Speaker · Updated
Ara Kharazian: latest market views
Ramp
Summary
Ara Kharazian is the lead economist at Ramp, where he created the Ramp AI Index tracking business AI adoption from spend data, and he previously led economic research at Square.
Chorus has 15 views from Ara Kharazian across 2 episodes on Prof G Markets and ARK Invest (FYI podcast, In the Know video) since November 4, 2026. Their latest calls: bullish on Anthropic.
Where they stand
Their latest bullish or bearish call on each company.
- AnthropicBullish ·
What they talk about most
The latest views
Newest first. Every quote is checked against the episode's transcript.
Flat enterprise AI spend reflects a token price war, not weakening demand.
Ramp data shows AI spend has been roughly flat since August, even though firms use more models, spread spending across providers and use more tokens. Token prices have fallen sharply because of a price war between model providers. So spending is flat not because demand is weak but because each token now costs much less. He says that opens new questions for OpenAI and Anthropic about where growth comes from next.
“It's not a demand issue and firms are using more AI by pretty much any measure. They're using more models... but it's that those tokens have come down so significantly in price because of this price war that their actual spend has remained relatively flat.”at 26:14
Prof G Markets: The Trillion-Dollar Cover-Up: What Washington Knew About Jensen Huang ·
Chinese models hold under 5% of business AI spend, so the price war is not China-driven.
Ramp's dataset shows Chinese models have less than 5% of business AI spend. Ara says that even if all Chinese models were banned tomorrow, OpenAI and Anthropic would still face price competition from each other and from new American labs. The price fall is therefore driven by domestic competition and compute access, not by open-weight models flooding the market.
“Chinese models have less than 5% of the market as far as business spend goes.”at 27:36
Prof G Markets: The Trillion-Dollar Cover-Up: What Washington Knew About Jensen Huang ·
Compute-rich labs like SpaceX AI and Meta will soon turn the OpenAI–Anthropic duopoly into a four-way race.
SpaceX AI has a highly performing model that is popular with engineering teams and is gaining market share. It also has a compute advantage, since it owns many chips and rents them out to Anthropic. If OpenAI and Anthropic hit a funding crunch and cannot invest in compute, companies with compute already in hand will have the advantage. Meta is not a big player in business AI spend yet, but Ara expects it to follow the same path, which will intensify price competition.
“They're gonna expand their market share there. And what is today a two way race between [OpenAI] and [Anthropic] is very quickly gonna become a four way race between them and SpaceX AI, Meta, maybe some other players.”at 28:47
Prof G Markets: The Trillion-Dollar Cover-Up: What Washington Knew About Jensen Huang ·
Model companies' reported ARR is not truly recurring because enterprises can easily switch providers.
Model companies report ARR, which is recurring revenue annualised. But businesses can move their spend from one provider to another easily, and they are already finding ways to cut and control it. Market share can shift between companies within weeks, not months, as new frontier models pull demand from rivals. So Ara thinks it is hard to treat any of this spend as recurring, which will be a big challenge for model companies.
“It's also one of the funny things about how the model companies have been reporting ARR... But it's hard to think of any of the spend as recurring when businesses can so easily move it from one provider to another.”at 30:21
Prof G Markets: The Trillion-Dollar Cover-Up: What Washington Knew About Jensen Huang ·
Enterprise AI adoption & AI software
Engineering-led enterprise AI spend, the largest segment, is plateauing as teams optimise costs.
Engineering use is the largest segment of enterprise AI spend, and it is starting to plateau. Engineering teams are the ones now incentivised to spend on AI more efficiently, which is what Ramp hears from its customers. Whether AI spend as a whole flatlines depends on whether model companies can build commercially advanced versions that drive productivity gains for teams outside engineering.
“The largest segment of spend in terms of enterprise spend on AI, which is engineering use, is starting to plateau. Because those are precisely the teams that are incentivized now to figure out how to spend on AI more efficiently.”at 31:47
Prof G Markets: The Trillion-Dollar Cover-Up: What Washington Knew About Jensen Huang ·
Enterprise AI adoption & AI software
AI for customer support and cybersecurity could drive enterprise spend beyond engineering.
