Profit Is Not in the Tool: Why AI Applications Don't Make Money, and Why Profit Lies in To B Workflow Capture

[[**The difficulty of AI applications making a profit lies in the low barrier of single-point capabilities; real profit comes from to B workflow capture, i.e., taking over a company's complete business processes and binding to its revenue growth, rather than charging consumers.**

**Key points**

**1. The root cause of AI applications not making money is on the supply side**

Single-point capabilities (writing code, making images, translation, etc.) have abundant supply and zero switching cost, so profits inevitably trend toward zero; whereas AI infra sold to traditional enterprises to transform supply chains creates incremental value, so "selling shovels" is more profitable than "mining."

**2. The profit model must shift to enterprise-level workflow capture**

Help a client earn 500,000 more per month, and you can charge 300,000; what clients care about is ROI bound to real P&L, not which model runs behind the scenes. The only path to getting paid is to take over complete business processes and deliver clear revenue growth.

**3. The best target range is labor-intensive cognitive labor industries**

**4. The essence of the change is supply-side restructuring, not demand-side optimization**

Consumers will not pay for an AI price-comparison feature that saves a few minutes (the demand-side ceiling is low), but enterprises use AI to redo products, services, and operations, with costs entering COGS and covered by better unit economics; consumers only need to enjoy better products without knowing AI exists.

**5. The biggest opportunity is to redo traditional businesses with a new cost structure**

Higher productivity does not mean everyone's wages rise accordingly. Fewer people deliver more services, and the contraction of white-collar jobs and the improvement of consumer life will happen at the same time. Your salary may not rise, but the same money buys a better experience, more bang for the buck.

The whole industry is asking: why does AI infra make money while AI apps do not? NVIDIA keeps printing money, Higgsfield is doing well, while AI app makers come one after another and get flattened. Manus had just risen when Muse Spark cut it off; Cursor was gaining momentum when Claude Code suddenly appeared.

AI apps for AI users is a zero-sum game; traditional industries buying AI infra is incremental creation.

Anthropic, Zhipu, OpenAI Codex, and ByteDance Seedance collectively prove: the correct posture for AI is to B, not to C. The good model for ultimate payment must be Enterprise: enterprises pay.

Workflow Capture is the essence

When a vessel is abundant, it becomes cheap: single-point capabilities are worthless. Writing code, making images, translation, generating PPTs—abundant supply, low barriers, zero switching cost, profits inevitably trend toward zero.

What can get paid is workflow capture: taking over a complete business process and delivering a clear wave of revenue growth. Only by helping clients make more money can they not do without you.

A common misjudgment is thinking token cost is the core bottleneck of AI deployment. It is not. The decisive factor is L4 running through the full process of incremental revenue. Help a client earn 500,000 more a month, and you can charge them 300,000 (!!); they do not care which model runs behind you.

Even if the books do not break even today, token costs will drop 30-fold next year, and future EBITDA will look very good. Those who dare to operate at a loss during this window period will reap the time-difference dividend of the cost curve and occupy the workflow capture position.

The only KPI clients are willing to pay for long term is one type: vertical ROI bound to real P&L. Everything else is a demo.

The author's accounting formula is very plain: replacement rate x headcount; the product is the divisible value.

Following this logic, what businesses can currently make money by hiring purely low-end white-collar workers? E-commerce, livestreaming, game guilds, insurance sales teams, online course sales teams. Large numbers of white-collar workers do highly repetitive cognitive labor; labor is the largest cost item, and output is directly quantifiable. These are the best target ranges for AI workflow capture.

The author thinks the future is not AI personal assistants helping you choose between Mercedes-Benz, BMW, and Volkswagen, but new players like Li Auto and NIO emerging, using new technology to build better products and covering all channels—seeding, test drives, service, 4S stores, communities, OTA, lounges, car owner clubs, etc.—ultimately providing better products/services;

The supply side is completely different. Enterprises use AI to redo products, services, and operations; AI costs enter COGS and are covered by better unit economics. Consumers do not know there is AI behind it at all; they only know this company's stuff is better, the price more reasonable, and the service more in place.

The deepest change here is not making existing customer service 30% more efficient—that is optimization. The change is: what kinds of enterprises have, for the first time, become economically feasible?

In the past, after a brand sold a product, its relationship with the consumer was basically over. It was not that they did not want to maintain it; maintenance costs were too high. Continuously understanding each customer's preferences and proactively providing personalized after-sales service—under traditional cost structures, only luxury brands could afford to do this for top VIPs. When the cost of cognitive labor trends toward zero, you can write this capability into product definition and organizational structure from day one.

History has repeatedly verified the same thing: when the underlying cost structure changes by orders of magnitude, the win rate of new players is far higher than incumbents expect.

In the year Tesla was founded, no capital was optimistic.

Profit lies not in the vessel; bow down to toB

Huawei HarmonyOS Intelligent Mobility is taking this path. It does not sell SaaS, does not charge consulting fees; it goes deep into product definition and brand operations, jointly builds consumer-facing products with industrial partners that have manufacturing capabilities, takes responsibility for business results, and shares profits.

Profit lies not in the vessel, but in what the vessel accomplishes. AI industry signal channel: Frontier Technology. This content comes from the internet, original link; the views represent only the author and do not represent Huxiu's position. For copyright issues, please contact hezuo@huxiu.com, and we will verify and handle it promptly. Everyone who is changing and wants to change the world is in the Huxiu APP[ ]]]