#OpenAIAnthropicRace
About OpenAIAnthropicRace
OpenAI's annualized revenue topped $40B, about double end-2025, driven by AI coding, subscriptions and new businesses; it also changed its revenue chief pre-IPO. Investor materials put Anthropic's preliminary Q2 revenue above $11.5B, over twice Q1's $4.73B, with positive adjusted operating profit. Its latest round valued it at $965B, while investors discuss an IPO above $2T. Can growth and profits cover compute costs, and will its listing reset AI-chip, data-center and tech-stock valuations?
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The AI race is no longer just about who has the smartest model—it’s increasingly about who controls the physical infrastructure behind the boom.
$SKHY is committing roughly $38B toward two new memory plants as AI chip demand continues to outpace supply. The stock also received a boost from reports that Singapore’s Temasek may pursue a direct investment, highlighting how serious capital is flowing into real semiconductor capacity rather than just chasing AI narratives.
At the model layer, competition is getting even tougher. OpenAI and Anthropic have been cutting flagship-model prices as lower-cost Chinese rivals attract more price-sensitive enterprise customers.
AI is quickly becoming both an infrastructure race and a margin war. And with Anthropic reportedly preparing for potential investors ahead of a possible public listing later this year, the competition is only getting more intense.
#WeakConsumptionFedSplit
#OpenAIAnthropicRace
#SKHynixCapexSurge
Anthropic raised 65 billion in its Series H round at the end of May, with a valuation of 965 billion, surpassing OpenAI's 852 billion; OpenAI's valuation was set at 122 billion in the round at the end of March. Both are eyeing an IPO in the fall, targeting a trillion-dollar valuation.
Anthropic's "surpass" isn't that solid. The high valuation is
What really matters is where the money is being spent and whether it can be recouped.#WeakConsumptionFedSplit #OpenAIAnthropicRace #SKHynixCapexSurge
🤖 OPENAI vs ANTHROPIC: WHY CRYPTO TRADERS SHOULD CARE
The AI race is becoming much bigger than a competition between chatbots.
OpenAI and Anthropic are increasingly competing for the same scarce resources:
Compute. Chips. Memory. Data centers. Capital.
That creates a much broader investment chain.
AI model demand → data centers → GPUs → high-bandwidth memory → networking → power infrastructure.
And that is where the story starts crossing into markets far beyond AI software.
Anthropic has also been drawing enormous institutional attention, while the broader AI infrastructure trade continues influencing semiconductor and technology valuations.
For crypto traders, the takeaway is simple:
Narratives don't always stay inside one sector.
Capital can move from AI equities → infrastructure → semiconductors → risk assets → crypto.
The question isn't just who wins the AI race.
It's:
Where does the capital flow next? 👀
#OpenAIAnthropicRace #WeakConsumptionFedSplit
🚨 THE AI RACE MAY COME DOWN TO ONE THING: COMPUTE COST.
The biggest difference between OpenAI / Anthropic and xAI / Google isn’t just model performance.
It’s infrastructure.
1️⃣ Compute ownership
OpenAI and Anthropic rely heavily on hyperscalers for compute.
Google and xAI have much more direct control over their infrastructure.
2️⃣ Capital structure
OpenAI and Anthropic have received massive strategic investments from major tech companies. Google and xAI operate from a different infrastructure and capital position.
And that could create a major advantage in price competition.
For years, premium pricing could be justified by better AI performance.
But as the performance gap narrows, cost becomes increasingly important.
If two models deliver similar results, users and businesses will naturally ask:
💰 Why pay more?
That creates a difficult challenge for companies carrying enormous compute costs.
The key question is whether OpenAI and Anthropic can maintain a meaningful lead through the next generation of models.
If they can’t, the AI race could shift from:
“Who has the smartest model?”
to:
“Who can deliver intelligence at the lowest cost?”
And that could reshape the entire AI industry. 🤖⚡
#SP500Nears8000
#CPIPPIEaseFedSplit
#SandiskLongTermTargets
The difference between OpenAI/Anthropic and xAI/Google:
1. OpenAI and Anthropic rent compute from hyperscalers. xAI and Google own their data centers.
2. Most of OpenAI and Anthropic's equity comes from hyperscaler capital. The other two aren't built on that model.
This creates a massive gap in price competition.
