u/Random_individual_6

The NYSE is a private actor (publicly traded but not a public entity or a regulated utility) that markets itself as a public marketplace. Yet it charges $8.4k per month for real-time data access, plus $78 per professional user monthly. You can build algorithmic trading systems through Robinhood or Schwab APIs, but you can’t feed them real-time NYSE order flow because the NYSE owns that data and charges massive fees to access it. A developer trading their own money on their own brokerage still can’t programmatically access the market data they need to compete.

When analysis was the bottleneck, data gatekeeping didn’t matter. Now that frontier AI commoditized analytical tools, controlling data access is the only competitive advantage. When retail traders and developers can’t access real-time order flow, prices don’t reflect true value and volatility increases. The regulatory battleground isn’t AI tools anymore. It’s data access. Public markets need public, affordable, real-time data endpoints. Otherwise you’re not running price discovery, you’re running a lottery where entry fee determines odds.

If access to this data doesn’t provide an advantage, then why do people and institutions pay for it?

Following up on the Pelosi disclosure, because she actually placed two nearly identical options trades on May 29, and the Uber one is a completely different animal from the Intel one.

On paper it looks the same. She bought 200 call options on UBER, strike $50, expiring March 2027, worth between $500k and $1 million. Two hundred contracts again, so control of 20,000 shares.

Here is where it splits from the Intel trade. When she bought the Intel calls, the stock was sitting far above the strike, so those were deep in the money and basically a leveraged way to own the stock. The Uber calls are nothing like that. Uber was around $70 when she bought, against a $50 strike, so only about $20 of that is real value and the rest is premium. That makes this a much more directional bet.

Run the numbers. Her break even on the Uber calls lands somewhere around $75 to $80 a share. Uber is at roughly $70 right now, a touch below where she bought it. So unlike the Intel trade, where she is already up nicely, this one is currently in the red. Uber has to climb about 10 percent just to get her back to even, and more than that before she makes a dime.

And if Uber goes nowhere, she bleeds. These calls carry real time premium, so a flat Uber into 2027 still costs her a six figure chunk of the position, somewhere in the $100k to $200k range. If Uber slides back under $50, which is a drop of less than 30 percent, the whole thing is a zero. This is not a leveraged hold like the Intel trade. This is a straight conviction call that Uber goes up.

So what is the bull case she is paying for?

It comes down to two policy fights, and Uber is spending heavily on both. The company put roughly $7 million into lobbying last year, its filings naming transportation, labor, and technology.

The upside is autonomous driving. The Autonomous Vehicle Acceleration Act is sitting in the Senate, and a federal framework that lets robotaxis scale nationwide is exactly the future Uber has bet the company on. The risk is labor. The fight over whether drivers are contractors or employees keeps grinding on, with bills like the Gig Is Up Act trying to reclassify them, which would hit Uber's cost structure directly, and others pushing the opposite way. Whoever wins that one basically sets how much money Uber gets to keep.

She is not alone in the name either. Other members were in Uber ahead of her, including John Hickenlooper, who bought $100k to $250k worth in January, and Ro Khanna, who picked up $65k to $150k in February. Different members, same stock, in the months before Pelosi sized up.

So the questions kind of write themselves.

The Intel trade is a leveraged bet on a company the government literally owns a piece of. The Uber trade is a flat out wager that a federal robotaxi bill and a friendly ruling on gig labor both break Uber's way, and both of those run straight through Congress. Is buying calls on that outcome investing, or is it betting on legislation you help write?

And when a flat stock actually loses you money, the way these Uber calls do, that usually means somebody has a real conviction. What does Nancy Pelosi and the rest of Washington see in Uber that the rest of us do not?

What do you think?

She did not just buy Intel stock. On May 29, 2026 she bought 200 call options on INTC, strike $50, expiring March 2027. Two hundred contracts controls 20,000 shares. INTC was trading around $114 that day, so a $50 strike is deep in the money. This is not a cheap out of the money gamble. It is a leveraged long. She is controlling roughly $2.3 million of Intel for somewhere around $1.3 to $1.5 million in premium, and the position moves almost dollar for dollar with the stock.

