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Scaling Up: How Increasing Inputs Make A.I. More Capable
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This is an excerpt from our most recent Economic Outlook report. To access the full PDF, please click here.
Hyper-scaler capital expenditure (Cap-Ex) has experienced an unprecedented surge, driven by massive investments in artificial intelligence (AI) infrastructure, including data centers, servers, and, most notably, NVIDIA GPUs.
The major players — Alphabet’s Google Cloud (GOOGL - Free Report) , Amazon’s AWS (AMZN - Free Report) , Microsoft’s Azure (MSFT - Free Report) and Oracle Cloud Infrastructure OCI (ORCL - Free Report) — are spending over $600 billion annually — as of early 2026.
To build a deeper understanding up, on this?
I need to re-quote and re-position formal statements made, in the 2025 “Our World in Data” article I shared with you, this month.
Scaling Up: How Increasing Inputs Has Made Artificial Intelligence More Capable
“The path to recent advanced AI systems has been more about building larger systems, than making scientific breakthroughs.”
What is scaling in “AI” models?
Let’s briefly break down what scaling means in AI.
Scaling is about increasing three main things during training, which typically need to grow together:
The amount of data used for training the AI
The model’s size, measured in “parameters”
Computational resources, often called "compute" in AI
The idea is simple but powerful: bigger AI systems, trained on more data and using more computational resources, tend to perform better.
Even without substantial changes to the algorithms, this approach often leads to better performance across many tasks.
In short, the fear hypers-scalers have, living inside a ‘tech oligopoly’ generating excess net profit margins? “AI” can destroy those juicy 2.5X to even 4X net profit margins.
In turn, U.S. hyper-scalers are attempting to ‘bury the competition’ the only way they can: By spending huge annual amounts on “AI” cap-ex compute investment.
As the ‘Our World in Data’ author stated: This approach often leads to better performance across many tasks. That is a moat of competitive advantage the hyper-scalers are looking for.
Now, let’s revisit the latest broad U.S real macro facts: Final U.S. Q3 Real GDP Growth at +4.4%. GDPNow for Q4-25? +3.7%.
Does the huge “AI” cap-ex spending on data center you see make better sense of these broader U.S. real macro growth facts? Yes, it does.
Directly, through the data center construction spending they are doing, and NVDA chips, and other high-performance chips (MU, AVGO, WDC, etc.) they are buying, and
Indirectly, through the run-up in major large cap U.S. stock indices. These market cap weight indices disproportionally include these hyper-scaler and major semi-chip stock tickers.
Passive investing then sees consumer discretionary stock-market driven wealth rise.
Next, let’s revisit the following average monthly U.S. Federal nonfarm job additions, running quarter by quarter over 2025.
These are established, revised facts now…
Quarter Average Monthly U.S. Job Adds
Q1:2025 20 Q2:2025 34 Q3:2025 23 Q4:2025 -17
As of January 2026, here are the historical averages for the U.S. labor market:
Image Source: Zacks Investment Research
So, did “AI” cap-ex, over the last year or two, contribute to the slack U.S. job growth seen, across all 12 months of 2025?
The short answer is: Yes, but not as a "job killer."
In 2025, “AI” Cap-Ex acted more as a hiring brake than a layoff engine.
My Zacks Econ FEB 2026 revised quarterly data (20k, 34k, 23k, -17k) perfectly captures the narrative of a "slow-hiring, slow-firing" U.S. economy — that eventually stalled.
The surge in AI investment (about $300B+ from hyper-scalers in 2025) essentially diverted capital from "human-centric" expansion, into "compute-centric" efficiency.
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Scaling Up: How Increasing Inputs Make A.I. More Capable
This is an excerpt from our most recent Economic Outlook report. To access the full PDF, please click here.
Hyper-scaler capital expenditure (Cap-Ex) has experienced an unprecedented surge, driven by massive investments in artificial intelligence (AI) infrastructure, including data centers, servers, and, most notably, NVIDIA GPUs.
The major players — Alphabet’s Google Cloud (GOOGL - Free Report) , Amazon’s AWS (AMZN - Free Report) , Microsoft’s Azure (MSFT - Free Report) and Oracle Cloud Infrastructure OCI (ORCL - Free Report) — are spending over $600 billion annually — as of early 2026.
To build a deeper understanding up, on this?
I need to re-quote and re-position formal statements made, in the 2025 “Our World in Data” article I shared with you, this month.
Scaling Up: How Increasing Inputs Has Made Artificial Intelligence More Capable
“The path to recent advanced AI systems has been more about building larger systems, than making scientific breakthroughs.”
What is scaling in “AI” models?
Let’s briefly break down what scaling means in AI.
Scaling is about increasing three main things during training, which typically need to grow together:
The idea is simple but powerful: bigger AI systems, trained on more data and using more computational resources, tend to perform better.
Even without substantial changes to the algorithms, this approach often leads to better performance across many tasks.
Next, consider the implications of this table…
Net Profit Margin Overview (Q1 2026)
Entity Net Profit Margin (%)
S&P 500 Index ~13.9%
NVIDIA (NVDA) ~56.0%
Mag 7 (Including TSLA) ~26.2%
Mag 7 (Excluding TSLA) ~29.8%
Source: Zacks research
In short, the fear hypers-scalers have, living inside a ‘tech oligopoly’ generating excess net profit margins? “AI” can destroy those juicy 2.5X to even 4X net profit margins.
In turn, U.S. hyper-scalers are attempting to ‘bury the competition’ the only way they can: By spending huge annual amounts on “AI” cap-ex compute investment.
As the ‘Our World in Data’ author stated: This approach often leads to better performance across many tasks. That is a moat of competitive advantage the hyper-scalers are looking for.
Now, let’s revisit the latest broad U.S real macro facts: Final U.S. Q3 Real GDP Growth at +4.4%. GDPNow for Q4-25? +3.7%.
Does the huge “AI” cap-ex spending on data center you see make better sense of these broader U.S. real macro growth facts? Yes, it does.
Directly, through the data center construction spending they are doing, and NVDA chips, and other high-performance chips (MU, AVGO, WDC, etc.) they are buying, and
Indirectly, through the run-up in major large cap U.S. stock indices. These market cap weight indices disproportionally include these hyper-scaler and major semi-chip stock tickers.
Passive investing then sees consumer discretionary stock-market driven wealth rise.
Next, let’s revisit the following average monthly U.S. Federal nonfarm job additions, running quarter by quarter over 2025.
These are established, revised facts now…
Quarter Average Monthly U.S. Job Adds
Q1:2025 20
Q2:2025 34
Q3:2025 23
Q4:2025 -17
As of January 2026, here are the historical averages for the U.S. labor market:
Image Source: Zacks Investment Research
So, did “AI” cap-ex, over the last year or two, contribute to the slack U.S. job growth seen, across all 12 months of 2025?
The short answer is: Yes, but not as a "job killer."
In 2025, “AI” Cap-Ex acted more as a hiring brake than a layoff engine.
My Zacks Econ FEB 2026 revised quarterly data (20k, 34k, 23k, -17k) perfectly captures the narrative of a "slow-hiring, slow-firing" U.S. economy — that eventually stalled.
The surge in AI investment (about $300B+ from hyper-scalers in 2025) essentially diverted capital from "human-centric" expansion, into "compute-centric" efficiency.