The New Rules of Fundraising for AI Startups

Startup fundraising has always followed a familiar formula: build the product, prove traction, raise enough money to reach the next milestone.

But AI is disrupting that formula. Models change quickly, inference costs fluctuate, enterprise sales cycles can stretch for months and today’s breakthrough feature can become tomorrow’s commodity. The assumptions behind a traditional runway or growth plan can change before a company reaches its next round.

That means AI founders need to rethink what they are raising capital to prove. Is it customer demand? Technical feasibility? Sustainable unit economics? Enterprise readiness? Or a durable advantage that can survive the next model release?

Founders shouldn’t just consider how much money their startup can raise, but what that capital needs to accomplish—and what evidence it should produce along the way.

Members of the Senior Executive AI Think Tank—a curated group of executives and practitioners working across machine learning, generative AI and enterprise AI applications—share the fundraising advice they believe AI founders should leave behind, from chasing maximum capital to relying too heavily on traditional traction, and what investors should see instead.

“In the AI era, investors should finance evidence of value—not the cost of building impressive technology.”

– Fabio Danze Montini, Investor and Owner of FDM Industrial Sales & Marketing SL

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Raise Against Evidence, Not Maximum Capital

Fabio Danze Montini, Investor and Owner of FDM Industrial Sales & Marketing SL, challenges one of the most familiar fundraising formulas: “Raise as much as you can, as early as you can.”

“In AI, technology, costs and competitive advantages can change faster than your funding cycle,” Montini says. “Too much capital too early can lock a company into assumptions that will be obsolete in 12 months.”

His alternative is to make fundraising a progression of validated milestones.

“I would raise against validated milestones: a real customer problem, measurable business impact, repeatable adoption and a defensible advantage in data, workflow or distribution,” he says. “In the AI era, investors should finance evidence of value—not the cost of building impressive technology.”

Separate Pilot Enthusiasm From Real Demand

Mani Padisetti, Founder of Almost Magic Tech Lab, argues against another conventional fundraising signal: the high-profile paid pilot.

“I would challenge the advice that a paid pilot with a famous customer proves demand,” Padisetti says. “An AI trial may be paid for by an innovation team whose job is to experiment; renewal may depend on an operations manager with a different budget and a harder test.”

Before using revenue from the pilot to support fundraising, he adds, leaders should establish “who would pay to keep the product running and what result would justify that decision.”

“Show investors the difference between money committed to an experiment and money committed to continued use,” Padisetti says. “That distinction helps founders avoid building a sales plan around enthusiasm that has no lasting budget.”

Build the Layer That Survives Model Change

Vivek Kumkar, Sr. GenAI Leader at Amazon Web Services (AWS), challenges founders who see proprietary foundation models as the ultimate moat.

“Founders should stop raising capital to build and own a proprietary foundation model,” Kumkar says. “Owning the base-weights layer looks like a moat, but pre-training is now a fast-commoditizing cost center that larger providers reprice and surpass every few months.”

Instead, he says, founders should build around what enterprise customers actually buy.

“Enterprise buyers do not pay for raw weights,” he says. “They pay for secure, compliant, deeply integrated workflows built on trusted data.”

That also changes how founders should think about runway. Traditional runway math can overlook the time required to turn a technically ready product into an approved enterprise deployment. 

“A regulated buyer often takes nine to 14 months to clear a vendor through security, privacy and model-risk review,” Kumkar says. A round sized around a single product build cycle can therefore run out before the customer is cleared to deploy.

For Kumkar, the fundraising strategy should account for both technological change and enterprise timelines. 

“I would raise against the orchestration layer, the proprietary data and the governance controls that survive the next base-model release,” he says, “and size the round to two procurement cycles, not one product roadmap.”

“In AI, building is the cheap part. Getting approved is not.”

– Ajay Pundhir, Founder of AskAjay.ai and AIExponent.com

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Fund the Approval Cycle, Not Just the Build Cycle

Ajay Pundhir, Founder of AskAjay.ai and AIExponent.com, would retire the advice to raise 18 months of runway to build the product.

“In AI, building is the cheap part. Getting approved is not,” Pundhir says.

He also says founders need to incorporate enterprise procurement into their capital plan.

“Founders raise a build-cycle round and then run out of cash inside someone’s procurement queue,” he says.

His proposed milestone is therefore operational rather than purely technical.

“Size the round to two full procurement cycles,” he says, “and treat evidence as a milestone. A passed model-risk review or a signed DPA in a regulated sector tells me more than a discounted design-partner logo.”

He adds investors should stop asking, “Can you build it?” and instead ask, “Who has already cleared you to deploy it?”

Do Not Wait for Traditional Traction

Hastimal Jangid, Co-Founder of RankRabbit AI, argues that the familiar sequence of prototype, revenue, traction and then fundraising does not always fit AI businesses.

“‘Show traction before you raise’ is outdated for AI companies,” Jangid says. “Traditional advice says wait for revenue to prove demand. But AI costs are front-loaded—compute, training, talent—before a product can exist.”

His alternative is not to abandon evidence, but to change what qualifies as evidence.

“Instead, raise on a credible technical thesis and a narrow, provable wedge, not full traction,” he says. “Show a working prototype at a small scale, and state the capital intensity ahead.”

Jangid also cautions against presenting an artificially precise development roadmap.

“The honest answer to ‘what happens in 18 months’ is often ‘we’ll know more once models evolve,’” he says. “Investors who get this read overconfident roadmaps as a red flag.”

Treat Investors as Strategic Inputs

Bhubalan Mani, Leader in Supply Chain Technology and Analytics, brings a perspective that shifts the fundraising question from the amount of capital raised to what each investor enables the company to access.