Customer support operations is the second-largest department for AI spend on Ramp's platform after engineering. Ara expects some spend to shift there and to accelerate growth. He also thinks AI used to defend against cyberattacks will be another accelerant. These are the most likely takers of growth outside engineering, though whether they can match the hyper-growth of coding remains an open question.
“AI for customer support operations, that's the second largest department in terms of spend on our platform that we can observe after engineering. You can imagine some spend is gonna be taken up by that.”at 32:29
Prof G Markets: The Trillion-Dollar Cover-Up: What Washington Knew About Jensen Huang ·
OpenAI and Anthropic revenue depends on VC-backed firms, so a VC pullback would hit them disproportionately.
Ramp data shows the top 1% of companies drive 80% of spend on OpenAI and Anthropic, and pretty much all of them are VC-backed, mainly in tech, finance and technical services. Concentration is fine in itself. It becomes a problem when the customers are highly correlated, as VC-backed firms are because their dollars move together regardless of company or use case. If spending slows, the effects would cascade through the stack and hit OpenAI and Anthropic's revenues disproportionately.
“Concentration in industry is fine. It's a problem when it's highly correlated, especially when it's something like VC where these dollars tend to move with each other no matter the company or its use case.”at 36:54
Prof G Markets: The Trillion-Dollar Cover-Up: What Washington Knew About Jensen Huang ·
AI trade's big risk is that OpenAI and Anthropic still depend on tech-sector customers; not yet proven otherwise.
Kharazian says one of the big risks to the AI trade today is that AI spending is concentrated among tech companies. If AI labs' spending has a disproportionate effect on markets, the damage can reach parts of the economy that don't touch AI. He says he would change his view if OpenAI and Anthropic develop a commercially advanced product that generates enough ROI outside the tech sector and becomes less reliant on tech companies. He treats that as an unproven 'if' that the labs have not yet demonstrated.
“if [OpenAI] and Anthropic are able to develop a commercially advanced product that drives sufficient ROI outside of the tech sector... then I would start to change my tune. I wouldn't worry about this at all necessarily. But that is an if, and it's something that they have not yet necessarily demonstrated.”at 37:36
Prof G Markets: The Trillion-Dollar Cover-Up: What Washington Knew About Jensen Huang ·
Enterprise AI adoption & AI software
Top 1% AI spenders are slowing spend even as their token usage keeps rising.
Ramp data shows the top 1% of AI-spending businesses, spending roughly $7,000 to $10,000 per employee per month, slowing from the hyper-growth of the first half of the year (September versus July, with early October data showing the same). Token volumes over the same period kept rising, and seasonality does not explain it. Ara attributes the slowdown to falling prices (AI cost almost halved over the window, so usage can double while token spend flatlines), and to top firms adding spend controls such as limits on frontier models for automated tasks, better defaults and routers. Ramp itself is a top 1% business and is starting to implement these controls. She says the slowdown is not a crash, but that it is observable in the top 1%.
“And it's not that they're using it less. Right? They actually are using it more based on all the indicators we have. They're using more tokens.”at 2:35
ARK Invest (FYI podcast, In the Know video): Ramp's Economist Found Cracks In The AI Thesis | The Brainstorm 156 ·
AI prices are falling fast from OpenAI-Anthropic competition and cheaper models, pushing down enterprise AI spend.
AI is getting cheaper because of intense competition between OpenAI and Anthropic, which are announcing price cuts and releasing more efficient models. Enterprises are also getting better deals, as both labs now offer large-enterprise contracts with new discounts. New high-performing models at standard and light tiers add further downward pressure. Over the roughly three-month window in question, the price of AI fell by almost half. Ara argues that competition is no longer just between OpenAI and Anthropic, so these price declines will continue and get stronger over time.
