital to a DCA leverage equity rotation strategy (which, if the worse happens, and it goes to zero, then still only burns through a few percent of the starting value of the portfolio), contributing less to the DCA when valuations are high and more in the recovery (e.g. back above 200 DSMA) after a crash (e.g. more than 20% drawdown on 52 week high).

Money left of the table, where’s the value left at now in the AI stack?:

https://open.substack.com/pub/randomwalkwithdata/p/assets-or-software-and-at-what-price

Kernel optimisation:

https://open.substack.com/pub/importai/p/import-ai-439-ai-kernels-decentralized

Intriguing: “The most important takeaway is that decentralized training is growing quickly relative to frontier AI training, with decentralized training runs growing their compute by 20X a year versus 5X a year for frontier training runs. But the other important takeaway is that the sizes of these things are completely different – today’s decentralized training runs are still about 1000X smaller than frontier ones.”

So true.

https://substack.com/@mjreard/note/c-185945397?r=2kxl2k

Dwarkesh is my Go To AI Guru.

Thanks for the link there @TI. Fantastic piece of research by JPM.

So we’re looking at the New Deal Public Works Administration, the Manhattan Project, the electrification of industry, the Interstate Highway System, the Apollo programme and the Broadband rollout all rolled into one (p.4)!

What could possibly go wrong?

I was struck by two points on p.9.

First the 1.5x-1.75x cost improvement and 1.25x-1.5x speed improvement of current frontier model assistance to existing human task experts.

Obviously, this seems cautious (and necessarily subjective, even though precisely quantified here) in itself, especially given that earlier models, like GPT-4o, are shown here as actually slowing down experts to half unassisted speed and doubling the task cost. That seems a very harsh assessment of earlier models’ IRL performance.

But what really strikes me here is the disconnect between the chart and reality.

The chart suggests to me organisations could shead 30% of their staff costs now for the same output, or, if the market they’re selling into has capacity, that they could increase output now by 50% for the same costs base.

Yet when I look left and right I see no sign of mass redundancy and no productivity boom.

It’s like what Alan Greenspan said about the Internet in the mid 1990s (1996 perhaps?), namely that the impact was showing up everywhere except in the productivity and growth data (and, of course, a lot of the corporate profits of that era turned out to be baloney, as we now know).

So what the heck is happening???

We’ve got a demonstrably very capable technology.

Aside from motors and electricity this is perhaps the most capable (and in many ways it is *the* most impressive) tech that I’ve ever come across.

And yet I see near zero sign of it (yet) changing businesses truly fundamentally, or in many cases even very much.

Are people just using this to work less to output the same with the same numbers and mix of staff and, therefore, with the same cost base?

Will business act like Tito’s Worker Cooperatives and basically let staff run the show for their benefit, or will shareholders at some point demand their pound of cost cutting flesh out of payroll?

Maybe it’ll all just take a long time.

Organisational and institutional culture bottlenecks are in their way as significant as energy, data and financing availability to the success of this endeavour.

The second point is on the question of data bottlenecks.

The footnotes on p. 9 reference a total of 4,750 tn tokens of data on the Internet, on video and in image libraries. That sounds loads but in the context of LLMs is it really?

Frontier models are (already) using up to 10exp26 (100 septillion) effective Floating Operations in training and seeing demand of over 1,300 tn tokens per month.

Accordingly, it seems quite blase for JPM to note that those 4,750 tn tokens available for training data (3,100 tn + 300 tn + 1,350 tn) will be enough for training until 2030.

Zvi’s on the case for that Philip Trammel and Dwarkesh Patel piece on ‘Capital in the 22nd century’ which I previously linked to and which was also separately linked to by @platformer in the most recent W/e reading MV links:

https://open.substack.com/pub/thezvi/p/dos-capita

Or perhaps hydrogen fluoride is the ‘real’ AI bottleneck (for making silicon tetrafluoride for chip wafers):

https://open.substack.com/pub/shanakaanslemperera/p/the-invisible-chokepoint

Scaling walls, energy connection backlogs, power generation shortfalls, data availability insufficiency, financing issues, revenue shortfalls and operating losses. It’s not an obviously happy picture.

The periodic table od AI (a very useful conceptualisation of how it fits together):

https://youtu.be/ESBMgZHzfG0?si=zXyG7D_nSFZb42Y7

Nvidia Rubin CPX “a chip that analysts estimate costs roughly 25% as much to manufacture as a standard Rubin R200 while delivering approximately 60% of the compute performance”:

https://open.substack.com/pub/shanakaanslemperera/p/the-architecture-of-dominance-nvidias

A nice link to the 14 top AI resources (in the authors opinion) from 2025:

https://open.substack.com/pub/theaiopportunity/p/the-2025-ai-breakthroughs

Trouble at mill with Meta and LLMs:

https://open.substack.com/pub/garymarcus/p/breaking-marcus-weighs-in-mostly

Reinforcement Learning as part of Intelligence As A Service (‘IaaS’):

https://open.substack.com/pub/semianalysis/p/rl-environments-and-rl-for-science

Vibe coding with Claude: 39 ‘free’ models to use, apparently…:

https://open.substack.com/pub/aidisruption/p/39-free-models-to-use-with-claude

Interesting spin off from Google Ventures:

https://open.substack.com/pub/appeconomyinsights/p/how-motive-makes-money

Erratum: “do” in my intro to #432 should be “so”. Apologies. Typing on move again.

Robots on the move:

https://open.substack.com/pub/robopub/p/new-atlas-robot-heads-to-hyundai

The buzz is shifting from GPUs to memory. SanDisk, Seagate. One share tipping site today: “In February 2025, Western Digital spun off Sandisk. Wall Street yawned. The market was brutal. Sandisk opened at $52.20—then promptly crashed 7% to close at $48.60. That $5.6B valuation at close represented a 65% haircut from the $16B Western Digital actually paid for the company in 2016. Wall Street thought flash memory was a dead commodity. They were wrong. 11 months later, Sandisk is a ~$50B juggernaut. The “easy” money in GPUs has been made. The real money is now in the second-order effects—the bottlenecks that hyperscalers can’t engineer around, memory storage is just one of them.”

xAI going from $50 bn 2024 valuation to $200 bn on a $20 bn raise.

https://open.substack.com/pub/aidisruption/p/xai-raises-20b-more

With OpenAI looking to increase its next up round from a $500 bn to a $830 bn valuation this year and a float at $1.5 tn in 2027, SpaceX looking to IPO this year or next for $1.5 tn, Anthropic eying a $350 bn valuation round were headed into uncharted waters for private into public. Saudi Aramco can’t be considered a real comparator here IMO.

AI at the science frontline and frontier:

https://open.substack.com/pub/sciencewtg/p/americas-genesis-mission-artificial

Very true. The advances may come in the boring industries and mundane if impressive tech has longer adoption curves than current expectations credit:

https://substack.com/@chocolatemilkcultleader/note/c-196658825?r=2kxl2k

AI tools worth paying for?:

https://open.substack.com/pub/artificialintelligencemadesimple/p/the-ai-tools-im-actually-paying-for

In the author of the piece’s opinion ChatGPT and Claude trump Gemini on the top tier ($/£200 pcm)

Why isn’t AI taking all the (white collar jobs (already)?:

https://open.substack.com/pub/randomwalkwithdata/p/if-ai-is-taking-jobs-then-where-are

Answer perhaps: aging in place. Maybe we need more RE in the FIRE?

SK Hynix, Micron and Samsung highlighted on this one as (mixing metaphors again) the ‘gatekeepers’ to solving the data centre storage ‘bottleneck’:

https://open.substack.com/pub/uncoveralpha/p/2026-ai-landscape-who-benefits-the

SK Hynix tipped by Woodford last year since when it’s gone from ~200,000 South Korean Won a share to ~750,000, on a 12x TTM P/E today, compared to just 6x a year ago. Say what you like about Neil (and no doubt WPCT/ SUPP/ INOV has to be one of the biggest % disasters in Investment Trust history) he still called this one right.

The Apple/TSMC partnership (a deep dive):

https://open.substack.com/pub/semianalysis/p/apple-tsmc-the-partnership-that-built

The TSMC fundamentals over the last several years are amazing.

It’s all about context window memory: storage stocks melt up:

https://open.substack.com/pub/amritaroy/p/memory-and-storage-stocks-are-melting

Astonishing claims on I/O speed up and power requirements from fractal, distributed computing.

Is this a steaming load of BS or something worth investigating further???:

https://open.substack.com/pub/fractalcomputing/p/if-data-is-the-new-oil-what-if-prices

My suspicion antenna are screaming red alert, but it beggars belief there’s not more info on this given the magnitude of the claims made. Why are OpenAI et al not all over this? They’re invested in survival and winning, not propping up a failed data centre paradigm of bigger and more expensive is better. I’m struggling to see how *everyone* but the fractal computing people would be incentivised to downplay or ignore this. Maybe my framework (sociological motivation for sidelining) for looking at this question is wrong.

