Nithin Kamath Says ‘We Use AI’ Is No Longer Enough: What Startups Should Focus On
“We use AI” may sound impressive, but it is no longer enough to make a startup stand out. Zerodha founder and CEO Nithin Kamath has a blunt message for entrepreneurs pitching investors: artificial intelligence has become so common that simply mentioning it is no longer a meaningful differentiator.
Kamath made the comments in a post on X, arguing that AI is now “table stakes” for startups. In other words, using AI has increasingly become an expected part of building modern products rather than a unique competitive advantage.
His comments come at a time when startups across technology, fintech, SaaS, healthcare, education and other sectors are adding AI features to their products. The result is a crowded market where almost every pitch can include an AI angle.
For founders and investors, that changes the question from “Does this startup use AI?” to “What makes this startup worth backing?”
Why ‘We Use AI’ Is Losing Its Differentiation
AI has become considerably easier to access.
Startups can use existing foundation models, APIs and development tools to add capabilities that once required substantial technical resources. This lowers the barrier to experimentation, but it also makes it harder for one company to claim that AI alone gives it an advantage.
Kamath's point is essentially about differentiation.
He argued that founders should not treat AI usage as a unique selling point in an investment pitch. He went as far as comparing bragging about using AI to bragging about taking a bath every day.
The provocative comparison makes the underlying business lesson clear: if everyone can make the same claim, the claim has limited value.
For an investor, a startup that says “we use AI” still leaves several important questions unanswered:
What problem does the company solve?
Who is willing to pay for the solution?
How large is the opportunity?
Why will customers choose this product over alternatives?
What prevents competitors from copying it?
Is there evidence of product-market fit?
AI can help answer some of these questions, but it cannot replace them.
What Nithin Kamath Says Founders Should Focus On
According to Moneycontrol's report, Kamath believes founders should make the problem they solve and the reason customers would choose them the centre of the pitch.
The technology should support that story rather than become the entire story.
That distinction matters.
Imagine two hypothetical startups selling software to small businesses.
The first says:
“We have an AI-powered accounting platform.”
The second says:
“Our software helps small manufacturers reconcile invoices automatically, reducing a task that previously took hours every week.”
The second pitch communicates a customer problem and a business outcome. AI can still be an important part of the product, but it is not being presented as the sole reason the company deserves attention.
That is likely to become increasingly important as investors become more familiar with AI technology.
AI Can Build the Product Faster — But That May Also Increase Competition
One of the biggest changes brought by generative AI is the speed at which software can be developed and tested.
For founders, that is an enormous advantage.
A small team can experiment with product ideas, generate prototypes and automate parts of development much faster than before. But the same technology is available to competitors.
This creates an interesting paradox.
AI can reduce the cost of building a startup while simultaneously reducing the uniqueness of what that startup builds.
If two companies can access similar models and development tools, their long-term advantage may come from factors outside the underlying model.
Those factors can include proprietary data, distribution, customer relationships, domain expertise, brand trust, regulatory knowledge and deep integration into customer workflows.
In startup terminology, these are potential moats — advantages that make it harder for competitors to take customers away.
A Better AI Startup Pitch Should Show Business Value
The most convincing AI startup pitches may increasingly focus on measurable outcomes rather than technical labels.
Instead of saying:
“Our platform uses advanced AI.”
A founder could explain:
“Our product reduces the time required to complete a specific business process, improves accuracy and generates measurable savings for customers.”
The difference is important because customers ultimately pay for outcomes, not technology buzzwords.
An investor may be interested in the underlying technology, but the investment case usually depends on whether that technology can create a scalable business.
This is particularly relevant for startups building products on top of third-party AI models. If competitors can access the same models, founders need to explain what else makes their company difficult to replicate.
AI-Generated Pitch Decks Are Not the Same as a Strong Business
Kamath also highlighted another side effect of the AI boom: the increasing availability of AI tools for creating presentations.
AI can now help founders produce polished-looking pitch decks quickly. That can save time and improve presentation quality.
But a visually impressive deck does not automatically demonstrate a strong business.
According to Moneycontrol, Kamath's broader concern is that easier access to AI-powered presentation tools could make it even easier for startups to create convincing-looking pitches without necessarily having a differentiated underlying proposition.
That creates a new challenge for investors.
When presentation quality becomes easier to manufacture, substance becomes more important.
Traction, customer retention, revenue quality, unit economics and the competitive landscape can tell investors much more than a sophisticated slide deck.
What Makes an AI Startup Truly Different?
There is no single formula for creating a defensible startup, but several factors can matter.
Proprietary Data
A company that collects unique, high-quality data through its product may be able to build capabilities competitors cannot easily reproduce.
Distribution
Having a great product is not enough if reaching customers is expensive. Strong distribution can become a major competitive advantage.
Industry Expertise
A startup with deep knowledge of a specific industry can potentially solve problems more effectively than a general-purpose AI application.
Customer Trust
In sectors such as financial services, healthcare and enterprise software, trust and reliability can matter as much as technology.
Workflow Integration
An AI tool that becomes deeply embedded in a customer's daily operations can be harder to replace than a standalone feature that simply produces similar outputs.
These advantages can remain valuable even as AI models improve.
What This Means for India's Startup Ecosystem
Kamath's comments are particularly relevant to India's growing startup ecosystem.
AI is lowering the barriers to building software, which could encourage more entrepreneurs to experiment with new ideas. At the same time, the number of competing products could rise.
That could make fundraising more selective.
Investors may increasingly ask founders to demonstrate not only that they can build something using AI, but also that they can turn that technology into a durable business.
For early-stage startups, this could mean greater emphasis on customer validation and product-market fit. For more mature companies, metrics such as recurring revenue, retention, margins and customer acquisition efficiency could become increasingly important when investors assess whether the AI story translates into economic value.
The AI label itself may carry less weight as the technology becomes more widespread.
The Bigger Startup Lesson
Kamath's comments do not amount to an argument against AI.
Quite the opposite: AI can be an extremely powerful tool for founders.
The lesson is that founders should avoid confusing technology adoption with differentiation.
A company can use the latest AI models and still have a weak business if customers do not care about the product or if competitors can reproduce it easily.
On the other hand, a company with a genuinely valuable product can use AI to make that product faster, cheaper, more accurate or more scalable.
That distinction could shape the next phase of the startup market.
The first wave of AI enthusiasm was heavily focused on what the technology could do. The next phase is likely to focus more heavily on who can build sustainable businesses around it.
What Founders and Investors Should Watch
For founders, the takeaway is straightforward: make the customer problem, solution, traction and competitive advantage the centre of the pitch. AI should explain how the company delivers that value — not serve as the entire reason the company deserves investment.
For investors, the important question may be whether AI is creating a genuine economic advantage or simply adding another feature to an otherwise ordinary product.
As AI becomes increasingly accessible, the companies that stand out may not necessarily be those shouting the loudest about artificial intelligence. They may be the ones solving a specific problem better than competitors and building advantages that are difficult to copy.
Bottom Line
Nithin Kamath's latest warning to founders is simple but increasingly relevant: “We use AI” is no longer a strong startup pitch by itself.
AI is becoming part of the basic toolkit for modern companies. What matters next is differentiation — a clear customer problem, strong product-market fit, measurable value and a business advantage that competitors cannot easily reproduce.
For founders, the challenge is therefore moving from using AI to explaining why their business still matters when everyone else can use AI too.
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