“AI is getting cheaper because there's so much competition between OpenAI and Anthropic and [stating] price cuts and also introducing more efficient models.”at 2:42
ARK Invest (FYI podcast, In the Know video): Ramp's Economist Found Cracks In The AI Thesis | The Brainstorm 156 ·
Meta PlatformsUncertain
Meta's enterprise AI entry should not be underrated, though it has no meaningful share yet.
Ramp data shows no significant Meta share in business AI today, because Meta only recently entered enterprise. Ara still argues that competition is underrated: new entrants have taken more than half the market within months, and Anthropic's rise took about a year. Business AI spend also lags consumer adoption, so Meta's consumer reach could help it win enterprise spend later.
“And I wouldn't underrate Meta either because part of what's so interesting about this market being a market for what is essentially b to b spend is that it's actually developed in many ways as a sort of lagging indicator of how the consumer market has developed.”at 14:17
ARK Invest (FYI podcast, In the Know video): Ramp's Economist Found Cracks In The AI Thesis | The Brainstorm 156 ·
AnthropicBullish
Anthropic is now the most-used business AI model, with consumer strength driving enterprise adoption.
Per Ramp data, Anthropic is now the most popular AI model among businesses and has the largest business spend. Its rise came over roughly a year. It lines up with the launch of Claude Code and the coding-agent wave, and with its pivot to consumer (Super Bowl ads, and a celebrity posting about using it), which was the season of its run-up. Ara argues that consumer product strength can drive enterprise adoption and spend, which suggests the consumer brand is feeding its enterprise share gains.
“I wouldn't underrate the extent to which consumer strength as a product can actually drive enterprise adoption and spend in this market.”at 15:16
ARK Invest (FYI podcast, In the Know video): Ramp's Economist Found Cracks In The AI Thesis | The Brainstorm 156 ·
AI demand is concentrated in VC-backed firms, so a slowdown there would cut AI spend in unison.
Ara agrees with Chamath that AI revenue is not circular but is a revenue quality problem. The top 1% of AI spenders on Ramp's platform are mostly VC-backed tech companies. That is fine while AI spend keeps growing and those companies keep growing their own customer bases. The concentration is also a correlation risk: if the AI trade slows, all of these firms would cut AI spend simultaneously, which could cascade across the sector.
“I do think he's actually right about it, that it's not circular revenue problem, but it is a revenue quality problem.”at 18:19
ARK Invest (FYI podcast, In the Know video): Ramp's Economist Found Cracks In The AI Thesis | The Brainstorm 156 ·
Enterprise AI adoption & AI software
Coding agents are the most commercially advanced AI, but median firms won't match engineers' value from them.
Coding agents are clearly productivity-enhancing for the tech sector and for the job category that uses them actively. Ara finds it hard to see a world in which the median business gets as much value from a coding agent as a typical software engineer at a tech company, so she does not expect the median firm to reach those levels. She acknowledges that new products may bring another coding-agent-style revolution, but argues that current expectations should be grounded in the data.
“At the same time, today, the most commercially advanced form of AI is coding agents, which are clearly productivity enhancing, at least for the tech sector and for that job category that uses them actively.”at 23:13
ARK Invest (FYI podcast, In the Know video): Ramp's Economist Found Cracks In The AI Thesis | The Brainstorm 156 ·
Enterprise AI adoption & AI software
Median firm AI spend per employee will fall under $1,000 per month within five years.
Ara estimates the median firm's AI spend per employee per month will be under $1,000 in five years, and says she is giving herself room. She benchmarks against the spend on a computer and argues that predictions of tens of thousands per employee per month are unrealistic. She expects AI to become table stakes, like Google Ads for a restaurant, so firms that adopt it gain an edge and then need it to compete. Median firms can get significant ROI without heavy spend, and AI use will extend beyond knowledge workers.
“I'll say for the median firm, it's probably gonna be easier for me. The median firm per employee under 1,000 per employee per month in five years.”at 29:05
ARK Invest (FYI podcast, In the Know video): Ramp's Economist Found Cracks In The AI Thesis | The Brainstorm 156 ·
Shows
- Prof G Markets · 1 episode
- ARK Invest (FYI podcast, In the Know video) · 1 episode
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