Founder/CEO
Founders are past the AI hype, but into the messy middle.
We asked founders to react to five provocative statements about AI to understand their mindset about AI. Their responses reveal a cohort that has firmly moved past the hype cycle, but hasn’t yet figured out all the operational details.
The verdict is in: AI is a force multiplier.
The hype narrative is effectively dead among this founder base. They’ve seen enough real-world results to dismiss it.
Founders have moved past the belief that AI works. They claim it dispropor-tionately benefits them. AI amplifies the speed, scrappiness, and lack of legacy baggage that define startups.
Skeptics are extinct. What remains is a spectrum.
When we asked founders to assess their organisation’s AI maturity, the results fell into an almost perfect three-way split. The majority have moved beyond experimenta-tion into structured deployment, but at different depths.
Instead of a binary story of “adopters vs laggards,” this is a continuum where nearly everyone has crossed the starting line. The gap between “Application” & “Transforma-tion” is where the next wave of competitive differentiation will play out.
When asked where AI has delivered the most measurable business impact, founders are unequivocal: increased productivity, followed by faster time-to-market. Together, 82% of all measurable impact is in some form of “doing things faster.” Only 9% point to increased sales or conversions. The revenue impact, founders believe, is coming but hasn’t landed yet.
Measurable Impact from AI Adoption
For the majority, the standout surprise is faster experimentation. 45% say they can now test 10x more ideas than before.
This reframes AI’s core value from efficiency (doing the same work cheaper) to acceleration (doing far more and far faster). For founders who live and die by their ability to iterate, test, and pivot, AI has expanded the frontier of what a small team can attempt.
What’s been the most unexpected benefit of AI adoption?
Starting with AI is hard because teams are stretched. Scaling it is hard because tools aren't ready.
Bandwidth is the dominant blocker at 53%. Teams are too busy to implement the thing that would make them less busy. It's a classic catch-22: you need capacity to build capacity.
The cultural blocker is fading fast. Only 25% cite cultural resistance to AI adoption. That’s one of the lowest-ranked barriers and almost certainly lower than it was 18 months ago. The ecosystem has stopped arguing about whether to adopt AI. The conversation is now entirely about how.
What's striking is how evenly distributed the remaining blockers are. Technical infrastructure, pace of change, data quality, and budget all cluster tightly between 33–37%. There's no second villain. It’s a market facing several blockers simultaneously, all at comparable intensity.
The barrier to scaling is the limitations of AI tools.
Once founders move from “starting” to “scaling,” the profile changes entirely.
When asked about barriers to scaling AI adoption, the runaway answer is manual context requirements. 54% of founders believe AI tools demand too much hand-holding to work reliably at scale. Waiting for tools to mature (48%) and output quality concerns (48%) are close behind. These are capability gaps.
The blockers to getting started are about what founders have (bandwidth, budget, infrastructure). The blockers to scaling are about what the tools can do.
The two converge on one truth: the ambition exists, the tools haven’t fully caught up.
Nearly half of all founders (52%) are either freezing hiring in specific functions or actively reducing team sizes. Another 23% are in wait-and-watch mode. Only 29% say there’s no change and that AI is augmenting their team, not replacing.
How will AI impact your organisation’s hiring plans over the next 12 months?
For nearly half the respondents, engineering is the #1 cited function for hiring reductions. Marketing is a close second, followed by Customer Support and Operations. “Junior” and “entry-level” roles are bearing the highest brunt.
This restructuring also aligns with the founders’ budget allocation patterns.
CEOs are directing 45% of their AI budget to Engineering, Product, and Data, the functions where adoption is highest and where the tools are most mature. Sales and Marketing receive 21%. HR, Finance, and Operations sit in single digits.
Money and headcount are moving in the same direction: toward the functions where AI delivers, and away from those where it doesn’t yet.
“Replaced almost 60% of junior associates’ job responsibilities.”
// FOUNDER SPOTLIGHT - Early-stage founder
Given a choice between building AI capabilities in-house, buying from vendors, or going hybrid, the hybrid approach is the pragmatic default. Founders want to use vendor tools when they’re good enough, and build custom when they’re not.
But what stands out is the strength of the in-house contingent. More than a third of founders want to build everything themselves. This speaks to the startup DNA: control, customisation, and the belief that proprietary AI capabilities will become a competitive moat.
The Indian startup founder it appears wants to own the AI layer or at least co-own it. This has implications for the vendor ecosystem. Pure-play SaaS AI tools may find that their best customers also want to compete with them.
When asked how much competitive pressure is driving their AI adoption, the majority project calm. This isn't a market paralysed by fear of disruption. It's one moving on conviction.
The reason is simple: it's hard to feel behind when you're already building. With 95% of founders past the exploration phase and in active deployment, competitive anxiety has naturally given way to competitive confidence. They're no longer watching AI from the sidelines.
The 12% who do feel existential pressure are worth watching closely. They likely operate in sectors where AI-native startups are attacking the business model directly.
Founders’ AI priorities for 2026 converge around a
clear two-part bet:
Boosting internal team productivity (75%)
Launching customer-facing AI features (69%)
These two tower above everything else, and the narrow gap between them (just six points) is telling.
Founders are not choosing between internal efficiency and external differentiation. They’re pursuing both simultaneously. Productivity is the proven playbook; embedding AI features in the product is the emerging frontier. The former creates the capacity (faster teams, fewer bottlenecks) that makes the latter possible.
75%
Boost internal team productivity
24%
Build AI talent capabilities
12%
Strengthen AI governance/safety
12%
Modernize data infrastructure
The operating model for AI in early-stage startups has moved away from a top-down mandate to a philosophy of “letting a thousand experiments bloom.”
What Would 10x AI Adoption
We asked founders about the one thing that would accelerate their AI journey the most. From 48 responses, three enablers dominate:
Lower cost with maintained quality is the #1 ask.
Founders want premium model capabilities at price points that work for startup unit economics. Rate limits, trial credits, and cost-optimised inference all come up repeatedly.
Better context integration across tools is the #2 theme.
Founders describe a fragmented landscape where AI tools don’t “know” the company context. The ability for AI to seamlessly connect across CRM, codebase, internal docs, and communication tools - without manual context-stuffing - is seen as the next major unlock.
Talent and culture readiness rounds out the top three.
From training team managers to become AI champions, to building internal AI expertise, to shifting leadership mindsets, founders see human readiness as a key multiplier. The tools are good enough; the question is whether teams are prepared to use them at full capacity.
Tool maturity, data security, and real-world proof points (case studies showing 10x impact in comparable companies) complete this wishlist.