So which way does she need it to go? Up, and she is already winning. Her break even works out to about $115 to $125 a share depending on what she paid inside the disclosed range. Intel is sitting near $132 right now, so she is up something like $300k on paper just from the move since late May.

Because the calls are so deep in the money, she barely loses anything if Intel goes flat. If the stock just sits here into 2027 she keeps almost the whole position and only bleeds a little time value. The only way this goes to zero is if Intel falls about 60 percent and crashes back under $50. So she is not betting Intel rockets. She is betting Intel does not fall apart, and probably keeps grinding up, for the next nine months.

Why would anyone be that confident in Intel of all names? Look at who is on the other side of the cap table.

Back in August 2025 the Trump administration converted billions in CHIPS Act money into actual Intel stock, around 433 million shares, and the federal government became Intel's single largest shareholder. That stake cost about $8.9 billion. After Intel's run this year, including a major Apple chip deal, it is now worth somewhere around $57 billion. Washington turned $9 billion into $57 billion on one stock, and the administration keeps promoting it in public.

Then there is the legislation. The Semiconductor Superiority Act was introduced this month in both chambers, next to the Stop Stealing our Chips Act and the SAFE Chips Act, all of them pushing more money and protection toward domestic chip making. Intel is the face of domestic chip making. The company has also spent millions lobbying Congress, with its filings naming appropriations, manufacturing, and trade, which are the exact levers that decide how much help it gets.

And Pelosi is not the only one who noticed. She actually kept her Nvidia (NVDA) and stacked Intel on top of it. But three other House members who bought INTC this year, Gilbert Cisneros, Ro Khanna, and Michael McCaul, were selling NVDA over the same stretch. That is a quiet rotation out of the old AI winner and into the chip stock the government is personally invested in.

Could Intel keep climbing? The bull case is real. The government holds the biggest stake and wants the number to go up, the reshoring push has support from both parties, and fresh money is being written into law right now. The bears will point out the stock has already gone up roughly six times over and is priced for a lot to go right. Pelosi's calls run until March, so she has about nine months for the bull case to keep paying.

A couple of questions worth sitting with.

The federal government owns the largest slice of a company, and the President talks the stock up, and Congress is writing bills to fund it. Is a member of Congress buying leveraged calls on that same company investing? or front running policy the government itself controls?

And when three more members quietly rotate out of Nvidia and into the one chip stock Washington has a direct financial stake in, how many times does that have to happen before it stops reading like a coincidence?

What are your thoughts?

What Congress (House + Senate) disclosed over the past two weeks. Leaned clearly long:

• 🟢 Buys: ~$11.1M (384 trades)
• 🔴 Sells: ~$6.85M (197 trades)
• Overall buy:sell ratio ≈ 1.6 : 1 — and semiconductors were everywhere.

📊 Top 15 tickers — volume, avg trade-date price & buy:sell ratio

Ticker	Buy vol	Avg buy	Sell vol	Avg sell	Buy : Sell
Micron (MU)	$849K (5)	$788.96	$1.15M (5)	$821.14	1 : 1.4 (sell)
Microchip (MCHP)	$1.21M (9)	$90.15	$33K (1)	$93.43	37 : 1 (buy)
Texas Instruments (TXN)	$549K (6)	$303.37	—	—	buy only
Microsoft (MSFT)	$288K (10)	$421.51	$164K (6)	$414.50	1.8 : 1 (buy)
Molina Health (MOH)	$375K (1)	$181.26	—	—	buy only
Farmer Mac (AGM)	$375K (1)	$173.31	—	—	buy only
Nvidia (NVDA)	$330K (10)	$220.22	$32K (4)	$216.89	10 : 1 (buy)
Walmart (WMT)	$300K (4)	$126.56	$33K (1)	$131.60	9 : 1 (buy)
Lennar (LEN)	$225K (3)	$90.58	—	—	buy only
Exelon (EXC)	$116K (3)	$46.23	$75K (1)	$46.23	1.5 : 1 (buy)
Apple (AAPL)	—	—	$183K (2)	$288.03	sell only
VirnetX (VHC)	$75K (1)	$13.41	$108K (2)	$15.17	1 : 1.4 (sell)
Vista Gold (VGZ)	—	—	$175K (1)	$2.29	sell only
Parker Hannifin (PH)	—	—	$175K (1)	$859.44	sell only
Novartis (NVS)	—	—	$175K (1)	$152.01	sell only

