As generative AI, Vibe Coding, and various automation tools rapidly become mainstream, more and more founders are pursuing “AI Maxing”: buying large amounts of Tokens, deploying AI agents, and rapidly developing apps—trying to do more work with fewer people.
However, U.S. entrepreneur and investor Alex Hormozi has offered a reflection on this trend. In a recent video titled “Why AI Won’t make you rich in 2026,” he pointed out that many people use AI heavily every day, Token bills keep rising, yet their actual income doesn’t increase in step.
Hormozi believes the issue is not that AI lacks efficiency, but that companies mistake “increasing output” for “creating value.” When AI enables people to accomplish more, companies often don’t concentrate resources on the most critical bottlenecks; instead, they begin executing lower-priority tasks that previously weren’t worth doing—faster. AI may make companies busier, but it doesn’t necessarily make them more profitable.
AI has not eliminated the leverage brought by capital, media, and teams.
Hormozi defines “leverage” as the gap between input and output: putting in little but getting a lot in return means higher leverage; putting in many resources and ultimately getting limited results means lower leverage.
AI is undoubtedly a high-leverage tool. A single person can now use generative AI to write ad copy, analyze data, generate images, and create programs—doing work that used to require multiple people to handle.
But Hormozi emphasizes that the emergence of AI has not canceled other forms of leverage.
Capital can still amplify investment returns; media content can influence thousands or even millions of people at once; and teams can let a company push more work simultaneously. When different levers—capital, media, talent, brands, and AI—combine with each other, the effect may even be multiplicative rather than simply additive.
He also gives the example of large AI labs, noting that even these companies that have the world’s most advanced AI models still employ thousands of employees. At the very least, this shows that, at this stage, AI has not made human labor, organizational management, and professional judgment lose their value.
A company’s biggest problem is usually not that “the work isn’t done fast enough.”
Hormozi observes that after many business owners adopt AI, because individual and team workloads expand, they begin to carry out tasks they previously had no time for.
On the surface, the company’s production capacity has increased. But these previously postponed tasks often weren’t prioritized precisely because they were less important.
The result is that companies start using AI to produce more content faster, develop more features, and build more automated processes—without first answering the most core question: Will these tasks truly increase revenue?
Hormozi believes that the lack of AI sometimes forces operators to make more strict prioritization decisions, concentrating limited manpower and capital on the things that truly change operational results.
When resources become more abundant because of AI, companies may lower the threshold for choosing work, allowing more low-value tasks to enter the execution process.
AI can improve efficiency, but it may not be able to solve a company’s real bottleneck.
Hormozi does not completely deny AI. He points out that AI has indeed delivered real results in some business scenarios.
For example, an advertising team can use AI to produce more ad creative; a sales team can deploy AI sales representatives to handle initial outreach, qualification screening, and information replies. Companies can also reduce certain administrative and delivery costs through automation.
But the problem is that these tasks are often not the main constraints to business growth.
A company may truly lack market demand, brand trust, an attractive product, an effective sales process, or the ability to continuously deliver high-quality service. If a company’s core product lacks competitiveness, even fully automating the back end is unlikely to bring fundamental growth.
Hormozi says that if AI can perfectly solve most of a company’s bottlenecks, then companies that adopt AI heavily should, in theory, show significant revenue growth—but in reality, that is not the case.
The reason is that many companies use AI in places that are not the true areas constraining revenue growth.
You can create ten times the leverage without using AI.
Hormozi further points out that there are many ways businesses can greatly increase leverage without any AI technology at all.
For example, one-on-one service can be changed into a one-to-many small-group model. If one consultant could previously serve only one client at a time, and now can serve ten people at once, then—at least theoretically—the revenue and influence generated per unit of effort could increase to ten times the original.
Companies can also convert synchronous services that require reservations into asynchronous delivery, reducing the time staff must be present at the same time; or redesign the sales process to educate potential customers in advance, so that customers who truly enter a sales call understand the product better and are more likely to buy.
In that case, the company may no longer need ten salespeople to have a large number of low-efficiency conversations; instead, it may only need two salespeople to serve the filtered, high-intent customers and complete the same—or even more—transactions.
These process designs don’t necessarily require AI, but they may improve a company’s unit economics more directly than simply introducing a chatbot.
The tool with the highest leverage is not AI—it’s “making the right decisions.”
Hormozi believes that the behavior with the highest leverage in running a business isn’t even using AI—it’s making the right decisions about resource allocation.
Suppose a team is about to spend a large amount of time executing a task. If a manager can promptly determine that the task is no longer important and directly cancel the entire plan, the team’s time can be freed up immediately.
By contrast, if managers merely use AI to automate the task, the result at the end is still likely to be an unimportant outcome.
Therefore, he proposes a core idea: “Deciding that something is no longer a priority is more efficient than automating an unimportant thing.”
This decision not only saves an individual employee’s time—it simultaneously improves the resource utilization efficiency of the entire team, capital, and operational systems. Related show summaries describe this concept as: if AI is used for low-priority tasks, it only makes the company more efficient at work that has no real impact.
An increase in token bills does not mean an increase in revenue
Hormozi points out that many people now treat using lots of tokens, building more agents, and developing more apps as proof that they are improving their competitiveness.
But for businesses, the most important yardstick isn’t how much AI is used; rather, after adopting AI, whether it truly increases revenue, improves gross margin, reduces delivery costs, or removes the company’s main bottleneck.
Business operators should ask themselves directly: “After I introduce AI into my company, am I really making more money?”
If the answer is no, the problem usually isn’t that AI has no value—it’s that the company may be using AI in the wrong place.
The reasonable use of AI should be to focus on solving the key problems that limit business growth, rather than—because tool capabilities have increased—starting to create many more tasks that didn’t exist before.
Hormozi compares the current stage of AI to companies suddenly gaining a large batch of virtual assistants.
These virtual assistants can quickly research information, organize content, generate drafts, handle administrative tasks, and even help develop software. But even if a company was able to hire low-cost assistants in the past, that doesn’t necessarily mean it can build a successful brand or come up with an excellent product.
AI also cannot automatically eliminate market competition.
Companies will still compare product quality, brand trust, sales capability, service experience, and product positioning. Good products still compete with bad products; a strong brand still closes deals more easily than a brand lacking trust.
If a company offers a solution that lacks appeal, AI might help create more ads, write more copy, and build a more complete automated funnel—but these efficiency improvements may not change the fundamental reality that customers are unwilling to buy.
AI is a tool, not the business strategy itself.
In the end, Hormozi summarizes that AI indeed increases the leverage companies can use, but it does not replace capital, talent, media, brand, process design, and management decision-making.
For most companies, the factors that really drive growth haven’t changed: creating more market demand, improving demand conversion rates, and continuously delivering high-quality products and services.
AI can strengthen the processes above, but it cannot decide for a company which problems should be addressed first.
The capital, manpower, and time each company has are extremely limited compared with everything that could possibly be carried out in the market. Whether a business can concentrate these limited resources into a single bottleneck that most drives revenue growth remains at the core of business management capability.
After AI significantly reduces execution costs, the importance of “what to do” may be even higher than “how to complete it quickly.”
Alex Hormozi’s video
This article is authorized for reprint from: (LinkNews)
Original author: Neo
Original title: Why AI can’t make you rich? Alex Hormozi: Automating unimportant things only makes wasting resources more efficient
“Why can’t AI make you rich? Entrepreneur: Automating unimportant things only wastes resources more efficiently.” This article was first published on “Crypto City.”