Virtually every company is touting artificial intelligence (AI) today.
Here’s the thing: there’s a major difference between “AI-powered” marketing speak and actual revenue hitting the bottom line.
With Big Tech collectively pouring hundreds of billions into AI infrastructure, investors need a framework to separate the substance from the spin.
In essence, the question is not whether AI is headed for a boom or bust.
The real question we should be asking is whether the AI revenue generated supports the generous spending.
Here are some tips to spot real AI revenue.
Sign #1: Look for Specific Dollar Figures
Vague claims like “AI is driving growth” or “we are seeing strong AI adoption” should raise red flags.
The companies making real money from AI should not be shy in sharing hard numbers.
Meta Platforms (NASDAQ: META) is an example to follow for AI revenue transparency.
In its recent earnings briefing, the social media giant disclosed that its AI-powered ad tools generate a US$60 billion annual run-rate.
That’s actual revenue flowing through its AI systems today.
Even better, Meta breaks it down further.
Reels alone hit a US$50 billion run-rate — remarkable for a feature that barely monetised three years ago.
Advantage+ shopping campaigns exceeded US$20 billion in annual run-rate.
These are specific, verifiable figures that investors can track.
When the next earnings report arrives, you can check the progression of these numbers.
Compare this to companies that speak in generalities.
If management can’t put a dollar figure on their AI revenue, they either do not know or do not want you to know. Neither is a good sign.
The best practice?
Listen for annual run-rates, growth percentages, and segment breakdowns.
If a company is vague about AI revenue but specific about everything else, that is a telling sign.
Sign #2: Distinguish Between AI Revenue and AI-Enhanced Revenue
This is where it gets tricky — and where many investors get confused.
At the end of 2024, Microsoft (NASDAQ: MSFT) reported that its AI business exceeded a US$13 billion annual run-rate.
That’s impressive — but what exactly does “AI business” include?
In Microsoft’s case, it encompasses Azure AI services, GitHub Copilot (26 million users), and Microsoft 365 Copilot (used by 90% of Fortune 500 companies).
These are products where AI is the core value proposition, and customers are paying specifically for AI capabilities.
GitHub Copilot users pay a subscription fee for AI-powered coding assistance.
Microsoft 365 Copilot costs an additional US$30 per user per month on top of standard Office subscriptions.
Azure AI customers pay for compute and API calls.
In each case, the AI is the product.
Contrast this with AI-enhanced revenue, where AI improves an existing product but isn’t separately monetised.
Meta’s advertising falls into this category — advertisers don’t pay extra for AI-powered targeting, but the AI makes the ads more effective, driving higher volumes and prices.
Meta’s AI systems like Andromeda (ad retrieval), Lattice (connecting ad knowledge), and GEM (ad selection) work behind the scenes.
Advertisers don’t see a line item for “AI services” on their invoices.
But these tools drove a 14% increase in ad impressions and a 10% gain in price per ad last quarter.
Both types of revenue are valid.
But investors should understand which bucket a company’s AI claims fall into.
Pure AI revenue is easier to track and value.
AI-enhanced revenue requires investors to draw a clear line connecting the solutions to revenue gains.
Sign #3: Check for Customer Proof Points
Real AI revenue comes with real customers. And real customers leave evidence.
Alphabet (NASDAQ: GOOGL) does not provide a single AI revenue figure similar to Meta.
Instead, the search giant offers customer proof points that tell the story.
For 2025’s third quarter, Google Cloud revenue grew by over 33% year-over-year, with operating margins expanding to nearly 24% from around 17% a year ago.
But the more telling metrics lie in the details.
Nearly 150 Cloud customers are each processing approximately one trillion tokens.
Consider the scale: these are not companies running small experiments.
Trillion-token workloads represent production-grade AI usage at massive scale.
The deal flow confirms this.
Alphabet secured more contracts worth over US$1 billion in the first nine months of 2025 than in the previous two years combined, more evidence that enterprise customers are relying on Google’s infrastructure for their AI distribution.
Similarly, Microsoft highlighted that customers who purchased Copilot in its first month of availability have collectively increased their seat count by 10-fold over 18 months.
Daily Copilot usage per user has more than doubled over the past quarter.
Usage trends that accelerate over time signal genuine value creation, rather than one-off experimentation.
If customers come back for more and use the product more frequently, the AI is delivering real value.
Look for paid customers, expansion rates, and retention metrics.
Sign #4: Watch the Margin Impact
The ultimate test of AI revenue quality is whether it improves profitability.
This is where hype meets reality.
AI infrastructure is expensive, consisting of data centres, chips, energy, and talent.
If a company’s AI revenue is growing but margins are compressing, the unit economics may not work in the long run.
Google Cloud’s operating margin expansion from around 17% a year ago to a little under 24% in its recent quarter is a positive signal.
The trend suggests that AI services are scaling efficiently where revenue is growing faster than costs.
For context, Amazon‘s (NASDAQ: AMZN) AWS operates at around 35% margins, suggesting Google Cloud has room to improve further.
Contrast this with companies where AI investments are diluting margins with no clear path to profitability.
Investment spending is acceptable in the early stages, but at some point, the returns need to materialise.
The question to ask: is AI revenue accretive to margins, or is the company spending a dollar to make eighty cents?
Get Smart: Revenue Receipts Over Promises
Here’s something everyone should remember: the AI sector is still in its infancy.
ChatGPT is barely three years old.
As it stands, many companies are still in investment mode, spending today for revenue tomorrow.
That’s fine — as long as they’re honest about it.
The danger lies in confusing potential with performance.
A company might have brilliant AI technology and still fail to monetise it.
Another might have mediocre technology but excellent distribution and customer relationships.
As investors, our job is not to pick the best AI model.
It’s to identify companies turning AI capabilities into durable revenue streams.
The framework is simple: specific dollar figures, clear revenue categorisation, customer proof points, and margin impact.
Companies that score well are likely building real AI businesses.
The best AI investments come from companies that can show you the money today while articulating a clear path to greater returns tomorrow.
After all, where the business goes, the stock price eventually follows.
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Disclosure: Chin Hui Leong owns shares of Alphabet, Amazon, Meta Platforms, and Microsoft.