🔑 Takeaways

• Chips dominated both sides. Microchip ($1.2M, 37:1 buy), Texas Instruments ($549K, all buys), and Nvidia ($330K, 10:1 buy) led the accumulation.
• Micron was the exception — and the biggest name overall. ~$1.15M sold vs ~$849K bought (1:1.4 sell). Members were taking profits on its insane run — MU went from ~$680 to ~$970 across the month, and they were selling around $821.
• NVDA buyers chased strength: avg buy $220 > avg sell $217 — adding into the highs.
• The sell side was a blue-chip trim: Apple ($183K, no buys), plus single $175K clips of Novartis, Parker Hannifin, and Vista Gold — much of it Rep. Michael McCaul's methodical selldown.

What do you think of this recent activity?

The NYSE is a private actor (publicly traded but not a public entity or a regulated utility) that markets itself as a public marketplace. Yet it charges $8.4k per month for real-time data access, plus $78 per professional user monthly. You can build algorithmic trading systems through Robinhood or Schwab APIs, but you can’t feed them real-time NYSE order flow because the NYSE owns that data and charges massive fees to access it. A developer trading their own money on their own brokerage still can’t programmatically access the market data they need to compete.

When analysis was the bottleneck, data gatekeeping didn’t matter. Now that frontier AI commoditized analytical tools, controlling data access is the only competitive advantage. When retail traders and developers can’t access real-time order flow, prices don’t reflect true value and volatility increases. The regulatory battleground isn’t AI tools anymore. It’s data access. Public markets need public, affordable, real-time data endpoints. Otherwise you’re not running price discovery, you’re running a lottery where entry fee determines odds.

If access to this data doesn’t provide an advantage, then why do people and institutions pay for it?

Over $620 million in new federal awards across 96 recipients in the past month. Here's the top ones by dollar value from USASpending data:

1. General Dynamics (GD): Multiple multimillion-dollar IT and operations contracts
2. CGI Inc. (GIB): Several large IT and consulting wins

Defense, aerospace, and engineering names continue to stack federal work. Recent prices (as of June 12 close): TXT at $92.82, GD at $360.22, KBR at $35.86, GIB at $66.20.

Anyone tracking these for overlaps with congressional trades or lobbying?

With GDP around 1.6%, unemployment holding at 4.3% and CPI ticking up through mid-2026, the macro setup feels reminiscent of 2023's post-hike stabilization phase. I pulled recent data on congressional trades to compare sector activity year-over-year and see if sentiment lines up.

Key observations from PTRs and portfolio simulations on Armed Services-connected members and peers:
Technology/semiconductors stand out. One senator's backtested holdings show heavy weight in chip names tied to AI and defense tech. Lifetime trades top 1,100, with notable concentrations despite CAGR around 4.6% (below broader politician average of ~7.8% and SPY).
Defense overlaps appear repeatedly — sales in contractors like LMT flagged near legislation and federal contract events, with some high confluence on campaign signals too.

Crypto picked up in House filings (repeated trades in ETH-linked and related assets during 2025 spikes).
Healthcare and financials featured in sells for other active traders, several with strong earnings-timing stats (one at nearly 90% beat rate on pre-earnings moves).

2025 had distinct clusters (4-5 trades in short windows, elevated z-scores), while 2026 filings lean more toward position trimming. This mirrors 2023 behavior where uncertainty drove defensives and select tech bets. Full portfolios range $200k to $10M+ in these examples, with win rates 52-61% and varying Sharpe (0.6-1.7).