AI = energy:

https://substack.com/@denisgorbunov/note/c-197401471?r=2kxl2k

But Claude 4.5 and Claude Code apparently not to shabby either:

https://open.substack.com/pub/thezvi/p/claude-codes

Masayoshi Son, SoftBank, Switch and financing the AI boom:

https://open.substack.com/pub/netinterest/p/financing-the-ai-boom-2

“How long can Nvidia’s “insane demand” for GPUs last? And what happens to the company when the peak cash cow of their only viable revenue source is over? Nobody has given a good explanation. The semiconductor industry is cyclical by nature, Nvidia’s market cap rise has lifted the entire Semiconductor sector with it, but it’s boosted mainly on myths of scaling”:

https://open.substack.com/pub/futuresin/p/nvidias-2026-updates

Ohio gets it right? 6 bn cubic ft per day of new natural gas. Cheap electricity. Attract data centers. New construction jobs:

https://open.substack.com/pub/doomberg/p/intelligent-design

“AI 2027” now AGI 2034?? Some detailed and wild extrapolation here:

https://open.substack.com/pub/aifutures1/p/ai-futures-model-dec-2025-update

It (the AI Futures Model) is just so nuts here that it’s actually worth quoting Seth Llyod’s widely cited 1999 MIT paper on “The Ultimate Physical Limits of Computation”:

https://arxiv.org/abs/quant-ph/9908043

“A kilogram of ordinary matter holds on the order of 10exp25 nuclei. If a substantial fraction of these nuclei can be made to register a bit, then one can get quite close to the ultimate physical limit of memory without having to resort to thermo nuclear explosions. If, in addition, one uses the natural electromagnetic interactions between nuclei and electrons in the matter to perform logical operations, one is limited to a rate of approximately 10exp15 operations per bit per second, yielding an overall information processing rate of ≈ 10exp40 operations per second in ordinary matter. Although less than the ≈ 10exp51 operations per second in the ultimate laptop, the maximum information processing rate in ‘ordinary matter’ is still quite respectable.”

“The ‘ultimate laptop’ is a computer with a mass of one kilogram and a volume of one liter, operating at the fundamental limits of speed and memory capacity fixed by physics. The ultimate laptop performs 2mc2/π¯h = 5.4258 × 10exp50 logical operations per second on ≈ 10exp31 bits. Although its computational machinery is in fact in a highly specified physical
state with zero entropy, while it performs a computation that uses all its resources of energy and memory space it appears to an outside observer to be in a thermal state at ≈ 10exp9 degrees Kelvin. The ultimate laptop looks like a small piece of the Big Bang.”

Somehow, even with AI assisted accelerating progress (which, with respect to AI Futures, is one heck of a big assumption to make here) I can’t see us getting anywhere even close to just the room temperature version of the ‘ultimate’ computer, at ~10exp40 FLOPS/CPS per kg / litre. If so, this then, in turn, surely rules out the AI Futures main projection on grounds of practical if not physical credibility (AFAICT).

Some ordinarily, and all too plausibly, bad AI futures (no apocalypse/ no economic collapse, just sucks in a regular, if still profoundly depressing, way; think idiotocracy):

https://open.substack.com/pub/bloodinthemachine/p/four-bad-ai-futures-arrived-this

There’s nothing new under the sun whether (as Jesse Livermore, writing as Edwin Lefèvre, observed in his autobographical ‘Reminiscences of a Stock Operator’) in the arena of investing or, for that matter, in computer science and AI.

Just as the Panic of 1907 and the Great Crash of 1929 share many features, will we look back upon the later 2020s as being a 1974 like (first) outbreak of AI winter moment?:

https://youtu.be/hYnadoy8aQE?si=IWMKQk8zRPH4YGad

The first explicitly articulated AI bubble ran from the coining of the term as a grant research marketing tool at the Dartmouth conference of 1956; but it took until long after the General Problem Solver of 1957 and the Perceptron of 1958 failed (both already obviously so by the time that Eliza debuted in 1964, in a misfired attempt to show the shallowness of the field) for the UK to finally pull the funding plug with the Lillil report in 1973 and then DAPRA to do likewise in the US the following year.

How long might it take this time around? If the past is a prelude (which it rarely is), and taking the first LLMs as 2017 and comparable, in relative importance, to say the Perceptron of 1958; then we’d be looking at 2032/33 for things to fall apart.

But the cycle of hype and the vortex of VC and Private Lending funding is much greater now than then. That suggests to me an acceleration of developments and cycles now relative to the field of ‘AI’ in the 1960s.

This is the US orientated bull case for LLMs *already* creating a productivity boom extracted from the start of one of the many (many) investing related emails which I get now each day (I can’t share a link as there’s no associated Substack etc to it to share):

“Productivity is eating inflation for lunch
“Experts” told us what would happen. All the tariffs, deportations, pressure for lower interest rates, pointed to a guaranteed crash. They were wrong…Trade Deficit: Dropped to $29.4B in October, down from $136B in March. That is the lowest level since 2009. GDP Growth: The Atlanta Fed projects 5.1% for Q4. The historical average since 1947 is only 3.2%. We are growing nearly twice as fast as “normal.” Inflation: It isn’t spiking. It is dropping. Down to 1.9% (Truflation). How does that happen? Productivity. It’s up nearly 5%. Companies are figuring it out. They’re using AI. They’re navigating deregulation. They are producing more profit with fewer people.”

I have to say, and against my sceptical (cynical?), miserly curmudgeonly instincts, this is (I reluctantly concede) actually quite plausible. I’m using enterprise Copilot at work. Copilot is not exactly highly regarded by premium paid tiers LLM ‘super users’. But I’m impressed nonetheless. Surprisingly impressed.

On appropriate tasks (some of which are very highly complex and novel, multi layered questions), and with my guiding, shaping, evaluating and amending (where needed) outputs; I reckon (even with all that human domain ‘expert’ input) that, overall, I’m still getting at least (a conservatively estimated) composite 2x to 3x speed up (i.e. 3 days’ of work compression into 1; although, sadly, this just leaves more time for bureaucracy to fill the vacuum productivity creates).

On “What Happens When Superhuman AIs Compete for Control?” 🙁

https://open.substack.com/pub/aifutures1/p/what-happens-when-superhuman-ais

It’s either 6,000 words of ‘sci fi’ ‘faction’ presented both as a warning scenario and as an aid to prudent risk management,… or we’ve had it….

I can’t see how 2027 is still on the table as a serious timeline for AGI (or even ‘just’ for the timeline for fully Automated Coders) given the evident slow down in the rates of improvement of GPT4 to 4.5, GPT4.5 to 5, GPT5 to 5.1, GPT5.1 to 5.2 and from Gemini 2.5 to 3; as compared to the rate and the overall magnitude of incremental improvements in utility from going from GPT3 to GPT4.

Building moats at the AI application layer:

https://open.substack.com/pub/artificialintelligencemadesimple/p/how-openai-builds-amazing-products

Despite incremental increasing utility, core failure modes (most notably non specificity of output to instructions, of which hallucinations were the most obvious early examples) still seem to be ‘baked in’ to the current diffusion and transformer architectures, even after nearly a decade:

https://youtu.be/bv19nXfb0bc?si=OjDUYSY5IdLaoZpC

Not promising for AGI.

Finally, saying the silent part out loud:

https://open.substack.com/pub/aisupremacy/p/generative-ai-might-be-hurting-the-labor-market-future-of-jobs

Yes, it is, I think, likely that LLM/LRMs, and wider neural net ML applications, are now starting to cause measurable, and indeed significant, permanent job losses.

Kinda obvious that it probably would, sooner or later; and so it seems likely that it is now actually showing up.

To paraphrase and mangle misquote Sherlock Holmes, when you’ve eliminated everything else what remains is truth.

If this is indeed ‘AI’ related job losses (especially given the strong GDP prints State side), then the issue becomes not if but how far, and for how long, does this go on for???

Are we about to be entering a world transformed (and not necessarily for the better) job wise???

Although the Chinese seem to be doing pretty damm well on the innovation front to me.

They don’t exactly feel like Kodak in 1975, developing, but not then going on and commercially pursuing, a digital camera:

https://open.substack.com/pub/robopub/p/world-no-1-chinese-firm-open-sources

The AI blizzard is coming in thick and fast to my investment related inbox. I’ll quick fire the next nine in below in the interests of economy.

Here’s the first, starting with Mr Musk’s very bold claims:

https://open.substack.com/pub/aidisruption/p/musks-3-hour-bombshell-interview

For powering data centres China’s a hundred years ahead of the US, apparently…

https://open.substack.com/pub/exponentialview/p/data-to-start-your-week-26-01-12

AI capex is not slowing down…

https://open.substack.com/pub/crackthemarket/p/the-crack-the-market-signal-1

We’re on motorcycle and either about to career off a cliff, or head up a jump ramp to leap over half a dozen monster trucks!