Up until now, they justified high prices with superior performance. But that performance gap has narrowed, and because of those structural costs, they can't offer competitive pricing anymore.
Unless they pull ahead again with a massive breakthrough, OpenAI and Anthropic will eventually get acquired by their investors, Amazon and Microsoft.
The frontier AI labs are tracking toward a COMBINED $200B+ revenue run rate by year end
These are some of the fastest growing companies in the history of business, and their revenue is what keeps the entire AI infra bull market alive (so pay attention!)
And Grok/Cursor is now entering the big leagues along with Anthropic and OpenAI it seems
Here's the numbers:
Anthropic is nearing $80B RR, up from $10B back in January (based on @tickerplus data) They are expected to reach $100-$120B by EOY
OpenAI recently surpassed $40B up from about $21B in January, however their growth is currently accelerating, jumping $10B from May to July. At this rate they should hit $60B-$70B by EOY
Then we have Grok/Cursor. Cursor reportedly hit a $4B run rate in early June, up from $2B in February. xAI was also targeting $2B from Grok this year. So call it $6B+ between them and compounding after their release of Grok Bot and Grok 4.6. I expect them to be over $10B by the end of the year combined
So currently, between the 3 we are sitting at $126B in revenue run rate and an expected combined RR of around $200B by the end of the year (assuming things continue as they are)
of course, these are not yet confirmed numbers as Anthropic and OpenAI are not yet public and SpaceX didn't separate grok/cursor numbers in their Q2 report as the Cursor deal still isn't closed
But next quarter we'll get those numbers from SpaceX and Anthropic is looking to go public in October latest, so we should have more clarity soon
But the more revenue these companies make, the more compute demand there will be. And if we start to factor in that models are getting more efficient to create, we should soon start to see profit from these companies too
I shared below how good the margins are per 1GW for these frontier models and it's only going to get better (assuming compute prices don't keep accelerating)
While we can't yet invest in the labs companies, we can invest in the companies enabling their products. This is the AI infra companies providing compute, data centres, power, memory and more. So long as these revenues continue higher, the infra trade will too!
If this was helpful, my company provides a service where 5 top-tier analysts share their market analysis and real-time portfolios so you can see exactly which AI infra names we own. It's inside Milk Road PRO and just $1 to try (insane price just to check it out). You can learn more here:
Thanks for reading!
Kyle Reidhead | Milk Road
Why would anyone pay SpaceX or Nebius $30-$50B/year/1GW of compute?
Because OpenAI and Anthropic can generate $100B+ per gigawatt per year selling inference API!
Here's how that's possible, and why it's sustainable (save this)
First, what are they actually selling? Every ChatGPT answer, every Cursor autocomplete, every enterprise copilot runs on "inference", the model generating tokens. The labs sell those tokens through subscriptions or an API, metered like electricity
@SemiAnalysis showed their model: run one gigawatt of Nvidia GB300 compute selling tokens at posted API prices and it generates over $100 BILLION a year of revenue. That same gigawatt costs roughly $12B-$50B/year to rent out
A 2-8x spread between what compute costs and what intelligence sells for
So why can they charge that much? Because the customer isn't comparing token prices to compute prices. They're comparing tokens to LABOR
A few dollars of tokens replaces work that costs hundreds of dollars an hour. Legal review, sales ops, financial close, code. That's why enterprise agent adoption is up 20x to 108x across job functions in just five months. At today's prices the buyer's ROI already fantastic
Why it's sustainable:
1. Demand compounds faster than prices fall. Token prices drop constantly, but agents burn dramatically more tokens per task than chatbots ever did, and every job function is adopting at once. Falling price x exploding volume = growing revenue
2. Supply is rationed. A handful of frontier labs, and none of them have enough compute. When you're capacity constrained you serve the highest-value demand first and pricing holds
3. The buyers keep paying UP, not down. Microsoft sells this same inference through Azure and Copilot. Nebius just disclosed its first deal at $40-50M per megawatt, the top of its own range. Nobody negotiates prices higher on a product that's about to be oversupplied
This is why the "AI capex bubble" framing keeps missing. The $100B at the top of the stack is what pays the $30-50B compute deals, which pay the datacenters, the chips, the memory, the power. The most profitable product in tech is funding everything below it
And you don't need to own the private labs to win. Every dollar of inference revenue flows down through the infra stack, and that's exactly where I'm positioned (compute, memory, power)
If this was helpful, my company provides a service where 5 top-tier analysts share their market analysis and real-time portfolios so you can see exactly how we're positioned across this stack. It's inside Milk Road PRO and just $1 to try (insane price just to check it out). Learn more here:
Follow me @kylereidhead for more insights on AI, robotics and markets!