This is a narrow sample from public disclosures — not investment advice, just observable patterns in the data. Timing relative to bills or awards raises interesting questions but filings are the only hard evidence. Sources are senate/house disclosure portals.

What do you think — is committee oversight bleeding into portfolios, or just coincidence in similar macro years? Would love links to other analyses.

Gov share: $15.2B lifetime / 254 awards, but only ~$1.5–2B/yr lately vs ~$18.7B FY25 revenue → gov is ~low-teens % and shrinking (Starlink is the majority now). It's the profitable backbone, not the growth story.

The gov book:

• DoD military launch (NSSL) — 31% — recurring, the moat
• NASA Commercial Crew — 23% — the reliability brand
• NASA Artemis lunar lander — 20% — Starship-dependent, behind schedule
• Civil agencies — 23% — low-stakes
• Starshield (mil-Starlink) — 3% — small, fastest-growing

Most critical: NSSL + Commercial Crew.

Highest-risk: Artemis HLS (fixed-price, hardest tech) and Crew (a crewed mishap, not cost).

In April 2026, Bryan Catanzaro, vice president of applied deep learning at Nvidia, said something that shouldn’t have been controversial but absolutely was: for his team, the cost of compute is far beyond the cost of the employees.

That sentence should have ended the conversation about AI replacing human workers. It didn’t. Instead, companies like Meta, Microsoft, and Uber have doubled down, firing thousands of people to cut costs, then spending multiples more on AI infrastructure than they saved. Uber reportedly burned through its full year AI budget in 4 months. We’re watching a trillion dollar industry bet everything on a technology that, for most use cases, costs more than the thing it’s supposed to replace. And nobody’s really talking about what happens when the market figures that out.

The Numbers Tell a Story of Desperation

OpenAI reportedly spent over $5 billion on compute against roughly $4.9 billion in revenue in a recent fiscal period. They’re essentially breaking even on infrastructure before you account for salaries, rent, or R&D. Anthropic is valued near $1 trillion. Neither company is profitable.

And the pricing ceiling is real. If you raise API costs or subscription fees much higher, you hit the wage floor where hiring a human just makes more economic sense. That ceiling isn’t theoretical. It’s the structural limit on every AI company’s revenue model, and it varies by role and geography. A junior developer in San Francisco, a support rep in Manila, a content writer in Austin. Each one represents a different price cap the AI vendor cannot exceed for that function.

Where the Ceiling Actually Sits

A 2024 MIT study analyzed the economics of AI automation across job categories and found something striking: AI automation was economically viable in only 23% of roles studied. In the remaining 77%, the total cost of implementation, maintenance, and compute significantly exceeded human wages.

Run the math yourself. A junior developer in San Francisco costs roughly $100K to $150K annually, fully loaded. Heavy agentic API workloads for equivalent output, once you factor in prompt engineering, guardrails, error correction, and rework, can run $15K to $20K per month at scale. That’s $180K to $240K per year. You’re already above the human salary floor, and you haven’t hired anyone.
This is showing up in real budgets right now. IT departments are reporting AI spend that exceeds the salaries of the teams using it. Companies that cut headcount to fund AI adoption are discovering the replacement costs more than the people did.

The Pricing Shell Game.

Look at the pricing trajectory since these tools launched. Early free tiers gave way to $20/month subscriptions. API pricing has been restructured repeatedly across model generations. On paper, some per token prices have dropped. Claude Opus went from $15 per million input tokens to $5 across generations.

But the headline price drop is misleading. Newer tokenizers can use up to 35% more tokens for the same text. Usage based billing changes, feature level charges, and cache pricing add layers that make true cost comparison nearly impossible. The effective cost per unit of work has not fallen the way the sticker price suggests.
The pattern is clear: these companies are experimenting with pricing architecture because they haven’t found a model that works. They can’t raise prices enough to be profitable. They can’t lower them enough to escape the comparison against simply hiring someone.

“But Moore’s Law Will Fix It”

Some will argue falling compute costs save the model. Chips get cheaper, margins compress, volume makes up the difference. That’s technically true and it misses the capital problem entirely.