Software AI was just the warm up act:

https://open.substack.com/pub/theaiopportunity/p/robotics-will-be-the-next-decades

And, lastly, this from the aforesaid commentary (from Money Machine Newsletter) on the effect of AI on jobs and productivity:

“There’s an old habit in the investing world. It goes like this…If companies aren’t hiring, the economy must be crashing. It’s time to break that habit. Everyone spent the last year panicking about a “cooling labor market.” They looked at the slowing hiring rates and flattened work hours and predicted the worst. They were wrong. While mainstream media is obsessing over headcount, real GDP accelerated to a 4.3% annualized pace. Productivity surged at nearly 5%. This is what efficiency looks like. We are seeing an economy that generates more output with fewer workers. That isn’t a recession signal. That is the holy grail of business. It’s a productivity-driven expansion. For corporate America, this is the perfect setup: Unit labor costs go down. Inflation pressure eases. Profit margins expand. Companies are realizing they can grow earnings without aggressive hiring or raising prices. They are doing more with what they have. That’s why earnings and revenues are hitting records even while payroll gains moderate.”

And on the current viability of the picks, shovels and power producers as investment themes:

“AI’s big checks just cleared, expect more zeros in the market. Microsoft spent ~$9B on IREN—a stock we called out early in the year, when it was on no one’s radar.
Amazon tossed in ~$5B for CIFR. Yesterday, these were bitcoin miners. Today, they’re AI power plants. AI’s no longer about compute—it’s about capacity.
Each new data center sucks down enough juice for 100,000 homes. Satya Nadella (Microsoft CEO) nailed it: “We have the compute. We just don’t have the power to plug it into.” You can buy GPUs. You can’t buy electricity. At least not fast enough. So the leverage quietly shifted—from silicon to supply. How long will this last, who knows. Could this change? Absolutely. But not at this very moment”.

And on Apple choosing Google Gemini to power Siri:

https://open.substack.com/pub/fundaai/p/researchgoog-google-gemini-may-become

Another win for Alphabet with TPUs, Gemini 3.0, AI search summaries, Waymo tie in, Google cloud, DeepMind, AlphaFold, and all the rest. A good year for Google.

Evening @TI.

Direct knowledge is a dangerous thing given (as Morgan Hounsel reminds us) that we each sample just 0.000000001% (1 exp minus 11) of the lived experiences of the 117 bn odd who’ve ever lived, but….that said: even though it’s clearly not at all what any of us ever though AGI would be, and, on any reasonable and fair minded view, it isn’t (at least yet) anywhere even remotely near to what a truly generalisable AI should be capable of; the top paid tier frontier LLMs still deserve more credit for what they demonstrably *can* do than what they’re currently getting from most people most of the time.

I’m using mid tier (£24 pcm) Copilot to do my self assessment this month. It seems to be getting it right (have to make sure all the docs are OCR’d first of course).

Just on educational YouTube today, ChatGPT 5.1 scores 88% on 2nd undergrad year quantum physics paper in just 30 seconds for a 3 hour paper:

https://youtu.be/JcQPAZP7-sE?si=vTd02DjjoTpLWeIh

How is it possible that this tech’s not having a positive effect on productivity? It beggars belief.

I’m not saying (unlike Elon) that this is the singularity. It certainly ain’t that.

But it is something, and probably something really quite important.

I am sure many companies will be able to downsize payroll and boost the bottom line P&L very soon. Whether they’ve the gumption and the brass neck to do so might turn out to be a different matter.

Of course, the socioeconomic and public finance effects (less payroll tax) *if* we see mass ‘head shed’ could be devastating if it’s handled badly.

But that doesn’t mean that the rate of profit in GDP can’t go up a lot even as a result of only mundane utility models. The aggregate profit margin is probably no longer a mean reverting series.

Of course, none of this means LRMs/LLMs can find out anything truly new (though they might, see AlphaFold and DeepMind). And without genuine innovation it’s an open question whether you can get long term economic growth out of ML. But you probably can reduce the labour share of output, make the Gini coefficient worse, make a few people very rich indeed, and raise the IRR for shareholders.

And of course the first to ‘lose’ their job will be the young would be starters who don’t ever get taken on to begin with because the cheapest lay off is the one you don’t make because you freeze recruitment. I think this is demonstrably what we’re starting to see in graduate employment stats.

My cohort (the fifty plus brigade) will be in the firing line eventually I guess, but at least I’m expensive to make redundant (21 months’ pay).

Interesting pointa.

On your a)., if the task is outside the circle of competence/ comfort zone of the person concerned then they’d have to outsource to a professional at much greater cost than a paid up LRM (yet alone a free one).

If the task was something the person could do then the LRM can do it so much quicker even with checking.

Any 130+ IQ undergrad physics savant could get 88% on a 2nd year paper on quantum mechanics but (remembering that the paper used was one which had never been made public, and so couldn’t have been within the model’s pre training set).

But no human could answer the paper at such a solid 1st class (>70%) degree level in matter of seconds (versus the 3 hours allotted the exam).

There must (surely?) be a huge (actual / potential?) speed up for tasks here.

*If* workflows can really be joined up / managed effectively by agentic automation imminently, then I’m finding it hard to see how there’s not then going to be some possibly really quite big productivity boost.

That doesn’t necessarily mean any more revenue/ sales for firms, but it would be disruptive, and it might be important.

On b)., these models are, in practice, becoming a heck of a lot more reliable, accurate and useful over time (so far).

It’s true that they still screw up some tasks and for really compute intense things like image creation they still can mangle the instructions.

But I can’t help feeling like we’ve passed the iPhone moment.

We’re not I think anymore in the Palmtop Palm Pilot / PDA / Apple Newton phase of a ‘nice idea’ but it’s clunky, hard to use, not that useful (of the GPT3 era).

Whether things can continue to improve given scaling walls, energy constraints (especially getting new generation onto the network), financing woes (Oracle CDS spreads) etc is an open question; although, as a general steer to action, I tend not to bet too heavily against progress.

On c). the outsourcing question is fascinating.

It might make outsourcing even cheaper and more useful / reliable because the offshore centre is fully utilising LRMs etc.

On the other hand, fully agentic models (if they do arrive soon, as promised) make automated reshoring / ‘inhousing’ much more economically viable too.

I suppose a virtual task doesn’t really have a location.

Although the comparison is inexact (to say the least), I’m slightly reminded of 1989, in the aftermath of Tiananmen, when my history teacher provoked derision for saying (IIRC, it’s stuck with me) that within 40 years the world would be looking to copy China’s economic model (state capitalism / market socialism). It seemed ridiculous then, with China’s midget peasant based economy, and the communist world then teetering on thr brink of collapse. Less than 37 years on and China leads the world on robotics, manufacturing, exports, high speed rail, fastest urbanisation in history, greatest and swiftest lift of people out of poverty in history, largest PPP GDP in world etc.

Maybe in the early 2060s we will look back on the shaky start to the ML era in 2022/23 in a similar way.

All agreed.

It’s a heck of a lot of work / iterating and what not to fully use these tools (and perhaps most aren’t) but, by God, for the right task, when they do work well, then they’re (or at least can be) downright impressive.

And I don’t impress easily on tech. I stayed well clear of dot.com companies and though social media in the aughts to be a profitless scam. More fool me! 😉

I think that the biggest issue is that it’s not obvious (at least me) that it’s a case of the ‘West and then the Rest’ on AI.

China may be a lot closer to realising the benefits of this and interrelated automation tech than the US realises or understands.

I’ll post some links in a second from the last couple of days on this. I mention it here in case it gets auto filtered into moderation (as more than one hypertext per comment / post).

I wonder if the question isn’t so much, for now, outright job losses as changing job structure and roles for those in work.

This one today further presses the point:

https://open.substack.com/pub/shanakaanslemperera/p/the-apprenticeship-severance-ai-eliminated

For every winner there’s a loser, and with Apple choosing Google’s Gemini over both xAI’s Grok and OpenAI’s ChatGPT, Elon Musk and Sam Altman are the losers for sure:

https://open.substack.com/pub/garymarcus/p/the-rapid-rise-and-slow-decline-of

Is copper the true ‘biggie’ for AI related materials’ bottlenecks?:

https://open.substack.com/pub/paretoinvestor/p/copper-crisis-2026-supply-shortage

A current state of the frontier view of using models:

https://open.substack.com/pub/robotic/p/use-multiple-models

Loyalty to all is a moat for none.

Generate the idea in GPT5.1. Feed it to Claude to tear down. Present both sides to Gemini 3 to evaluate. Ask Grok if anyone’s come up with anything similar on X etc. Explore the research topic literature on Perplexity.

Strong criticisms. Here’s the academic paper (How AI Destroys Institutions) from Boston University School of Law on SSRN:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5870623

Too plausible I fear.

This is a civic / natural justice / public law perspective.

But there’s a broader tendency of historical thought on the anarchist and revolutionary left that the one and only technology which was truly democracy enhancing and empowering (of the masses both individually and collectively) was the spread of the cheap small arm because it afforded the peasantry a means of force to resist feudal exactions.

All the others, the printing press, radio (propaganda), tanks, etc; all enhanced the power of the centre over the periphery and the mighty over the meek.

Will AI follow that template?

AI running the economy hot?:

https://open.substack.com/pub/marklapedus/p/analyst-intels-foundry-unit-wins

“We know we will likely catch some flak from the economist crowd, who will find every possible way to explain this away, but we have a hot take here. It’s possible that the hundreds of billions of dollars we’ve been investing into the “productivity-enhancing-technology-machine” for the past three years is actually…enhancing productivity.”