🚀 AI infrastructure is entering a new phase.
Strong revenue growth alone is no longer enough—investors now want proof that massive AI investment is translating into sustainable profits.
The latest numbers remain impressive:
NVIDIA: $81.6B in revenue (+85% YoY), with Data Center revenue reaching $75.2B (+92% YoY).
AMD: Data Center revenue climbed to $16.6B (+32%), driven by strong demand for EPYC CPUs and Instinct AI accelerators.
But the AI ecosystem is now much broader than GPUs.
It includes:
AI accelerators & GPUs
CPUs
Networking
Optical connectivity
Memory
Cooling systems
Power infrastructure
Data-center construction
The next challenge is no longer just making faster chips—it's building the infrastructure around them.
As earnings season continues, I'm focused on three key areas:
1️⃣ Revenue conversion – Are AI orders becoming real revenue?
2️⃣ Capex efficiency – How much investment is needed to generate each additional dollar of AI revenue?
3️⃣ Customer concentration – What happens if a handful of hyperscalers reduce their AI spending?
The AI infrastructure story remains compelling, but the market is becoming more selective.
The next big question isn't who spends the most on AI—it's who generates the strongest returns from that spending.
#OKXOrbitTopics #OKXTraderVoices
$XNVDA $NVDA $AMD
#AIInfraEarningsWatch #CPIPPIEaseFedSplit #SpaceX99%ValueFromAI
#AIInfraEarningsWatch # AI Infra Earnings Watch: Can Spending Turn Into Profits?
The **#AIInfraEarningsWatch** narrative keeps attention on earnings across the companies supplying the infrastructure behind the artificial-intelligence boom. Investors are increasingly looking beyond headline revenue and asking whether enormous AI spending is producing sustainable returns.
The ecosystem spans GPUs, networking, memory, storage, cloud capacity, data centers, and power infrastructure. Companies such as **$NVDA**, **$AMD**, **$AVGO**, **$MU**, and **$TSM** provide different pieces of this supply chain, so their results can offer clues about where AI demand is strongest.
Capital expenditure is one of the most important indicators. Hyperscalers continue committing substantial resources to AI data centers, but investors want evidence that these investments can generate sufficient revenue and productivity gains. Strong cloud demand and rising AI-related bookings could reinforce the spending cycle.
Supply is another variable. Tight availability can support pricing and margins, while aggressive capacity expansion could eventually create pressure. Memory and semiconductor companies are particularly sensitive to this balance.
For traders following **#AIInfraEarningsWatch**, the key metrics are AI-related revenue, data-center growth, gross margins, backlog, capital expenditure, free cash flow, and management guidance.
High expectations create additional risk: even strong quarterly results may fail to satisfy investors if future guidance falls short of already-elevated forecasts.
Ultimately, earnings will help determine whether AI infrastructure remains a durable multi-year growth cycle or begins moving toward a more mature phase where spending and valuations normalize.
**$NVDA $AMD $AVGO $MU $TSM**
**#AIInfraEarningsWatch #AI #DataCenters #Semiconductors #TechStocks**
Tokenized Anthropic Rises to $1,800 Range, Implying $1.6T–$1.84T Valuation
Anthropic tokenized asset (ANTHROPICUSDT) listed on Binance Pre-IPO Perpetual Futures has seen active trading, with prices fluctuating between $1,600 and $1,842; based on the contract's estimated benchmark share count of 1 billion shares, the pre-market derivatives pricing implies a valuation of ~$1.6 trillion to $1.84 trillion for Anthropic, whereas in traditional private markets, Anthropic completed a $65 billion Series H round in May 2026 at a $965 billion post-money valuation, indicating a 65% to 88% premium in the crypto pre-market that aligns closely with Wall Street investment banks' $1.5 trillion to $2 trillion IPO valuation expectations.
