Every wave of technological change gives entrepreneurs a kind of illusion: that old experience has expired, and that the pitfalls previous generations stumbled into have nothing to do with them.
Recently, in conversations with many partners in AI entrepreneurship, one of the phrases I hear most often is: we are AI Native — said with a touch of pride.
The implication is clear:we are not the same as those old-guard traditional internet companies.
Every time I hear this, I think of the pioneering entrepreneurs in the blockchain industry. People liked to talk about Crypto Native and Web3 Native, emphasizing that they grew up out of the crypto world. And the internet companies represented by BAT naturally became the representatives of "traditional enterprises."
Many teams believe that by placing organization, financing, community and product within a new technological narrative, many problems will simply resolve themselves; and if you cannot resolve them, then it is because you do not understand Web3, do not understand Crypto, and are not Native enough.
The moon that shone on people in ancient times once shone on those before them.
Today's entrepreneurial teams that speak of nothing but AI Native carry, to some degree, a familiar recipe and a familiar flavor.
Is AI Native a false concept?
To avoid misunderstanding, let me put my personal conclusion first: AI Native is not a completely meaningless term, but treating it as a new corporate species may still be premature.
I am inclined to believe that AI will gradually enter the daily operations of more and more companies. But the pace of adoption and the depth of use still depend on the costs, reliability requirements and difficulty of transformation in specific industries.
E-commerce companies will use it for product selection, customer service and content production; manufacturing enterprises will use it for forecasting, quality inspection and production scheduling; traditional software companies will also put AI back into their product, sales and R&D processes; and even in the legal profession, which is the least digitized of all, many lawyers will use it to organize materials, search for information and build knowledge bases.
The fact that everyone will eventually be able to use the same technology does not mean everyone will become equally strong.
For teams that adopted AI early, if they have already turned customer feedback into evaluation criteria and distilled trial-and-error experience into their processes, latecomers who buy the same model may not be able to catch up immediately. Technology will become widespread, but the organizational capabilities built around that technology require accumulation.
It is not sufficient to deny first-mover advantages merely on the ground that "everyone will use it eventually."
AI Native is meaningful as a description of a product design. For example, if a product from the outset has AI receive objectives, invoke tools and execute tasks, its interaction model and cost structure may be markedly different from traditional software. If the service cannot exist without AI, then "native" does indeed indicate a certain technological dependence.
But a product's dependence on AI and a company's possession of long-term competitiveness are two separate things that must each be proven. Can competitors buy the same capabilities? Why would customers continue to choose you? A technological necessary condition cannot directly serve as a sufficient condition for commercial success.
This is precisely what I think everyone needs to pay attention to regarding "AI-native enterprises."
A company is not a collection of prompts
Those who have actually run a company may better understand what I am trying to convey.
A company is not a pile of task cards, nor will it run automatically simply by assigning a model to every position. Whether a product should be built, and who makes the call; what customers are truly willing to pay for; how sales commitments align with delivery capacity; whether customer relationships and key knowledge remain after core employees leave; who bears responsibility when an incident occurs; how long the cash on the books can sustain operations—AI can help you collect information, generate options and reduce repetitive work, but it cannot eliminate the costs for you.
Organizational problems in particular are often harder than tool problems. The fact that a salesperson receives an AI-generated customer profile does not mean he better understands why the customer declined; the fact that a product manager can produce ten prototypes in half a day does not mean anyone is willing to use any of them on an ongoing basis. Once the bottlenecks that previously constrained production capacity are cleared, customer choice, internal coordination and acceptance of results may become the new bottlenecks.
One link becoming ten times faster does not necessarily make the entire business ten times faster.
What many AI teams are most prone to overestimating right now is precisely this point: mistaking "faster output" for "business problems solved."The former did indeed happen quickly, while the latter still depends on long-term judgment, collaboration and trade-offs.
Take an AI customer service project that is about to be deployed: it answers fluently in a demo, but that completes only part of the work. Once connected to the client's actual operations, it must also determine which refunds can be approved automatically, which prices cannot be promised, and under what circumstances the matter must be handed over to a human. What the client buys is a service that is handled properly; when evaluating it, the client will naturally factor in error handling and subsequent rework as well.
Part of an organization's value lies in these less standardized matters: who makes the trade-off when requirements conflict, where authority stops, who takes over when something unexpected happens, and how to avoid repeating a mistake after it has been made once. Even with fewer people, these arrangements still have to exist. They can be written into software and rules, or assigned to a responsible person, but the work cannot be treated as having disappeared merely because the position has disappeared.