Yep. 😉

The dark side of data centres and of LLMs: “Less reading time. Less human connection. Less stable white collar career ladders. Higher electricity bills. More noise pollution for local communities. More AI spam and AI slop on the internet. Less human and real social media (as if that were even possible). More authoritarian use of AI by Governments, big corporations, tycoons, dictators and elites to serve their own agendas”:

https://open.substack.com/pub/aisupremacy/p/was-2025-the-year-of-the-ai-datacenter

“What could possibly go wrong” AI as a socioeconomic phenomenon:

https://open.substack.com/pub/adamtooze/p/the-ai-boom-as-a-socio-economic-phenomenon

Copper theme again as a shortfall bottleneck critical material for the AI buildout.

A very user friendly and down to Earth guide to better prompts:

https://open.substack.com/pub/ruben/p/how-to-better-use-ai-before-prompting

More on Apple’s Siri choosing Google’s Gemini:

https://open.substack.com/pub/aidisruption/p/google-snags-apple-ai-deal-chasing

A very deep dive on all things ASML:

https://open.substack.com/pub/aryadeniz/p/deep-dive-asml-holding-asml

Go Netherlands!!

Adapt work for AI or AI for work?:

https://open.substack.com/pub/amistrongeryet/p/the-new-model-of-software-development

“My timeline is suddenly awash in engineers (including me!) reporting that Claude Code is revolutionizing their work”

“AIs struggle (for now) with large projects, but they can drive the cost of small projects to near zero”

“software engineering is still mired in the steam era. Designed around the strengths and limitations of human engineers, current development practices will soon seem as ridiculous as a factory full of shafts and belts”

“Electric motors didn’t revolutionize manufacturing by doing the job of a steam engine. They did it by enabling a new approach to machine power that eliminated the need for shaft-and-belt systems and multi-story factories. AI is going to revolutionize industries, not by doing traditional jobs in traditional ways, but by enabling new, often more personalized, approaches”

Excellent Gresham House lecture (bizarrely starting about 46 minutes in!?) on the history and economics of ‘AI’ from the 1955 proposal for what became the Dartmouth conference the next year (and which, arguably, created the field as a tangible endeavour) onwards:

https://www.youtube.com/live/0HvvPDZoxdA?si=xR-BE0urjpOI8N4w

Does AI have to ride on the coattails of human / evolved biological intelligence or are many pathways to many different types of intelligences possible? The true ‘Alien’ may be the AI (ASI?) one which we ourselves had a hand in creating.

What does Ash say in “Alien”….Ahh yes:

‘I admire it’s purity’.

I can hear Elon’s voice with those words! 😉

Indeed. OpenAI’s sole objective is to become TBTF:

https://albertoromgar.medium.com/you-have-no-idea-how-screwed-openai-is-9481fe33f1db

AI eats SaaS for breakfast:

https://open.substack.com/pub/davefriedman/p/the-saas-selloff-ai-and-interest

Interesting on the margins and cost structure. New billing model needed.

Running enterprise workflows in Gemini:

https://open.substack.com/pub/aidisruption/p/building-enterprise-workflows-with

Jarvis your AI digital butler from Gemini 3:

https://open.substack.com/pub/aidisruption/p/gemini-awakens-googles-ecosystem

More circular funding, now xAI:

https://parsers.vc/news/260108-ai-titans-surge–xai-secures-billions/

No problemo. Will do. I was just going with one link per post to avoid the spam filter kicking anything with more than one link into the awaiting moderation bucket, and, thereby, hopefully to save you the work of then having to moderate it. I hadn’t realised that it could create work in and of itself !

A phalanx of new curated AI related links from later today:

SIGNIFICANT MARKET SHIFTS

ChatGPT To Show Adverts After Losing Billions (The Telegraph)

https://www.telegraph.co.uk/business/2026/01/16/chatgpt-to-show-you-adverts-after-losing-billions/

This marks the end of the subsidised generative AI era.

OpenAI’s pivot to advertising confirms that subscription revenues alone cannot offset massive compute costs. For investors, this creates a direct collision with Google’s search monopoly and fundamentally alters the unit economics of the AI sector. Expect volatility in ad-tech stocks as budget allocation shifts toward conversational interfaces.

Google Starts to Eat the World (Neural Foundry)

https://open.substack.com/pub/neuralfoundry/p/google-starts-to-eat-the-world

A signal that the incumbent is successfully leveraging its distribution moat.

Google is moving from defensive maneuvering to aggressive ecosystem lock-in, integrating agentic AI to cannibalise standalone startups. The implication is that “wrapper” companies are doomed; value is consolidating back to the platform layer. This reinforces the bull case for Alphabet as it operationalises its proprietary data advantages to crush fragmentation.

The Death of Software 2.0

https://open.substack.com/pub/mule/p/the-death-of-software-20-a-better

A paradigm shift for the SaaS business model.

We are approaching a tipping point where bespoke software is generated on demand rather than purchased as a static SaaS product. This threatens the recurring revenue moats of traditional B2B software companies. Capital should reallocate toward the infrastructure providers enabling this generation, rather than the application layer which is rapidly commoditizing.

The $40 Billion Disaster That Nobody Is Talking About (Private Markets News)

https://open.substack.com/pub/privatemarketsnews/p/the-40-billion-disaster-that-nobody

A warning on private market valuations.

A critical autopsy of a massive capital destruction event in the private equity/venture space. This serves as a stark reminder that elevated AI valuations require rigorous due diligence. Investors must scrutinize “growth at all costs” narratives, as the market begins to ruthlessly punish burn rates that lack a clear path to profitability.

INFRASTRUCTURE & HARDWARE

AI Needs Electricians: The $300 Pay Boom (AI Disruption)

https://open.substack.com/pub/aidisruption/p/ai-needs-electricians-300-pay-boom

Identifies the hard physical bottleneck of the digital economy.

The constraint on AI scaling is shifting from GPU availability to power delivery. With skilled labor shortages driving electrician wages to $300/hour, data center CapEx estimates must be revised upward. This highlights utilities and infrastructure service firms as essential, defensive plays in the AI portfolio.

Research: AXTI & Indium Phosphide (InP) (Funda AI)

https://open.substack.com/pub/fundaai/p/researchaxti-indium-phosphide-inp

Novel Insight. A specific deepdive into critical optical materials.

As data clusters scale, copper interconnects are failing. This piece outlines the thesis for Indium Phosphide (InP) as the necessary material for next-gen photonics and optical data transfer. AXTI represents a high-beta supply chain play on the inevitable transition to optical networking within the data center.

TSMC: AI Arsenal Builder (App Economy Insights)

https://open.substack.com/pub/appeconomyinsights/p/tsmc-ai-arsenal-builder

A reinforcement of TSMC’s monopoly status. Despite geopolitical risks, TSMC remains the singular bottleneck for silicon innovation. The analysis suggests their pricing power is increasing, protecting margins even as they ramp up massive CapEx for 2 nanometre and 1.4 nanometre processes.

The Connectivity Fabric of the AI Data Center (I Am Fabian)

https://open.substack.com/pub/iamfabian/p/the-connectivity-fabric-of-the-ai

Focuses on the “plumbing” of AI switches, interconnects, and cabling. As GPUs get faster, the network becomes the drag. Investors should look at Broadcom, Marvell, and optical networking firms that solve latency issues in massive clusters.

STRATEGY & ADOPTION

AI and the Age of Individual Empowerment (Big Technology)

https://open.substack.com/pub/bigtechnology/p/ai-and-the-age-of-individual-empowerment

Discusses the rise of the “one person unicorn.” AI tools are increasing individual leverage to the point where small teams can rival incumbent output. This suggests a VC shift toward smaller, leaner startups with lower capital requirements but high operational leverage.

Inside McKinsey’s AI Operating System (The AI Opportunity)

https://open.substack.com/pub/theaiopportunity/p/inside-mckinseys-ai-operating-system

An overview of how enterprise giants are attempting to standardise AI deployment. While useful for understanding corporate adoption cycles, it highlights the friction large firms face compared to agile competitors. Good for gauging the pace of Fortune 500 AI spend.

The AI Manager’s Schedule (Generatives)

https://open.substack.com/pub/generatives/p/the-ai-managers-schedule

Analyses how middle management is evolving. As AI handles coordination and reporting, manager value shifts to high level strategy and human empathy. Investment implication: Short traditional HR tech; long platforms that enable “management as a service.”

Why I Picked Legal AI & How to Build (AI Made Simple)

https://open.substack.com/pub/artificialintelligencemadesimple/p/why-i-picked-legal-ai-how-to-build

A case study on Vertical AI. Legal tech remains one of the highest ROI sectors for GenAI due to high text volume and billable hour structures. Offers a template for evaluating other vertical specific AI investments.

GEOPOLITICS & CONSUMER

ByteDance AI Glasses: Strategic Denial (Hello China Tech)

https://open.substack.com/pub/hellochinatech/p/bytedance-ai-glasses-strategic-denial

ByteDance is aggressively entering the hardware space to rival Meta. This signals that the battle for the “face” (smart glasses) is the next consumer frontier. Investors should watch for regulatory headwinds regarding Chinese hardware in Western markets.