Infrastructure doesn’t decline to zero cost. Hyperscalers spent over $400 billion on data center buildout in 2025, with 2026 projections pushing toward $600 billion. That’s upfront capex that has to be financed through retained earnings, bank debt, or equity raises.

Here’s the problem: why would a bank finance, or investors buy equity in, assets they know will be obsolete or deeply depreciated in 2 to 4 years? If your newest GPU cluster is outdated by 2028, the debt servicing doesn’t disappear with it. And you can’t just stop upgrading. You have to keep buying faster hardware to stay competitive. So you issue more equity, take on more debt, and repeat. It’s a cycle of returning to investors and lenders to fund equipment that depreciates faster than it generates returns.Moore’s Law doesn’t solve that. It guarantees it.

The Valuation Math Doesn’t Close

Here’s where it breaks down for investors. Industry analysis suggests that if current costs and pricing held, AI companies would need close to $2 trillion in annual revenue by 2029 to justify the capital already poured into data centers. For context, that’s more than the combined annual revenue of Google, Microsoft, and Amazon.
That market doesn’t exist yet. And if it ever did, the pricing power to capture it wouldn’t, because the human salary ceiling kicks in first. Every dollar of price increase pushes more customers back toward hiring people.
You can verify the spending side yourself. Google discloses its capex in SEC filings and earnings calls. Microsoft, Nvidia, and the other public infrastructure players all report the buildout numbers. The spending is documented. The revenue that justifies it is projected.
So either these companies find a way to reduce infrastructure costs dramatically, they accept far lower margins than their valuations imply, or the market recalibrates. None of those outcomes supports a $1 trillion valuation under current business models.

So Here’s What I’m Asking

Where is the ceiling for your work? If Claude or GPT pricing doubled tomorrow, would your company keep paying, or start interviewing?

At what point do investors stop accepting growth narratives and start demanding profitability?

And does anyone seriously believe a company can sustain a trillion dollar valuation selling a product that gets less competitive every time they raise the price?

Curious what this community thinks, especially those of you running real workloads through the API. Your usage bills are the data point that settles this.

In April 2026, Bryan Catanzaro, vice president of applied deep learning at Nvidia, said something that shouldn’t have been controversial but absolutely was: for his team, the cost of compute is far beyond the cost of the employees.

That sentence should have ended the conversation about AI replacing human workers. It didn’t. Instead, companies like Meta, Microsoft, and Uber have doubled down, firing thousands of people to cut costs, then spending multiples more on AI infrastructure than they saved. Uber reportedly burned through its full year AI budget in 4 months. We’re watching a trillion dollar industry bet everything on a technology that, for most use cases, costs more than the thing it’s supposed to replace. And nobody’s really talking about what happens when the market figures that out.

The Numbers Tell a Story of Desperation

OpenAI reportedly spent over $5 billion on compute against roughly $4.9 billion in revenue in a recent fiscal period. They’re essentially breaking even on infrastructure before you account for salaries, rent, or R&D. Anthropic is valued near $1 trillion. Neither company is profitable.

And the pricing ceiling is real. If you raise API costs or subscription fees much higher, you hit the wage floor where hiring a human just makes more economic sense. That ceiling isn’t theoretical. It’s the structural limit on every AI company’s revenue model, and it varies by role and geography. A junior developer in San Francisco, a support rep in Manila, a content writer in Austin. Each one represents a different price cap the AI vendor cannot exceed for that function.

Where the Ceiling Actually Sits

A 2024 MIT study analyzed the economics of AI automation across job categories and found something striking: AI automation was economically viable in only 23% of roles studied. In the remaining 77%, the total cost of implementation, maintenance, and compute significantly exceeded human wages.

Run the math yourself. A junior developer in San Francisco costs roughly $80K to $120K annually, fully loaded. Heavy agentic API workloads for equivalent output, once you factor in prompt engineering, guardrails, error correction, and rework, can run $15K to $20K per month at scale. That’s $180K to $240K per year. You’re already above the human salary floor, and you haven’t hired anyone.
This is showing up in real budgets right now. IT departments are reporting AI spend that exceeds the salaries of the teams using it. Companies that cut headcount to fund AI adoption are discovering the replacement costs more than the people did.

