Who Profits in the AI Era? A Review of Daniel Gross's 18 AGI Predictions

Original Title: The Remarkable AGI Trades of Daniel Gross

Original Author: @johncoogan

Original Compilation: Peggy, BlockBeats

Editor's Note: In early 2024, AI was still in a phase of both frenzy and uncertainty. At that time, Daniel Gross posed 18 questions on a single page: Where will value flow? Will energy become a bottleneck? Will software engineers be replaced? How will the competitive landscape between nations change?

Looking back two years later, these questions themselves are more enlightening than any specific predictions. AI profits have indeed concentrated at the infrastructure layer—Nvidia emerged as the biggest winner; energy and electricity rapidly became new strategic bottlenecks; API costs plummeted, while computing power, capital, and geopolitical risks continued to amplify.

This article revisits the key questions Gross raised at the time and examines them one by one against the reality of the past two years. It is not only a review of AI investment logic but also a roadmap for observing how technological revolutions reshape market structures, industry chains, and the global power landscape.

The following is the original text:

In January 2024, Daniel Gross, then CEO of Safe Superintelligence and now Head of AI Product at Meta, published an article titled "AGI Trades."

The article was just one page long, listing a series of questions about the potential impacts of AI progress. Looking back over two years later, these questions appear remarkably prescient, even though no definitive conclusions were provided for each at the time. Below, we review each of his 18 questions.

Markets

In a post-AGI world, where will value flow?

Currently, value is indeed concentrated at the infrastructure layer—chips, packaging, electricity, and other areas. Nvidia has captured over 100% of the profits from the AI boom, as many companies are still operating at a loss. This is also clearly reflected in market cap changes: Nvidia's market cap increased by $3.2 trillion, rising from $1.2 trillion to $4.4 trillion; in comparison, cloud platforms saw much more modest gains (Microsoft up 4%, Amazon up 30%).

In the private market, the valuation growth of OpenAI, Anthropic, and xAI has also been astonishing, but their combined total value increase of $1.4 trillion is still lower than the market cap Nvidia added in the same period.

This was a crucial question right from the start of 2024.

What will happen to Nvidia and Microsoft?

Nvidia has performed exceptionally strongly. Its revenue grew from $60.9 billion in FY2024 to $215.9 billion in FY2026, nearly tripling.

Microsoft has been less dominant. Azure's growth did accelerate to a 40% year-over-year rate, but from January 2024 to March 2026, Microsoft's stock price rose only 4%. The market has questioned its annual AI capital expenditure exceeding $80 billion—it remains unclear when these investments will translate into returns.

In this AI gold rush of "selling picks and shovels," Nvidia is clearly the biggest winner, while Microsoft's infrastructure bets have yet to deliver significant returns to shareholders.

Is copper mispriced?

It was severely undervalued. In January 2024, copper was priced at $3.75 per pound; two years later, it reached a record high of $6.61 per pound.

AI's demand for copper is enormous. For example, the Nvidia GB200 NVL72 server rack uses over 5,000 copper wires. If laid end-to-end, the total length exceeds 2 miles. A 100MW data center requires approximately 3,000 tons of copper.

Overall, data centers could consume 500,000 tons of copper annually. Some have therefore called "copper the new oil." Of course, many other things have also been called "the new oil" because AI infrastructure construction is extremely complex, with bottlenecks at almost every stage. So this analogy should be viewed with caution.

Real Estate

If AI can write all software, will San Francisco become the new Detroit?

It depends on what is meant by "the new Detroit."

AI actually saved San Francisco from becoming a declining city like Detroit. San Francisco is still thriving:

· Office vacancy rate dropped from 36.9% to 33.5%

· OpenAI occupies 1 million square feet of office space

· Anthropic occupies a 25-story office building

· Sierra signed a lease for 300,000 square feet of office space

In the first half of 2025, 78% of US AI venture capital funding flowed to the Bay Area. Of course, there is another side: San Francisco's total employment is still below pre-pandemic levels, but housing prices remain strong. Therefore, it is far from a "hollowed-out city." The urban environment has also become cleaner.

How will AI affect wealth inequality?

It's still too early to conclude, and data changes are not yet pronounced, but some noteworthy studies exist.

An IMF 2025 study suggested AI might reduce wage inequality (by automating high-income jobs) but could exacerbate wealth inequality (as capital gains concentrate among tech company owners). An OECD study found that low-skill job wages grew fastest (assemblers +11.6%), while high-skill job wages grew slowest (CEOs +2.7%), though this might reflect minimum wage policies more than AI itself.

In capital markets, concentration is also rising: The "Magnificent Seven" (Mag7) account for about 32% of the S&P 500's market cap and contributed about 42% of its total return in 2025. Meanwhile, massive funding rounds for AI startups (OpenAI $110B, Anthropic $30B) have created enormous private wealth for a small number of founders and investors.

Energy & Data Centers

If AI becomes an energy competition, how should one invest?

This assessment was completely correct. AI has indeed become an energy game.