ChatGPT’s Google Translate Feels Unnecessary (AI Disruption)

https://open.substack.com/pub/aidisruption/p/chatgpts-google-translate-feels-unnecessary

Real time, multimodal translation is now a commodity feature, not a standalone product. This spells trouble for single-purpose language learning apps and translation devices, viewing them as features within larger models rather than investable moats.

MACRO & MISC

The Quiet Tax Change That Could Reshape Portfolios (Nicholas Vardy)

https://open.substack.com/pub/nicholasvardy/p/the-quiet-tax-change-that-could-reshape

Discusses fiscal policy adjustments that may impact capital gains or sector specific write offs. While jurisdiction specific, it’s a reminder to optimise portfolios for tax efficiency as governments look to raise revenue from the tech boom.

Clouded Judgement: Platform Engineering (Clouded Judgement)

https://open.substack.com/pub/cloudedjudgement/p/clouded-judgement-11626-platform

A technical look at SaaS metrics and platform engineering efficiency. Useful for benchmarking cloud infrastructure stocks, specifically regarding how efficiently they are converting R&D spend into deployable code.

Interesting to see the Agglomerations piece in today’s as always excellent MV w/e links on the purported non effect (at least yet) of ML(/’AI’) on employment.

Can’t say that I’m actually convinced by it, but it puts the case for the complex causation well and is a welcome evaluation, giving nuance to an unclear and at this time probably unfalisiable set of scenarios for what’s going on and why.

One area which *is* however being impacted right now (bigly, as the Donald would say) is SaaS whose wall poster casualty is (mixing metaphors again) this train wreck:

https://open.substack.com/pub/davefriedman/p/the-saas-selloff-ai-disruption-in

With the broader SaaS drawdown (due to AI?) here:

https://substack.com/@reboundcapital/note/c-201240216?r=2kxl2k

And here’s the 2026 outlook from the AI futures project:

https://open.substack.com/pub/aifutures1/p/forecast-how-ai-will-progress-in

With a forecast data centre Capex of $520 bn ($10 bn per week) from just the top 4 hyperscalers!!:

https://substack.com/@onveston/note/c-201251030?r=2kxl2k

Facepalm moment. PLTR is an AI company not a SaaS one:

https://substack.com/@sergeycyw/note/c-201479074?r=2kxl2k

PLTR revenue growth literally off the charts on this, but Duolingo and Crowdstike sit at sweet spots:

https://substack.com/@sergeycyw/note/c-201106932?r=2kxl2k

Still, the fundamental point is sound. AI fear has made the sector ‘cheap’ on typical ‘rearview mirror’ backwards looking metrics:

https://substack.com/@sergeycyw/note/c-201148467?r=2kxl2k

What’s priced in:

https://open.substack.com/pub/tradesandgains/p/wall-streets-global-gamble-ai-chips

This one goes with the AI scapegoat thesis for those job losses (couple of month’s old now):

https://open.substack.com/pub/europecapitalnews/p/the-new-scapegoat-how-ai-became-the

And I see they share my long term bullish Alphabet outlook (this from August, I’m now going to add them to my Substack 200+ long list of free ‘subscriptions’, although, of course, that might just be an example of my own confirmation bias 😉 ):

https://open.substack.com/pub/europecapitalnews/p/alphabets-crossroads-navigating-the

In an AI arm’s race maybe the only winning move is not to play?

https://open.substack.com/pub/europecapitalnews/p/is-the-us-winning-the-3-trillion

And, before reading this one, I’d kinda mentally written Meta off on AI, but now maybe I need to rethink?:

https://open.substack.com/pub/investinquality/p/meta-platforms-is-the-market-underestimating

Productivity and AI:

https://www.telegraph.co.uk/business/2026/01/18/silicon-valley-misfits-fixing-worlds-productivity-slump/

Palantir and AI, the origin story:

https://open.substack.com/pub/theaiopportunity/p/the-palantir-origin-playbook-20032013

Impressive: “Code named FastRender, the project produced over 3 million lines of code, with a core rendering engine written in Rust from the ground up, even including a custom JavaScript virtual machine”, and all in just 168 hours from a standing start:

https://open.substack.com/pub/aidisruption/p/cursor-ai-writes-3m-lines-in-168

No AI shortcuts with agents:

https://open.substack.com/pub/tylerfolkman/p/your-ai-agents-are-skipping-v1-thats

And please don’t ask transformers to write your prompt:

https://open.substack.com/pub/ruben/p/your-prompt-sucks

Latest batch of AI themed pieces from the last 48 hours:

Investing in the Age of Extremes (Market Sentiment)

Analyses current market polarization, comparing today’s high valuations and tech concentration to historical bubbles, advising investors on navigating a “bipolar” market environment.

https://open.substack.com/pub/marketsentiment/p/investing-in-the-age-of-extremes

Further Breaking News Further Vindicating (Gary Marcus)

Marcus argues that recent admissions by AI leaders regarding “scaling walls” vindicate his long-standing skepticism about the limits of Large Language Models and deep learning.

https://open.substack.com/pub/garymarcus/p/further-breaking-news-further-vindicating

Did the Market Just Call the Top? (Nicholas Vardy)

Vardy examines technical indicators and sentiment data suggesting the current bull market may have peaked, warning investors to prepare for potential downside risks and volatility.

https://open.substack.com/pub/nicholasvardy/p/did-the-market-just-call-the-top

AI Deniers’ Ideas are Cheap (Michael Wiggins De Oliveira)

Criticises AI skepticism, arguing that betting against the technology is costly. He highlights specific investment opportunities where AI adoption is driving tangible financial outperformance.

https://open.substack.com/pub/michaelwigginsdeoliveira/p/ai-deniers-ideas-are-cheap-outperformance

571 Fooled, Runway Gen 4.5 Out (AI Disruption)

Covers the release of Runway’s Gen-4.5 video model and discusses a recent study where AI generated content successfully fooled a significant majority of human evaluators.

https://open.substack.com/pub/aidisruption/p/571-fooled-runway-gen-45-out

The Verification Inversion (Shanaka Anslem Perera)

Perera explores the growing asymmetry between cheap content generation and expensive verification, arguing that the cost of discerning truth will skyrocket as AI scales.

https://open.substack.com/pub/shanakaanslemperera/p/the-verification-inversion

AI Race, Iranian Malaise, How Tudor… (Adam Tooze)

In the intensifying global AI arms race, China flips the US on downloads 

https://open.substack.com/pub/adamtooze/p/ai-race-iranian-malaise-how-tudor

The Energy Singularity (Shanaka Anslem Perera)

Discusses massive energy demands of scaling AI infrastructure, predicting a pivotal moment where physical power constraints will dictate the pace of future technological advancement.

https://open.substack.com/pub/shanakaanslemperera/p/the-energy-singularity-8f0

The Internet is Turning Against OpenAI 2026 (AI Supremacy)

Chronicles shifting public sentiment against OpenAI, citing rising concerns over copyright data usage, lack of transparency, and the company’s move away from its non profit roots.

https://open.substack.com/pub/aisupremacy/p/the-internet-is-turning-against-openai-2026

Breaking: Sir Demis Hassabis Becomes… (Gary Marcus)

Marcus highlights recent comments by DeepMind’s Demis Hassabis regarding the limitations of current generative AI, interpreting them as high-level confirmation of the “scaling wall” hypothesis.

https://open.substack.com/pub/garymarcus/p/breaking-sir-demis-hassabis-becomes

The Robots are Coming and Wall Street… (Shanaka Anslem Perera)

Analyses the intersection of humanoid robotics and financial markets, predicting how the commercialisation of physical AI agents will disrupt labor markets and drive investment trends.

https://open.substack.com/pub/shanakaanslemperera/p/the-robots-are-coming-and-wall-street

Day 47: Quick Valuation Tech/SaaS Companies (DIY Investor)

Provides a concise valuation framework for Tech and SaaS companies, offering specific metrics and ratios to help individual investors assess fair market value during earnings season.

https://open.substack.com/pub/diyinvestor1/p/day-47-quick-valuation-techsaas-companies

My Thoughts on the “Masaasacre” (Manu Invests)

Offers a personal perspective on the recent sharp sell off in SaaS stocks, evaluating whether the downturn represents a capitulation event or a fundamental sector reset.

https://open.substack.com/pub/manuinvests/p/my-thoughts-on-the-masaasacre-my

Big Ideas 2026 (ARK Invest)

ARK’s annual research report detailing their high-conviction investment themes for 2026, focusing on disruptive innovation in AI, multiomics, reusable rockets, and autonomous logistics.

https://www.ark-invest.com/thank-you-Big-Ideas-2026

R1 Turns One, DeepSeek Model 1 Emerges (AI Disruption)

Reports on the anniversary of the R1 model and the emergence of DeepSeek Model 1, highlighting the rapid advancement and competitive performance of Chinese open weights AI.

https://open.substack.com/pub/aidisruption/p/r1-turns-one-deepseek-model-1-emerges