The Pricing Shell Game.

Look at the pricing trajectory since these tools launched. Early free tiers gave way to $20/month subscriptions. API pricing has been restructured repeatedly across model generations. On paper, some per token prices have dropped. Claude Opus went from $15 per million input tokens to $5 across generations.

But the headline price drop is misleading. Newer tokenizers can use up to 35% more tokens for the same text. Usage based billing changes, feature level charges, and cache pricing add layers that make true cost comparison nearly impossible. The effective cost per unit of work has not fallen the way the sticker price suggests.
The pattern is clear: these companies are experimenting with pricing architecture because they haven’t found a model that works. They can’t raise prices enough to be profitable. They can’t lower them enough to escape the comparison against simply hiring someone.

“But Moore’s Law Will Fix It”

Some will argue falling compute costs save the model. Chips get cheaper, margins compress, volume makes up the difference. That’s technically true and it misses the capital problem entirely.

Infrastructure doesn’t decline to zero cost. Hyperscalers spent over $400 billion on data center buildout in 2025, with 2026 projections pushing toward $600 billion. That’s upfront capex that has to be financed through retained earnings, bank debt, or equity raises.

Here’s the problem: why would a bank finance, or investors buy equity in, assets they know will be obsolete or deeply depreciated in 2 to 4 years? If your newest GPU cluster is outdated by 2028, the debt servicing doesn’t disappear with it. And you can’t just stop upgrading. You have to keep buying faster hardware to stay competitive. So you issue more equity, take on more debt, and repeat. It’s a cycle of returning to investors and lenders to fund equipment that depreciates faster than it generates returns.Moore’s Law doesn’t solve that. It guarantees it.

The Valuation Math Doesn’t Close

Here’s where it breaks down for investors. Industry analysis suggests that if current costs and pricing held, AI companies would need close to $2 trillion in annual revenue by 2029 to justify the capital already poured into data centers. For context, that’s more than the combined annual revenue of Google, Microsoft, and Amazon.
That market doesn’t exist yet. And if it ever did, the pricing power to capture it wouldn’t, because the human salary ceiling kicks in first. Every dollar of price increase pushes more customers back toward hiring people.
You can verify the spending side yourself. Google discloses its capex in SEC filings and earnings calls. Microsoft, Nvidia, and the other public infrastructure players all report the buildout numbers. The spending is documented. The revenue that justifies it is projected.
So either these companies find a way to reduce infrastructure costs dramatically, they accept far lower margins than their valuations imply, or the market recalibrates. None of those outcomes supports a $1 trillion valuation under current business models.

So Here’s What I’m Asking

Where is the ceiling for your work? If Claude or GPT pricing doubled tomorrow, would your company keep paying, or start interviewing?

At what point do investors stop accepting growth narratives and start demanding profitability?

And does anyone seriously believe a company can sustain a trillion dollar valuation selling a product that gets less competitive every time they raise the price?

Curious what this community thinks, especially those of you running real workloads through the API. Your usage bills are the data point that settles this.

In April 2026, Bryan Catanzaro, vice president of applied deep learning at Nvidia, said something that shouldn’t have been controversial but absolutely was: for his team, the cost of compute is far beyond the cost of the employees.

That sentence should have ended the conversation about AI replacing human workers. It didn’t. Instead, companies like Meta, Microsoft, and Uber have doubled down, firing thousands of people to cut costs, then spending multiples more on AI infrastructure than they saved. Uber reportedly burned through its full year AI budget in 4 months. We’re watching a trillion dollar industry bet everything on a technology that, for most use cases, costs more than the thing it’s supposed to replace. And nobody’s really talking about what happens when the market figures that out.