AI Native: Building Dreams for the Capital Market
I am not averse to a team using AI Native to express that it prioritizes AI within the organization. In external recruitment, financing or the search for its first customers, it at least conveys an attitude: this team is willing to start from what AI can do and redesign its products and processes, rather than attaching a chat window to an old system and calling the transformation complete.
It is easy to understand why this expression is appealing. Early-stage teams do not yet have enough customers and revenue, and they need to convince investors of future possibilities. "We are AI Native" can quickly convey an image of low team costs, rapid expansion and no historical baggage. In financing and recruitment, the term has communicative value. The problem is that the imagined advantages have to be delivered in subsequent operations; they cannot be regarded as proven merely because everyone has accepted the term.
Some companies have used AI since their first day of existence, yet still sell products, manage teams and serve customers in the old way; many teams labeled "traditional companies," by contrast, use AI very deeply in a particular business link. They may not call the entire organization AI Native, but they will redo customer support, R&D collaboration, risk control review or supply chain forecasting, and truly turn the time and costs saved thereby into customer value.
From an operational perspective, the latter is not necessarily behind the former. What is more worth guarding against is that if a concept offers only the declaration that "only native is advanced" without answering what outcome counts as failure, it can hardly help operators correct their decisions. If success with AI counts as a native advantage, while failure with AI is said to mean not native enough, such an explanation is almost never wrong, yet it provides little judgment value.
For business operators, there is a question rarely answered by the "AI native" narrative: after AI achieves high efficiency, who ultimately keeps the efficiency dividend?
Suppose a content services company uses AI to substantially reduce production costs. At first, it can deliver more and earn better profits. But if peers acquire the same capability and customers can easily compare and switch suppliers, prices may fall accordingly. In the end, industry output increases and customers buy more cheaply, yet this company is not necessarily more profitable than before.
Between higher production efficiency and higher corporate profits lies competition.
Of course, some teams can use the time saved to run more experiments, discover needs they could not previously serve, or retain customers with more consistent quality. Only the customer relationships, business data and delivery experience accumulated in this way can make the advantage sustainable. There are also services that did not exist before because they were too expensive, and that become a new business once costs fall.
The business opportunities of AI lie in these specific changes; it is not enough to look only at how many employees have been reduced.
Therefore, to assess the value of AI to a company, one must at least consider together the labor saved, the increased model costs, and the verification and rework costs, and then look at how much customers are actually willing to pay. If only per-capita output is presented and a large amount of manual review is hidden in the founder's overtime, it is very easy to mistake cost transfer for cost elimination.
As generated content, code and solutions become increasingly easy to obtain, what is scarce will also change. In a market that does not lack output, understanding demand, finding customers, identifying good from bad, and maintaining stable performance may instead have a greater impact on how much revenue a company can retain. This is not to say that these capabilities will forever be unaffected by AI, but rather to remind entrepreneurs: competition follows the bottleneck, and will not stop at the link you are best at demonstrating.
AI cannot let entrepreneurship bypass common sense
Every wave of technology gives entrepreneurs an illusion: that old experience has expired, and that the pitfalls predecessors fell into have nothing to do with them.
When blockchain was hot, some people treated community mobilization as governance already completed, and token financing as a business model already established. Today, some also expect that a few Agents can replace an organization, and treat generation speed as demand already validated. The uses and actual value of the two waves of technology are of course different; I think of them because entrepreneurs sometimes make similar mistakes: expanding the part of the problem that a new technology solves into an explanation of the entire business.
New tools do change jobs, and also allow small teams to attempt businesses that were previously unaffordable. Business common sense likewise needs updating: since some experiments have become cheaper, demand should be validated at lower cost and in shorter cycles; since some work can be executed automatically, there is no need to retain every approval step in order to maintain the original structure. But these changes still need to be tested by customer outcomes and real costs.
AI will push this process faster, and will also amplify mistakes faster.
In the past, a wrong direction might take half a year to be exposed; now a team can produce dozens of versions that look good in a month. As speed increases, the quality of choices becomes more important instead. Otherwise, AI merely helps you spend time and budget on the wrong places more efficiently.
So I prefer to change AI Native from an identity word into a verifiable business hypothesis. Take one key business chain, compare what customers obtained and what the company paid before and after the transformation, and then see whether this improvement can be repeated across more customers.
Truly competitive teams most likely will not prove themselves over the long term by saying "we are AI Native."
They will let customers feel the difference in products, prices, delivery and experience. Even if all competitors can use AI, customers are still willing to renew, and the team can restore delivery after mistakes occur; only this kind of difference is worth serious discussion.
Technological waves always bring new terms.
New terms can focus attention on areas of rapid change and can also help early-stage teams gain opportunities.
But entrepreneurs would be well advised to distinguish which statements are aimed at securing others' confidence in the future, and which facts are already sufficient to guide today's operations.