Real Signal China VC 2025 (Hello China Tech)

Surveying the 2025 landscape of Chinese Venture Capital, identifying where smart money is flowing, specifically deep tech and AI, despite broader macroeconomic challenges in the region.

https://open.substack.com/pub/hellochinatech/p/real-signal-china-vc-2025

Milestones of China in AI of 2025 (AI Supremacy)

Summarises key 2025 achievements in Chinese AI, specifically focusing on the technical breakthroughs and global adoption of models from DeepSeek and Alibaba’s Qwen.

https://open.substack.com/pub/aisupremacy/p/milestones-of-china-in-ai-of-2025-deepseek-qwen

The Big Short Meets Marcus on AI (Gary Marcus)

Draws parallels between the 2008 housing bubble and the current AI hype cycle, suggesting that overinvestment in generative AI may lead to a similar market correction.

https://open.substack.com/pub/garymarcus/p/the-big-short-meets-marcus-on-ai

BlackSky AI: 15 Daily Views of Global… (RoboPub)

Details BlackSky’s AI capability to provide and use high frequency satellite imagery, allowing for 15 daily views of critical global locations to support real time intelligence and monitoring.

https://open.substack.com/pub/robopub/p/blacksky-ai-15-daily-views-of-global

CoreWeave Revenue Backlog Now Exceeds… (Michael Wiggins De Oliveira)

Reports on CoreWeave’s massive revenue backlog, using it as a bullish signal for the sustained, tangible demand for GPU compute and AI infrastructure.

https://open.substack.com/pub/michaelwigginsdeoliveira/p/coreweave-revenue-backlog-now-exceeds

The Thermodynamic Reckoning: OpenAI (Shanaka Anslem Perera)

Argues that OpenAI faces a “thermodynamic reckoning,” where physical constraints regarding heat dissipation and energy consumption will severely bottleneck further model scaling efforts.

https://open.substack.com/pub/shanakaanslemperera/p/the-thermodynamic-reckoning-openai

*If* we get aligned ASI then it’ll be aligned with the technocratic oligopoly, Elysium 2.0:

https://youtu.be/zAxBoEkY4d0?si=ldEc6iEY8HIZpVJe

Since the 1980s, the share of US wealth owned or in effect controlled by the top 0.00001% persons (i.e. just 20 to 30 or so individuals, and their families) has risen from 4% to 12%. In a late 22nd century ASI world, that could exceed 99%, maybe we’ll over 99.9% (i.e. if global aggregate wealth can increase thousands to millions of times under a fully human labour substituting, ASI led, automated robotic production, hyper growth scenario).

Even without AGI/ASI, it’s very questionable if the technology will allow what we like to consider democracy to survive ‘intact’:

https://open.substack.com/pub/garymarcus/p/ai-bot-swarms-threaten-to-undermine

Elon Musk, already worth some $750 bn today, and talking of SpaceX (42% ownership, 100% effective control), due to float next year for $1.5 tn, being worth within his lifetime $100 tn, is ascendant:

https://open.substack.com/pub/shanakaanslemperera/p/the-davos-inversion-the-day-elon

And, speaking of the WEF, the Davos and the AI sets have now merged into one mass of momentum:

https://open.substack.com/pub/bigtechnology/p/ai-davos-four-reflections-from-the

Meanwhile, You Tubers going wild again on TSLA for robotics (and FSD/robotaxis and energy storage, the latter being essential for AI and advanced humanoid robotics):

https://youtu.be/UdEAs9jjNQU?si=x-nMd3Yr-ASd2GWg

Even though calmer heads have a more sanguine perspective:

https://open.substack.com/pub/bradmunchen/p/more-tesla-driverless-pr-stunts-in

All considered, *if we can control ASI* (doubtful I’m afraid), should it emerge this century (plausible), then it’ll be birthed by the like of (or the successors to) Google and Anthropic, and not by either xAI or OpenAI (the jokers in the pack?):

https://open.substack.com/pub/neuralfoundry/p/openai-and-xai-are-losing-the-ai

Google’s looking at token processing increases in the 50x to 100x per year range (most recently >3x in 3 months, i.e. ~10,000% annualised). That’s incredible:

https://open.substack.com/pub/apoorv03/p/why-we-invested-in-baseten

Although the future won’t belong to any one model:

https://open.substack.com/pub/cloudedjudgement/p/clouded-judgement-12326-the-year

And explicit monetisation remains the near term challenge:

https://open.substack.com/pub/aidisruption/p/openai-we-may-take-a-cut-of-future

Of course, there’s always an angle to make money, as the recent surge in memory stocks shows:

https://open.substack.com/pub/tmtbreakout/p/sndkmemory-the-perfect-storm-jensen

But, ultimately, shortages and bottlenecks in ‘AI’ will lead to surpluses and to gluts:

https://open.substack.com/pub/arcadianow/p/is-there-a-compute-surplus-on-the

As one active investment newsletter (h/t MMN) put it yesterday: “investors… they’re betting that inflation is coming back. They think the Fed has lost control. Which is why even though the Fed cut rates, long-term bond yields went up. They’re wrong. Inflation has cooled significantly. Official numbers (CPI) sit around 2.7% year-over-year for December 2025, with core at 2.6%. The Fed’s preferred PCE measure is similar, hovering near 2.6–2.8%. Real-time trackers like Truflation are even lower, recently dipping to 1.2%, showing disinflationary trends ahead of the official stats. The disconnect…The bond market pushed the 30-year yield near 4.9%. They’re pricing in a future that AI has already canceled. The real cause…Efficiency. The Fed thinks interest rates control prices. They don’t. Better tools control prices. Look at Lemonade. The insurance company just launched “Autonomous Car Insurance” for Tesla owners using Full Self-Driving mode, slashing per-mile rates by about 50% when FSD is engaged. Why? Not because of a Fed meeting. But because the data shows self-driving miles carry much lower risk—fewer accidents, lower claims. That isn’t a coupon. That’s a paradigm shift. We are seeing this everywhere. AI is doing to services what the assembly line did to the Model T… It makes things better, faster, and cheaper all at once. That is deflation, plain and simple. The bankers are studying history textbooks. The technologists are writing new code, inventing things that never existed before. One group thinks money is about to get worthless; the other is making everything cost less.”

Tom Pueyo of Uncharted Territories super bullish on AI progress today (more upbeat than I am tbh):

https://open.substack.com/pub/unchartedterritories/p/ai-in-2026

He was also very bullish on TSLA robotaxis IIRC and quick to dismiss LiDAR / Waymo on costs (a bit too quick, IMHO).

Hoarders, managers and builders, taking the temperature on the tech bros’ AI related singularitarianism after Davos:

https://open.substack.com/pub/exponentialview/p/the-end-of-the-fictions

Whatever the score on the singularity (and those tech and energy learning curves) OpenAI still looks like toast (to me at least):

https://open.substack.com/pub/aisupremacy/p/openai-is-about-to-face-real-competition-enterprise-ai

I may have to end up eating my words here, but if there’s one vector of AI exposure which I don’t want in my investments it’d be to OpenAI post the 2027 IPO. And here’s Altman today shilling for cash from the Gulf:

https://open.substack.com/pub/adamtooze/p/openai-hustling-for-gulf-dollars

Meanwhile the SaaSapocalypse continues:

https://open.substack.com/pub/sergeycyw/p/saas-valuation-weekly-recap-e60

If ML isn’t the end of the sector could be a bargain. If it is though then a value trap awaits.

Personally, I’ve dabbled a bit in Western Union stock as the anti-fintech, anti-Stablecoin, anti-XRP, anti-AI related disruption play in the ($2 quadrillion annual payments’ volume – nearly 20x global output – 3.6 trillion transactions a year) cross border payments sector. One of the lowest PEs in the US (single digits), 10% dividend, share buybacks. It’s trading like a liquidation stock but, when I go into the Post Office, I can’t help but see the prominent WU franchise and people actively using it.

The value is in the disconnect. Streaming and YouTube make terrestrial TV pointless but most people still have a TV and a licence.

Meanwhile the relaxation in the restrictions in exporting H200 GPUs to China might not help Nvidia as much as Mr Market currently thinks:

https://open.substack.com/pub/shanakaanslemperera/p/the-two-gate-trap-45-trillion-positioned

A cheap US listed mega cap tech stock ?! Rarer than hen’s teeth:

https://substack.com/@studios/note/c-204342738?r=62vrvp

The TTM P/E is 37x though, but forward P/E at 11x is what matters for the future, albeit it’s going parabolic which, even though it’s underpinned by fundamentals improving fast, is generally a red flag.