The Numbers Tell a Story of Desperation

OpenAI reportedly spent over $5 billion on compute against roughly $4.9 billion in revenue in a recent fiscal period. They’re essentially breaking even on infrastructure before you account for salaries, rent, or R&D. Anthropic is valued near $1 trillion. Neither company is profitable.

And the pricing ceiling is real. If you raise API costs or subscription fees much higher, you hit the wage floor where hiring a human just makes more economic sense. That ceiling isn’t theoretical. It’s the structural limit on every AI company’s revenue model, and it varies by role and geography. A junior developer in San Francisco, a support rep in Manila, a content writer in Austin. Each one represents a different price cap the AI vendor cannot exceed for that function.

Where the Ceiling Actually Sits

A 2024 MIT study analyzed the economics of AI automation across job categories and found something striking: AI automation was economically viable in only 23% of roles studied. In the remaining 77%, the total cost of implementation, maintenance, and compute significantly exceeded human wages.

Run the math yourself. A junior developer in San Francisco costs roughly $80K to $120K annually, fully loaded. Heavy agentic API workloads for equivalent output, once you factor in prompt engineering, guardrails, error correction, and rework, can run $15K to $20K per month at scale. That’s $180K to $240K per year. You’re already above the human salary floor, and you haven’t hired anyone.
This is showing up in real budgets right now. IT departments are reporting AI spend that exceeds the salaries of the teams using it. Companies that cut headcount to fund AI adoption are discovering the replacement costs more than the people did.

The Pricing Shell Game.

Look at the pricing trajectory since these tools launched. Early free tiers gave way to $20/month subscriptions. API pricing has been restructured repeatedly across model generations. On paper, some per token prices have dropped. Claude Opus went from $15 per million input tokens to $5 across generations.

But the headline price drop is misleading. Newer tokenizers can use up to 35% more tokens for the same text. Usage based billing changes, feature level charges, and cache pricing add layers that make true cost comparison nearly impossible. The effective cost per unit of work has not fallen the way the sticker price suggests.
The pattern is clear: these companies are experimenting with pricing architecture because they haven’t found a model that works. They can’t raise prices enough to be profitable. They can’t lower them enough to escape the comparison against simply hiring someone.

“But Moore’s Law Will Fix It”

Some will argue falling compute costs save the model. Chips get cheaper, margins compress, volume makes up the difference. That’s technically true and it misses the capital problem entirely.

Infrastructure doesn’t decline to zero cost. Hyperscalers spent over $400 billion on data center buildout in 2025, with 2026 projections pushing toward $600 billion. That’s upfront capex that has to be financed through retained earnings, bank debt, or equity raises.

Here’s the problem: why would a bank finance, or investors buy equity in, assets they know will be obsolete or deeply depreciated in 2 to 4 years? If your newest GPU cluster is outdated by 2028, the debt servicing doesn’t disappear with it. And you can’t just stop upgrading. You have to keep buying faster hardware to stay competitive. So you issue more equity, take on more debt, and repeat. It’s a cycle of returning to investors and lenders to fund equipment that depreciates faster than it generates returns.Moore’s Law doesn’t solve that. It guarantees it.

The Valuation Math Doesn’t Close

Here’s where it breaks down for investors. Industry analysis suggests that if current costs and pricing held, AI companies would need close to $2 trillion in annual revenue by 2029 to justify the capital already poured into data centers. For context, that’s more than the combined annual revenue of Google, Microsoft, and Amazon.
That market doesn’t exist yet. And if it ever did, the pricing power to capture it wouldn’t, because the human salary ceiling kicks in first. Every dollar of price increase pushes more customers back toward hiring people.
You can verify the spending side yourself. Google discloses its capex in SEC filings and earnings calls. Microsoft, Nvidia, and the other public infrastructure players all report the buildout numbers. The spending is documented. The revenue that justifies it is projected.
So either these companies find a way to reduce infrastructure costs dramatically, they accept far lower margins than their valuations imply, or the market recalibrates. None of those outcomes supports a $1 trillion valuation under current business models.

So Here’s What I’m Asking

Where is the ceiling for your work? If Claude or GPT pricing doubled tomorrow, would your company keep paying, or start interviewing?

