Everyone is Raising Shrimp, the Great Shrimp Battle, but in 2026, 99% of Companies Using AI Still Haven't Made Money.

In recent weeks, from comprehensive shrimp farming to the great shrimp battle

WorkBuddy, jvs claw, ArkClaw... these big companies are frantically launching their consumer-grade Agent products. Trying to redefine personal efficiency?

But there is a fact that few people mention:

It's 2026, and 99% of companies using AI still haven't made money.

You can look around you, everyone is talking about large models, Agents, and shrimp farming, but how many can actually run Agents in business operations?

Generating a beautiful PPT or writing a copy is indeed very simple.

But what businesses face is: understanding the market, defining products, breaking down business traffic, formulating strategies...

These things have never been solved by a single prompt.

So where does the problem lie?

Now, for companies using AI, the model is definitely not the threshold.

As of today, a very objective fact is:  the performance gap between models is very small now.

Models from both domestic and foreign sources, open-source and closed-source models. On mainstream benchmarks, scores are all clustered in a very narrow range.

Now everyone can access first-class models. Even the pricing between models is almost in the same order of magnitude, all competing against each other.

Therefore, many recent articles have stated that the real barrier is no longer the model, but the context.

Forbes recently published an article defining the biggest current problem for enterprises as the Business Context Gap.

https://www.forbes.com/councils/forbestechcouncil/2026/03/12/the-business-context-gap-undermining-enterprise-ai/

The main point is that although today's large models can find a lot of data based on some retrieval tools, they cannot understand the business logic and decision-making context within the enterprise, so the models cannot truly make reliable decisions.

For enterprise AI, Data is never equal to Context

This is a very crucial distinction!

Because many people particularly like to say, we have a lot of data in our company, just let the large model learn, we are not lacking information at all.

But in fact, data and context are two different things.

Recently I saw a very good article from Trackmind, the address is: https://www.trackmind.com/data-vs-context-enterprise-ai/

This article discusses some very interesting points and can use an analogy to illustrate the two.

For example, you tell AI that last month 10,000 items were sold, this is Data.

But what AI doesn't know is that among these 10,000 items, it might be because of channel stockpiling, the remaining 5,000 items came from major influencers, and another 2,000 items were repurchases from old customers. It also won't know that the batch with stockpiling might have a very high return rate, and the user profiles and target groups of the batch sold by influencers are completely mismatched.

These