Economics of AI, lower inference costs but worse business models:

https://substack.com/@scstrategist/note/c-204442204?r=62vrvp

https://open.substack.com/pub/lesbarclays/p/who-captures-the-value-when-ai-inference?l

Per JPM here:

https://am.jpmorgan.com/us/en/asset-management/liq/insights/market-themes/artificial-intelligence/

“J.P. Morgan reports that the cost efficiency frontier has dropped by 99.7%, from $37.50 per million tokens for GPT-4 in March 2023 to $0.14 for GPT-5 Nano by August 2025. The balanced frontier, which optimizes both capability and cost, has improved just as much. SemiAnalysis finds that algorithmic improvements alone can boost efficiency by four to ten times each year. Nvidia, using SemiAnalysis’s InferenceMax v1 benchmarks, shows that software updates for the B200 cut inference costs fivefold in just two months, from $0.11 to $0.02 per million tokens for open-source models”

With task length (eyeballing JPM slides) completed autonomously at 80% success going up four fold last year from 6 minutes to a more practically useful 27 minutes, and with the bank estimating/ reporting (if the latter not clear on source) a very chunky 1.5%-3% p.a. increase to US labour productivity.

AI beyond the note taker:

https://open.substack.com/pub/ruben/p/you-forgot-70-of-yesterdays-meeting?

OpenAI revenue scaling linerally with power consumption and compute. Anthropic clearer path than OpenAI to cashflow positive:

https://open.substack.com/pub/exponentialview/p/ev-558

And in other news, @Mrs DH tells me that the February 2026 (and yet already published, so they live in the future!) “Garden answers” reports (on p.9) that the boffins at Ohio State University have found that “strands in the mushroom mycelium (the vegetative part of fungi) can act as living processors, or memoristors, transmitting electrical impulses like neurons in the brain and storing information with around 90% accuracy”. There’s then a picture of a circuit board hooked up to some large mushrooms. Apparently biodegradable fungal mushrooms are “cheaper than conventional semiconductors that use rare minerals and expensive energy”. Miniaturisation appears to be an issue.

Feels time for some Bear case views (these are a few days to a month old now, but YT’s algo just served them up on the current feed):

Zitron’s take on impending (apparently) data centre underutilisation:

https://youtu.be/rsCGisbz04Y?si=uukPrGjkr63-ZeIx

And Gary on the scaling wall here:

https://youtu.be/aI7XknJJC5Q?si=5IdniepjVWo9rs5V

And AI being a House of Cards here:

https://youtu.be/fUVOP0tWn2U?si=smnQKpBsRiMQMEOi

Latest wave of links breaking on my email/Substack shore:

The losers in the AI race:

https://open.substack.com/pub/neuralfoundry/p/openai-and-xai-are-losing-the-ai

A semis mini special round up:

https://open.substack.com/pub/jimmysjournal/p/semiconductor-industry-outlook-2026

https://open.substack.com/pub/thesemiconductornewsletter/p/week-4-2026

https://open.substack.com/pub/thesemiconductornewsletter/p/glass-substrates-as-surprising-new

With some specifics and deep dives for TSMC:

https://open.substack.com/pub/fundaai/p/deepasml-upside-from-tsmc-expansion

https://open.substack.com/pub/crackthemarket/p/tsmc-manufacturing-the-worlds-ai

For Tencent:

https://open.substack.com/pub/hellochinatech/p/enflame-tencent-captive-supplier

And, for the EUV supplier behind the foundries, ASML:

https://open.substack.com/pub/tacticzhazel/p/sell-asml-after-this-100-increase

More on Claude Code:

https://open.substack.com/pub/neuralfoundry/p/claude-code-will-be-the-most-important

And SaaSmaggedon with special reference to roll ups Constellation Software’s (previously rare) epic drawdown:

https://open.substack.com/pub/margarineofsafety/p/softwares-funeral

And the K shaped economy and whether AI is playing a role:

https://www.telegraph.co.uk/business/2026/01/24/trump-may-boast-but-his-two-speed-economy-could-blow-up/

Thinking through Damodaran’s interview linked to above in my immediately last post, if every company has equal access to AI they’ll save costs but then have to compete on price, unless they have a durable (anti-) competitive wide moat (e.g. Meta’s network effects, the non pecuniary switch costs from Apple’s ecosystem); and will thereby lose revenue, resulting in aggregate earnings’ reductions, and, ultimately, margin compression, with lower prices for end consumers/customers and a deflation effect.

But for those companies behind such a moat, they can maintain prices and simultaneously decrease their costs, making them even more dominant.

An AI assistant with access to all your sensitive personal financial data. What could possibly go wrong?:

https://www.telegraph.co.uk/business/2026/01/27/ai-breakthrough-runs-work-and-finances-via-whatsapp/

After all if this can be hacked…..

https://open.substack.com/pub/shanakaanslemperera/p/the-inverted-panopticon

And agentic AI risk is not just on the malware/ security front:

https://www.telegraph.co.uk/business/2026/01/26/ai-jobs-carnage-is-harming-britain-more-than-global-rivals/

Along with Cloudstrike this is the sort of business to benefit:

https://open.substack.com/pub/michaelwigginsdeoliveira/p/nbis-the-market-is-missing-what-comes

And there’s that AI – satellite convergence:

https://open.substack.com/pub/nicholasvardy/p/how-to-think-about-spacex-before

A trio from the Beeb on AI today

Victors and carnage:

https://www.bbc.com/news/articles/cr57p2ve8glo

China winning?:

https://www.bbc.com/news/articles/c86v52gv726o

Sight to the blind:

https://www.bbc.com/future/article/20260126-ai-mirrors-are-changing-the-way-blind-people-see-themselves

It all about memory now, not GPUs:

https://open.substack.com/pub/shanakaanslemperera/p/the-limiting-reagent-triad-602-billion

Wither Reddit in the face of the AI storm???:

https://open.substack.com/pub/michaelwigginsdeoliveira/p/reddit-markets-ai-panic-with-40-growth

More innovation, in the semi layer now:

https://open.substack.com/pub/artificialintelligencemadesimple/p/meet-wafer-the-yc-startup-making

Love the deep fake of Sam getting the wrestling treatment. OpenAI really is the weakest link, or co-weakest with Oracle:

https://open.substack.com/pub/neuralfoundry/p/openai-is-getting-crushed-on-all

But it might not matter. We’ve something perhaps more consequential than the ‘Greenspan Put’ for equities for AI now. This PoV from one newsletter (MMN) today: “we are currently in a silent arms race with China for AGI. The U.S. government literally cannot allow the AI infrastructure sector to fail. If private funding dries up, the government will backstop it. Regardless of your point of view, whether you think it’s good or not, it’s reality. This is “Too Big To Fail” 2.0.”

The AI trade is broadening out:

https://open.substack.com/pub/leadlagreport/p/the-ai-trade-is-broadening-and-portfolios

P(Doom) postponed:

https://open.substack.com/pub/aifutures1/p/clarifying-how-our-ai-timelines-forecasts

But still not much time to wake up to the risks:

https://www.theguardian.com/technology/2026/jan/27/wake-up-to-the-risks-of-ai-they-are-almost-here-anthropic-boss-warns

Case study of a winner in the AI enabled cyber security race:

https://open.substack.com/pub/businessinvest/p/115-nrr-23-revenue-growth-31-cash

And is it all about taste not AI outperforming humans?:

https://open.substack.com/pub/theaiopportunity/p/the-business-of-taste

Opsie, that should have been ‘up and to the right!’ (in the case of memory stocks, this past month’s more a case of just straight up vertical ascent on the charts).

The Foresight Institute (the grand daddy of the futurist think tanks) has just done a massive AI YT shorts drop:

Anders Sandberg is always entertaining on AI:

https://youtu.be/loiOT7QTorI?si=2iKtbslMZFLpNkuk

David Eagleman on the effect of AI on humans and our brains:

https://youtu.be/psHiTmafKeg?si=NbuRgb3s2bhmkSVY

Whole brain AI emulation?:

https://youtu.be/gUXMZCuk4_Y?si=NYPWkURXZKz4kpZa

Fiduciary AI?:

https://youtu.be/egJAS5fzDw4?si=4Z_CUITBH0wpMBEi

With several others on AI and AGI adjacent themes.

And this freestanding one from a climate scientist on the effect of AI power demands:

https://youtu.be/py0XpKxAnNU?si=791yMUtVg88donSs

Gary on LLMs eating themselves:

https://youtu.be/yex6Ti2VPr0?si=8IHurb9O5yVd-cpc

Data, data, data. But the contrasting ability to learn from little is the human edge, for now. 

Continual learning necessary for AGI? Ex OpenAI researcher changed his mind in past year:

https://youtu.be/XtPZGVpbzOE?si=BOtrY1G-X8LSin6b

One bleak view on OpenAI’s solvency:

https://youtu.be/J3pidxrneeQ?si=lX-KyMZtI-U3GVJJ

Thinking Machines dismessbled:

https://youtu.be/kj-SOMwDNhA?si=rafsmO9m-8FJopv-

Lefty Novara media on an AI jobs apocalypse:

https://youtu.be/Y5A_c4pvo7I?si=lts5JbANXNOYdxiF

1999 parallels:

https://www.youtube.com/live/O1aU5ewHATQ?si=4s6DjIOMAnrNOOyG

Deep:

https://open.substack.com/pub/afewthings/p/the-source-code-of-reality-revisiting

Transformer crunch:

https://open.substack.com/pub/themonentaryskeptic703/p/intermediation

Turns out one type of transformer is necessary for the other!