“Money is the commodity; allocation is the moat,” Mani says. “The tired advice: Take the biggest name and the biggest check.”

Instead, he recommends treating the cap table like an operating system for the company.

“In AI, capital is fungible while compute, proprietary data and distribution are not,” he says. “Run the cap table like a supply chain: Qualify each investor on the scarce inputs they unlock, not the logo.”

That can mean negotiating for resources that would otherwise be expensive or difficult to obtain.

“A term sheet that guarantees GPU allocation, a design-partner channel or clean data often beats a higher valuation from a passive fund,” Mani says. “Negotiate for credits and reference customers, not just dollars. And dual-source; leaning on one backer is the same trap as a single supplier. Raise less, but tie every check to inputs you cannot buy off the shelf later.”

Preserve Flexibility by Limiting Capital

Lynn Comp, Head of AI Center of Excellence at Intel, takes the argument about capital discipline a step further: If a company can finance its growth through operating revenue, it may not need to optimize for a large institutional round.

“If you can bootstrap yourself using run-rate operating revenue, and/or find creative ways of limiting your token use, don’t take the money or ‘free loans,’” Comp says.

The underlying point is control. If an AI company can generate significant revenue with a relatively small team, it has less reason to accept highly discounted tokens, large investor rounds or other capital simply because it is available.

“The more you can stick to seed funds and angel investing to get off the ground, the more flexibility you retain,” she says. “The early investors can realize lower-risk returns.”

For AI startups, where technology and pricing can move rapidly, preserving the ability to change course can itself have financial value.

Make Unit Economics Part of the Pitch

Manpinder Singh Panesar, Senior Solutions Architect at Amazon Web Services (AWS), challenges the advice to “focus on growth now; work out the economics later.”

“For an AI application, I would want a funding story that shows what it costs to deliver a reliable customer outcome, including inference, retries, human review and support,” Panesar says.

Panesar also says founders need to show what remains valuable when the underlying model improves.

“I would also show what remains valuable if a model provider adds our headline feature,” he says. “For me, that points to business context, trusted data, workflow integration and customer relationships.”

He recommends raising against milestones that prove repeatable value as well as sustainable delivery, while allowing for flexibility to change models or architecture.

“A polished five-year roadmap can create false confidence when the underlying technology, economics and customer expectations may change in months.”

– Divya Parekh, Founder of executive coaching brand DivyaParekh.com

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Replace Certainty With Adaptability

Divya Parekh, Founder of executive coaching brand DivyaParekh.com, says one conventional fundraising expectation deserves particular scrutiny: certainty.

“One piece of fundraising advice I would retire is to ‘sell investors on certainty,’” Parekh says. “In AI, a polished five-year roadmap can create false confidence when the underlying technology, economics and customer expectations may change in months.”

Rather than pretending uncertainty does not exist, she recommends making the ability to respond to it part of the investment case.

“I would rather see a founder who is clear about what is durable and what is still a hypothesis,” she says. “What do you know about the customer problem? What advantage survives a model change? What are you learning faster than others? And how quickly can you redirect capital when an assumption breaks?”

That turns adaptability from a defensive quality into part of the strategy.

Make Capital Buy Strategic Optionality

Dileep Rai, Manager of Oracle Cloud Technology at Hachette Book Group (HBG), challenges the idea that founders should maximize the amount of capital available whenever investors are willing to provide it.

“One outdated rule is to ‘raise as much as you can when capital is available,’” Rai says. “In AI, abundant funding can actually hide weak economics.”

Instead, he recommends linking fundraising to learning and durability.

“I would advise founders to raise against validated learning and durable milestones, not maximum valuation,” Rai says. “Prove customer willingness to pay, understand inference-level unit economics, demonstrate retention and identify what remains defensible if foundation models improve dramatically.”

His conclusion? Capital should create choices, not simply create a larger organization.

“The strongest AI companies will not necessarily be those that raise the most, but those that convert capital into differentiated data, distribution, trust and repeatable customer value,” Rai says.

10 Fundraising Rules for AI Founders

• Raise against validated evidence, not a maximum check size. Tie each round to customer problems, measurable impact, repeatable adoption and defensible advantages.
• Distinguish experimental revenue from durable demand. Identify who owns the budget for continued use and what measurable result will justify renewal.
• Build around advantages that survive model changes. Prioritize orchestration, proprietary data, workflow integration, governance and customer relationships over raw model weights.
• Budget for enterprise procurement timelines. For regulated markets, size capital around the time required for security, privacy, legal and model-risk approvals.
• Use technical proof when traditional traction is premature. A working prototype, narrow wedge and credible technical thesis can provide evidence before significant revenue exists.
• Evaluate investors for what they unlock. Compute capacity, design partners, distribution, reference customers and proprietary data can be as consequential as the check itself.
• Preserve ownership when operating revenue permits it. Bootstrapping or using smaller early rounds can provide flexibility when the business can finance development internally.
• Make AI unit economics visible. Show inference, retries, human review, support and other variable delivery costs rather than presenting growth without its underlying economics.
• Present uncertainty honestly. Explain which assumptions are durable, which remain hypotheses and how quickly the company can redirect capital when conditions change.
• Use capital to create strategic options. Fund differentiated data, distribution, trust and repeatable customer value rather than organizational complexity for its own sake.

Rethinking the Fundraising Playbook

The AI era does not eliminate traditional fundraising principles—it changes which ones matter most. Across the Senior Executive AI Think Tank, the advice is remarkably consistent: Founders should connect capital to evidence, understand the full path to enterprise adoption, know their variable costs and build advantages that can withstand rapid changes in underlying models.

As AI infrastructure, pricing and capabilities continue to evolve, fundraising itself may become less about predicting a five-year future and more about demonstrating that a company can learn, adapt and preserve value as that future changes.