Ground truth for markets today. The sensory layer between AI and the physical world tomorrow.
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The internet connected people to information.
The next layer will connect intelligence to the physical world.
Markets may be one of the earliest versions of this: millions of people turn observations, beliefs and actions into a shared signal.
Humans are the critical link.
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Want a sneak peak at some astounding @SpaceX & @Tesla TERAFAB progress?
Here just a couple of brand-new images of the TERAFAB construction site in Grimes County, Texas.
My last flight was on the 11th of September or just over 2 weeks ago, and just check out the amount of progress they have made since!
Concrete footings on the one large rectangular cleared section are mostly poured while others show the re3bar faces set in place and waiting for concrete. The tree clearing to the north of this foundation segment has widened the area significantly. On the south side, the “postage stamp” rectangular area has grown many times larger with clearing and the Starlink ground station antenna globes have been moved.
I’ll release more and a video update soon … just need to find time in between my Lithium video today and Starship Flight 14 on Monday. There is so much going on and this is only a fraction of what SpaceX and Tesla have going on!
Truly crazy in all the best ways!
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Very cool!
Alphaville: "[There's a] mismatch between consensus forecasts for chip sales and US data centre completions...Morgan Stanley measured the gap earlier this week by estimating the shortfall in available power, concluding that >1/2 of the GPU servers sold between 2026 and 2028 might not have anywhere to be plugged in. Research from Jefferies reaches a similar conclusion using a very top-down method: space. Tracking US data centre construction sites by satellite imagery shows between 16 and 18 GW of gross capacity that can be energised this year...The rush to complete what’s already started makes it likely that for next year, data centre deployment by GW probably can’t go much above the low twenties. This represents a doubling versus the 11 GW deployed in 2025, but is a huge shortfall when compared with what’s implied by chip sales forecasts."
This gets to two points I made in my recent report on "The AI Trade" (sageroadresearch.com/product…). First, hyperscalers appear likely to fail to build out the data center capacity required to improve their models enough to inspire adequate enterprise spending. To quote the report:
"Delays have always been part of the data center construction reality—historically, only 72% of data center capacity has come online on time. However, things are clearly getting worse. Goldman expects only about 1/2 of the AI computing capacity scheduled to activate between now and 2028 via data center construction will actually come online by its target date. As asset valuation firm Barkr calculated in an August report, delays at that rate would translate to a compute supply/demand gap of 13.4 to 19.2 GW in 2027 and 27.3 to 36.8 GW in 2028."
Second, market participants are underestimating how much chip stockpiling has been happening and what that could mean for chip demand expectations moving forward. To again quote the report:
"There’s always been an element of suspension of disbelief in the triple-digit percentage stock gains made by everyone from Nvidia to Broadcom to TSMC, Micron, AMD, and GE Vernova—as if the AI revolution had rendered cyclicality a challenge of the past. Now, AI spending’s rate of change is poised to slow. Politics is likely to increase fear that the pace of the AI data center buildout falls short of expectations. And the threat grows that one or more hyperscalers decides to pull back from the AI arms race. If AI CAPEX slows, it’s likely to hit picks-and-shovels earnings greater than most will anticipate. From hardware shortages has come stockpiling. As one VC noted recently: “In some data centers, I’m hearing the usage of graphics processing units is only around 35% or 40% because some companies are hoarding colossal amounts in case they come to a point at which they don’t have enough chips to provide the computing capacity.”
FT link: ft.com/content/4c3f75a2-c2f9…
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ChatGPT has now a big problem.
Researchers at Oxford and Cambridge exposed a massive threat to large language models.”
They call it “model collapse."
Internet ecosystem is rapidly changing, and generative AI will soon contribute much of the text found online. This forces us to consider what happens to future iterations like gpt-n when they are trained on data scraped from the web that was already generated by an llm.
According to the research, indiscriminately using model-generated content in training causes "irreversible defects" in the resulting ai. the model loses the "tails of the original content distribution." in other words, it begins to forget the creative, fringe, and unique nuances of actual human writing, collapsing into a repetitive echo chamber.
This isn't just a chatgpt issue.. the researchers built theoretical intuition showing this collapse is ubiquitous across learned generative models, occurring in large language models as well as in variational autoencoders and gaussian mixture models.
Tech companies rely on scraping the internet for large-scale data to build smarter models. However, the paper warns that if we want to sustain the benefits of training on web data, model collapse must be taken seriously.
Ultimate takeaway? data collected from genuine human interactions is going to become increasingly valuable in a web filled with ai content.
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GroundAlpha retweeted
The popular narrative of AGI often centers on a single, isolated super-intelligence. My colleagues James Manyika, @bratton, and I have a different model. In our new DeepMind Institute essay, we argue that the AGI transition will be a highly social event.
bit.ly/artificial-symbiotic-…
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The internet connected people to information.
The next layer will connect intelligence to the physical world.
Markets may be one of the earliest versions of this: millions of people turn observations, beliefs and actions into a shared signal.
Humans are the critical link.
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1
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AI has a data problem.
As models generate more of the internet, researchers have shown that recursively training on model-generated data can cause “model collapse” — losing information from the original data distribution.
Human interaction with reality becomes more valuable.
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Rumors I’ve been hearing, not here on X.
First, let’s start with OpenAI and I’ll go towards Anthropic.
GPT-6 Sol is coming Tuesday. It’s both cheaper and more intelligent than 6 Astra, think of it like a 6.2 jump. The internal model “significantly more capable than Astra,” named Bel internally, helped with this release. Bel is considered “AGI” within OpenAI. They are very impressed with this model.
OpenAI is growing very confident that their internal lead is so big that no other lab can catch up. Unbelievably confident.
Anthropic is currently not in, let’s say, a “code red,” but is aware of OpenAI’s lead and doing everything in their power to catch up. Their new model Opus 5.5 is coming probably Monday rather than Tuesday due to OpenAI releasing on Tuesday.
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