And data, data, data means memory, memory, memory. MMN today: “Dynamic random access memory (DRAM) spot prices just hit a record ~$30…average spot prices for 16GB DDR4 is up over 2,300% from last year to ~$77.”

Data is the new oil:

https://open.substack.com/pub/nicholasvardy/p/memory-is-the-new-oil-why-storage

GPT-5 unit economics:

https://open.substack.com/pub/exponentialview/p/inside-openais-unit-economics-epoch-exponentialview

ASML earnings broken out x3:

https://open.substack.com/pub/massivemoats/p/asml-q4-2025-earnings-review

https://open.substack.com/pub/tacticzhazel/p/asml-q4-earnings-review-update-valuation

https://open.substack.com/pub/fundaai/p/reviewasml-4q25-record-high-bookings

And META’s x2:

https://open.substack.com/pub/fundaai/p/reviewmeta-4q25-drivers-of-1q26-accelerating

https://open.substack.com/pub/jamesfoord/p/meta-crushes-but-im-buying-this-stock

MSTF and that OpenAI dependency:

https://open.substack.com/pub/aidisruption/p/microsoft-makes-76b-in-one-quarter

And all those earnings’ no’s crunched in one place:

https://open.substack.com/pub/sergeycyw/p/meta-servicenow-tesla-microsoft-earnings

Visibility before viability in AI integrated humanoid robots:

https://open.substack.com/pub/hellochinatech/p/china-robot-ipo-rush-2026

GMO seeing the culmination of 300 years’ of bubbles.

https://www.gmo.com/europe/research-library/valuing-ai-extreme-bubble-new-golden-era-or-both_viewpoints/

Recently, the number of countries hitting new 52-week highs reached 47.  That’s 67% of the global index. It exceed the previous 2003 peak. Contra GMO, maybe this time it really is different….

The ‘myth’ of the 95% of firms seeing no benefit:

https://open.substack.com/pub/exponentialview/p/how-95-escaped-into-the-world

The bifurcation of AI adoption:

https://open.substack.com/pub/aisupremacy/p/ai-adoption-tells-two-separate-stories-2026

Semis and jobs:

https://open.substack.com/pub/thesemiconductornewsletter/p/semiconductor-industry-and-job-landscape

That Davos interview with Hassabis dissected:

https://open.substack.com/pub/bigtechnology/p/google-deepmind-ceo-demis-hassabis-946

Palantir’s bill of health (as a stock holder it looks stalled now after an epic run):

https://open.substack.com/pub/fundaai/p/previewpltr-4q25-continue-to-accelerate

Zvi’s tour du horizon:

https://open.substack.com/pub/thezvi/p/ai-153-living-documents

Clawdbot limitations and potentials:

https://open.substack.com/pub/aidisruption/p/clawdbot-10000-data-and-tools-247

Misunderstanding Apple’s AI pivot and on device inference:

https://open.substack.com/pub/davefriedman/p/apples-ai-game-is-misunderstood

Again, unit economics dominate.

AI risk infects the bond market:

https://open.substack.com/pub/adamtooze/p/ai-in-the-bond-market-how-the-uk

DeepSeek approved for Nvidia H200s:

https://open.substack.com/pub/hellochinatech/p/deepseek-h200-framework-validation

I wonder if Beijing has been bluffing all along on import restrictions on semis. Best way to get something is to pretend you don’t want it, especially to gullible Yankees…

OpenAI’s game of survival:

https://open.substack.com/pub/aidisruption/p/openais-2026-game-of-survival

The great showmen Altman and Musk are living on borrowed time. When the circus moves on… speaking of which:

https://open.substack.com/pub/ftav/p/what-becomes-of-tesla-when-another

All about ASI Xrisk (p(Doom)):

https://open.substack.com/pub/thezvi/p/on-the-adolescence-of-technology

Agentic land grabs in Microsoft’s workflow wars:

https://open.substack.com/pub/appeconomyinsights/p/microsoft-workflow-wars

Holy Moses, one Deep Value blogger/ Substaker goes ‘all in’ on AI infra stocks:

https://open.substack.com/pub/michaelwigginsdeoliveira/p/strategy-2026-theme-is-taking-shape

Meme bubble top????

AI and Space, the final frontiers (or an endless, all consuming, dark void for VC cash 🙁  )

https://open.substack.com/pub/fundaai/p/deeprdw-selling-shovels-in-the-space

Clawbot becomes Openclaw (?):

https://open.substack.com/pub/aidisruption/p/openclaw-kimi-k25-support-kills-clawdbots

SaaS and AI again. You only die twice:

https://open.substack.com/pub/cloudedjudgement/p/clouded-judgement-13026-software

Don’t forget the Advanced Packaging for chips:

https://open.substack.com/pub/marketsentiment/p/advanced-packaging

Case studies in the AI design wars from Adobe and Microsoft:

https://open.substack.com/pub/davefriedman/p/ai-is-killing-figma-a-capital-structure

Coreweave now TBTF:

https://open.substack.com/pub/neuralfoundry/p/coreweave-cannot-fail

And DJT’s Fed nominee Kevin Walsh completes the hawk to dove transformation (metamorphosis) on the back of a belief that technology makes businesses so much more efficient that prices will drop  without Fed intervention to stop inflation.

What could possibly go wrong??

A permanently high plateau of productivity. Where have I heard that one before?

SaaS sell of continues with Adobe too cheap to ignore (but maybe still too expensive to buy?) and with multiple 52 week lows in sector stalwarts:

https://open.substack.com/pub/multibaggernuggets/p/forever-portfolio-the-software-sell

https://open.substack.com/pub/qualitystocks/p/adobe-stock-analysis-too-cheap-to

https://open.substack.com/pub/diyinvestor1/p/day-49-52-week-lows-in-saas-and-tech

Foundry earnings season breakdown:

https://open.substack.com/pub/marklapedus/p/q4-25-foundry-earnings-hit-or-miss-288

Coreweave and how the absence of a forward curve to price GPUs is hiking financing costs:

https://open.substack.com/pub/davefriedman/p/coreweaves-30-billion-bet-on-gpu

Google DeepMind CEO on the AI bubble meter:

https://open.substack.com/pub/atmosinvest/p/weight-watchers-value-trap-or-multibagger

From human computers to thinking machines:

https://youtu.be/Wm4UD-O6FrA?si=uX-jiHdVHH6MHh01

Half of planned data centres may never be built:

https://youtu.be/j2nE3_HCvoU?si=cgucuS33UNJPEVPM

Revisiting the ‘Bitter Lesson’ of AI research:

https://youtu.be/2hcsmtkSzIw?si=HifqYwzfeY_zJ71S

Some really excellent foundational points (the primitives or elements of ML v actual evolved, embodied intelligence) here:

https://youtu.be/Sx_hzF960GE?si=pyJbb6vo1wA89_NF

Meaning only becomes intelligence through a forward looking interaction with the physical environment for survival/ survival adjacent purposes. Backward looking (training set) token prediction based on an interaction with a natural language prompt definitionally cannot be or lead to intelligence, at least in the embodied consciousness sense. But what about other forms of disembodied self awareness? Maybe not through current LLM/LRM architecture but still purely digital domain?

A combined Moltbook and agentic AI mini special of sorts:

https://open.substack.com/pub/davefriedman/p/the-moltbook-moment-why-policymakers

147,000 agents in first 72 hours, now 1.5 million, and 12,000 communities:

https://open.substack.com/pub/exponentialview/p/data-to-start-your-week-26-02-02

Under the hood of Moltbook (plus Quantum, Meta v Microsoft and whose winning the AI race):

https://open.substack.com/pub/bigtechnology/p/the-markets-ai-guessing-game-moltbook

Zvi’s take:

https://open.substack.com/pub/thezvi/p/welcome-to-moltbook

Social media meets AI, into the mist with agent ecologies:

https://open.substack.com/pub/importai/p/import-ai-443-into-the-mist-moltbook

Dissecting Open Claw:

https://open.substack.com/pub/aisupremacy/p/what-is-openclaw-moltbot-2026

Google anti-gravity now with agent support:

https://open.substack.com/pub/aidisruption/p/google-antigravity-adds-native-agent

Agents and sub agents with Claude code slash commands:

https://open.substack.com/pub/aidisruption/p/claude-code-adds-slash-commands-and

As to humans and AI ‘enhanced’ social networks:

https://open.substack.com/pub/hellochinatech/p/sandbox-strategy-tencent-ai-social

In China they’re using WeChat to diagnose patients before they see their equivalent of a GP. The medic just checks the AI diagnosis and prognosis/treatment.

And in other news: 

In Zuck we trust? I don’t think so…

https://open.substack.com/pub/thescienceofhitting/p/in-zuck-we-trust

Tesla shape shifting into AI with the less obvious plays highlighted:

https://open.substack.com/pub/nicholasvardy/p/as-tesla-goes-all-in-on-ai-look-past

And the SaaS sell off continues:

https://open.substack.com/pub/randomwalkwithdata/p/saas-attacked-ongoing

And game companies look screwed too (?):

https://open.substack.com/pub/fundaai/p/deepu-and-app-and-ttwo-and-rblx-why