AI Spending Growth Slows as Competition Drives Down Prices

Exercise 1
Vocabulary
Repeat each word, its meaning and the example sentence out loud.
token spend
noun phrase/ˈtoʊkən spɛnd/the amount of money a company pays for artificial intelligence tokens, which represent units of AI processing
Our token spend increased by 30% this quarter because we rolled out AI coding features to all developers.
flatten
verb/ˈflætən/to stop increasing or decreasing; to become level or steady
The spending growth has flattened recently, which suggests we need to reconsider our AI budget.
headwind
noun/ˈhɛdwɪnd/a factor or force that slows progress or makes something harder to achieve
Rising competition is a major headwind for our plan to expand the AI product to new markets.
lock in
phrasal verb/lɑk ˈɪn/to secure a customer or agreement by making them committed, often through discounts or special terms
We offered enterprise discounts to lock in the biggest customers and prevent them from switching to competitors.
buyer's market
noun phrase/ˈbaɪ.ər̪z ˈmɑr.kɪt/a situation where there is more supply than demand, so customers have power and can negotiate for better prices
The AI software market has become a buyer's market, giving companies leverage to demand lower prices.
Exercise 2
Article
Read the article aloud on your own, or listen to it first.
AI Spending Growth Slows as Competition Drives Down Prices
Highlight any word to see what it means and translate it.
Spending on artificial intelligence stopped growing in late summer even though companies were using more AI than before.
Data from Ramp, a business spending platform, shows that token spend grew by only 1.5% between August and September. In July, it had jumped 42% from the previous month.
This sharp slowdown surprised many observers who expected AI spending to continue rising fast.
The slowdown matters because almost all AI spending comes from venture-backed technology companies that are growing quickly. If these companies slow down their spending at the same time, the entire AI industry suffers.
Currently, the top 1% of companies have reduced their spending slightly from their summer peaks, and that alone is enough to slow down total revenue across the market, even though companies are using more tokens overall.
Price competition is the main reason for this slowdown. OpenAI cut API prices in half for some of its models in September.
Anthropic responded by cutting prices for Fable and Opus models, a move that reduced its revenue growth by about $600 million, the company reported. Both companies also offered enterprise discounts to lock in major customers, but these discounts hurt their revenue targets.
Companies are also becoming smarter users of AI. As teams gain experience, they learn to use cheaper models instead of expensive ones. They set budgets, control which tasks can use AI, and route simple jobs to lightweight tools.
One example is Jev, a newer company that helps choose between different AI outputs more cheaply than traditional approaches.
Other competitors are adding pressure. Meta and Nvidia have introduced affordable enterprise offerings and open-source models.
Companies from China and the United States are also releasing cheaper alternatives. This abundance of options means it has become a buyer's market, where customers have the power in negotiations.
Some investors hope new use cases will drive growth again. AI coding emerged about 18 months ago and helped Anthropic and OpenAI become trillion-dollar companies.
Personal agents are now being released by OpenAI, Meta, and others. However, industry experts are not confident personal agents will provide the same boost as coding did.
Anthropic is preparing for an initial public offering, but the slowdown in AI spending is affecting its expected value. The company's valuation has fallen from around $2 trillion in early October to $1.2–1.3 trillion.
OpenAI also lowered its valuation from $1.4 trillion to $1.25 trillion and reduced its fundraising plans.
The fundamental question remains: companies can use more AI for less money, which is good for customers but challenging for AI providers who need to grow their revenue.
Exercise 3
Discussion
Have a discussion based on the following questions.
- 1.In your workplace, have you noticed spending on AI tools or services changing recently? Has your company cut costs or shifted to cheaper solutions?
- 2.Many companies are using more AI but paying less for it. Do you think this is sustainable, or will AI companies eventually need to raise prices again?
- 3.The article mentions that personal agents might be the next growth driver after AI coding. What new AI use cases do you think could drive significant spending in your industry?
- 4.Price wars between OpenAI and Anthropic have benefited customers, but hurt the companies' revenue. If you were leading one of these companies, how would you balance competitive pricing with profitability?
- 5.The data shows that the top 1% of companies have the most AI spending, but even they are slowing down. What factors might prevent most companies from ever reaching $10,000 per employee per month in AI spending?
Exercise 4
Further discussion
Have a discussion based on the following questions.
- 1.How might the slowdown in AI spending affect different parts of the tech industry—for example, chip makers, cloud providers, and AI software startups?
- 2.The article suggests that the model for AI growth might be fundamentally different from other tech platforms like Uber or Lyft. Why do you think price competition works differently in AI than it does in ride-hailing?
- 3.If new AI use cases do not emerge as hoped, what alternative business models could AI companies pursue to sustain growth and justify their high valuations?
- 4.How should investors think about the difference between 'usage growth' (more tokens being used) and 'revenue growth' (more money being spent)? Does one matter more than the other for long-term success?
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This lesson is our own retelling, for English learners, of a story from TechCrunch.
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