Those who caught this trade made significant profits. For example:

• Vistra: +321%, the second-largest gainer in the S&P 500 in 2024 (after Palantir)
• Constellation Energy: Stock price tripled since ChatGPT's release
• NRG Energy: Rose about 95% in 2025 alone
• Oklo: Up over 700% in 12 months

Nuclear energy experienced a boom:

• Microsoft signed a $16 billion, 20-year PPA to restart the Three Mile Island nuclear plant
• Google signed a 500MW agreement with Kairos Power for small modular reactors (SMRs)
• Meta signed power contracts totaling 6.6GW with multiple nuclear companies

Energy became one of the most successful investment themes of the AI era.

In the entire data center supply chain, which parts are hardest to scale 10x?

The bottleneck in the chip industry is CoWoS packaging technology (TSMC's Chip-on-Wafer-on-Substrate).

In the data center field, the biggest bottleneck is likely power transformers.

• Lead times approach 3 years
• A 30% supply gap emerged in 2025

Costs have risen 150% since 2020

This 100-year-old technology has become a key constraint on the speed at which data centers can connect to the grid.

Is coal undervalued?

To some extent, yes, but far less so than copper. Coal prices actually fell about 22% in 2025, recovering somewhat by early 2026.

Coal companies performed decently:

• Peabody Energy: +34%
• CONSOL Energy: +37%

Meanwhile, US coal-fired power generation increased by 13% by September 2025.

This was particularly evident in states with rapid data center growth:

• Ohio: +23%
• Oklahoma: +58%

Nations

Who are the winners and losers?

The clear winner is the United States.

In 2024, US private AI investment was $109 billion (China only $9.3 billion). Cumulative investment since 2013 reached $470 billion, exceeding the total of all other countries. The US released 40 significant AI models in 2024, compared to 15 for China.

The game isn't over, but for now, the US is the center of AI competition.

What happens to India's $250 billion GDP export dependency on GPT-4 tokens?

The situation is beginning to show, but it's still early. Hiring in India's IT outsourcing industry has noticeably declined. Between 2024–2025, large IT companies cut about 58,000 jobs, whereas the industry added 360,000 employees between 2021–2023.

Will software engineers be replaced like typists in history?

Software engineers haven't moved to blue-collar jobs yet, but the career structure is already diverging:

• Demand for AI engineers grew 143%
• Entry-level hiring at large tech companies declined 25%
• Internship positions decreased by 30%

The future choice might be: either move up to become "managers of AI agents" or pivot to fields like manufacturing—after all, many factories also need people who understand software to automate production processes.

Will there be a large-scale employment program similar to the "New Deal"?

Not yet.

In July 2025, the Trump administration launched the "American AI Action Plan," including:

• An AI Education Executive Order
• A skills training initiative
• A $84 million Labor Department apprenticeship grant program

But US workforce training spending is only 0.1% of GDP, among the lowest in OECD countries. No plan currently approaches the scale of the original WPA (which employed 8.5 million people).

Is lifelong learning worth investing in?

This is a very abstract and personal question. But my answer is: Yes.

Inflation

If AI is truly deflationary, how would we see the signal first?

The best indicator is likely AI API prices.

GPT-4 level inference cost:

Late 2022: $20 per million tokens

December 2025: $0.40

A 50x decrease in three years. This pace even exceeds the decline in PC computing costs or internet bandwidth costs. This is likely to be a leading indicator of service price deflation.

If demand for knowledge products keeps growing while production costs fall, how should we understand deflation?

While AI API prices have plummeted, AI company revenues are soaring. Price drops → usage explodes → total spending increases. Meanwhile, SaaS companies are adding 20%–37% "AI taxes" to renewal fees. Therefore, even as software production costs approach zero, SaaS revenues are still growing.

This is similar to the computing industry during the Moore's Law era: individual products get cheaper, but the overall market size keeps expanding.

Geopolitics

Is interconnect really important?

Extremely important.

In large GPU clusters, 30%–50% of training time is spent on communication between GPUs, not computation.

For example, Google's TPUv7 Ironwood uses a 3D torus topology to connect 9,216 chips; Nvidia's NVL72 connects 72 GPUs. Therefore, the interconnect network is crucial for AI scaling.

If a country has more energy, can it achieve AGI with lagging process nodes?

Currently, it seems unlikely.

All leading AI chips use 4nm or 3nm processes: Nvidia Blackwell, Google TPUv7, AWS Trainium3.

China's Huawei Ascend 910C (SMIC 7nm) is competitive in inference but requires more chips and more energy for training. Simply increasing energy consumption to compensate for a technological gap eventually runs into economic cost limits.

What is the most likely "Taiwan event"?

The most likely is a blockade of the Taiwan Strait.

Tensions are already escalating:

• 2024: China conducted "Joint Sword-2024B" military exercises
• 2025: "Mission Justice 2025" mobilized over 100 aircraft and 13 warships
• 27 rockets were launched from Fujian, with 10 landing in Taiwan's contiguous zone

Meanwhile, China's 2026–2030 Five-Year Plan began separating the terms "peaceful unification" and "unification."

TSMC is also preparing in advance: 8 fabs are under construction in Arizona, which may handle 30% of advanced chip capacity in the future.

But the entire system remains in an extremely fragile balance.

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