At what point do investors stop accepting growth narratives and start demanding profitability?

And does anyone seriously believe a company can sustain a trillion dollar valuation selling a product that gets less competitive every time they raise the price?

Curious what this community thinks, especially those of you running real workloads through the API. Your usage bills are the data point that settles this.

Three of the biggest private companies on earth are all hitting the public markets in the same window, and it's worth talking through the actual play rather than the hype. I pulled the primary filings so we're arguing over real numbers, not headlines.

SpaceX — the only one you can actually read. Public S-1 is live on SEC EDGAR. It prices after the close tonight (June 11) and is expected to start trading tomorrow on Nasdaq under "SPCX." Reported targets: ~$135/share, ~$1.75T valuation, ~$75B raise — which would be the largest IPO in history. The financials in the filing are the real story: FY2025 revenue $18.67B with a $2.59B operating loss; Q1 2026 revenue $4.69B. Read the actual risk factors and the Starlink-vs-launch revenue split before you form an opinion. (Link in comments.)

OpenAI — confidential, no public S-1 yet. Confidentially filed with the SEC on June 9, reportedly targeting up to a $1T valuation, debut possibly September. Note they restructured into a public benefit corp (OpenAI Group PBC) last October — that's the real primary source you can read today. There is no public prospectus yet, so anyone "quoting the OpenAI S-1" is making it up.

Anthropic — also confidential. Confidentially filed ~June 1, also potentially trillion-dollar range, reportedly $47B revenue run-rate and projecting break-even by 2028 (ahead of OpenAI's 2030). Same caveat: no public filing to read yet.

The questions I'd actually like discussed:

1. SpaceX is a profitless-on-operations $1.75T launch+telecom company. At ~$135, are you a buyer day one, or waiting for the post-lockup reset?
2. OpenAI and Anthropic are both burning cash toward trillion-dollar valuations. Does the AI cohort hold up if SPCX trades badly tomorrow?
3. SpaceX is one of the largest national-security / DoD contractors out there (NSSL launches, Starshield). Once it's a public ticker, it lands squarely in the same tech-and-defense lane that Congress already trades constantly.

On that last point, Technology has been the single most-traded sector by members of Congress over the trailing 12 months: 1,755 disclosed trades, more than Healthcare or Financials. NVIDIA alone shows up in ~133 disclosed trades this year, and the defense primes are active too (PLTR, LMT, RTX, NOC, GD). The interesting wrinkle with these IPOs: they convert three of the biggest untrackable private AI/defense names into public tickers — meaning the moment a member buys SPCX, it shows up in a disclosure. Worth watching who files first.

Primary sources for your own research in the comments. What's everyone's actual play here?

Laying this one out as a clean timeline, because the facts do the work.

9:37 a.m. ET, April 9, 2025 — with markets sliding, Trump posts on Truth Social: "THIS IS A GREAT TIME TO BUY!!! DJT"

1:18 p.m. ET that same day — less than four hours later — his administration announces a 90-day pause on most "reciprocal" tariffs.

The close: the S&P 500 finished +9.52% — its biggest single-day gain since 2008. Nasdaq +12.2%, Dow +2,962 points. Anyone who bought at 9:37 like he suggested was up big by the bell.

And per his own financial disclosures, the president's trust holds tens of millions in S&P 500 and broad-index ETFs — a standing position that rose right along with the rally.

Being fair, because it matters:

• It was a public post — not inside information. Anyone could have acted on it.
• His holdings are in a discretionary trust run by trustees; there's no evidence he personally traded around this.
• Legal experts have said he's unlikely to face charges.
• It was notable enough that four senators (Schumer, Wyden, Warren, Schiff) asked attorneys general to investigate possible manipulation — but that's a request, not a finding.

I'm not alleging a crime. The point is the conflict of interest: the one person who can move the entire market with an announcement is also publicly telling people when to buy — while personally holding the market.

It's all in the public record. You don't need an app — read his OGE disclosures and the timeline yourself.