Three Founders, One Week in Singapore: The AI Readiness Gap Is Not What You'd Expect
Spend a week walking through Singapore's business districts — Tanjong Pagar, one-north, Marina Bay — and the AI narrative feels settled. Every tech company has an AI strategy. Every co-working space has a whiteboard with an AI workflow on it. Cursor, Perplexity, Claude, Gemini. It feels like the transition is already over.
Then step into a conversation with someone running a real business — construction, training, financial services — and the picture changes completely.
Three Meetings, Three Realities
Last week I had separate conversations with three founders. They operated in completely different industries, but they were all wrestling with the same question: what does AI actually mean for my business?
Founder A runs a fintech company — digital lending, serving SMEs across Malaysia and Singapore. Sophisticated product, strong regulatory relationships, Series A funded. When I asked about AI adoption, the answer was careful and measured: "We know it's important. We're watching what the regulator says. We're testing a few things internally, but we're not moving until the compliance picture is clearer."
Founder B runs a construction business — mid-size, specializing in commercial fit-outs across Singapore and JB. Not a tech company by any definition. When I asked the same question, I got a completely different answer: they'd already automated their estimation process, were using computer vision to flag defect patterns in site photography, and were running AI-generated competitive bid analysis. "We have no choice," he told me. "Margins are too thin. If I can cut estimating time by 60%, that's real money."
Founder C runs an offline corporate training business — workshops, leadership programs, facilitated sessions for large enterprises. When I asked about AI, she'd rebuilt her entire assessment methodology around it. Every participant gets a personalized development report post-session. Her facilitators use AI to prepare customized case studies 48 hours before every engagement. The product is fundamentally different than it was 18 months ago.
The Surprise
If you'd asked me before those meetings to rank those three founders by AI decisiveness, I would have put fintech first, construction last, training somewhere in the middle.
I had it exactly backwards.
The fintech founder — who works in the most tech-adjacent industry, whose investors are definitely talking about AI, whose competitors are almost certainly experimenting — was the most hesitant. The construction founder and the training founder were moving aggressively.
Why the Gap Exists
The fintech founder's caution isn't irrational. Digital lending touches credit decisions and customer data in ways that are genuinely regulated. The regulator hasn't issued clear AI guidance. Getting it wrong has real consequences — license risk, customer harm, regulatory action. The hesitation is logical given the incentive structure.
But it reveals something important: in heavily regulated industries, the same compliance infrastructure that creates competitive moats also slows AI adoption. The fintech company is simultaneously more protected from new entrants and slower to implement the tools that could sharpen its edge.
The construction and training founders face no such constraint. There's no regulatory approval needed before using AI to price a renovation tender or personalize a leadership development report. The absence of guardrails — which might look like a disadvantage — is actually enabling faster movement.
What the Decisive Founders Have in Common
Looking at Founder B and Founder C, two things stand out.
They started with a specific, expensive problem — not an AI strategy. Construction estimation is time-consuming, error-prone, and directly tied to whether you win jobs. Corporate training assessment is bottlenecked by facilitator time. Both founders found one painful process and used AI to fix it. The broader AI strategy came later, if at all.
They weren't waiting for permission. Neither founder asked whether their industry "should" use AI. They asked what problem AI could solve right now, ran a small experiment, saw the result, and kept going. The bar wasn't "prove AI is transformative." The bar was: does this make Tuesday easier than last Tuesday?
What This Means for How We Invest
We spend a lot of time at Pertama Ventures thinking about AI as a product category — which AI-native companies will win, which applications will have durable margins.
But this week's conversations reminded me of something different: AI is also changing the competitive dynamics inside every industry, including the industries we invest in. And the change isn't correlated with how tech-adjacent the industry is.
The construction company that automates estimating has a structural cost advantage over competitors who don't. The training company that delivers personalized post-session reports can charge more and retain clients longer. Neither is an AI company. Both have AI-driven competitive advantages that compound over time.
For founders we back: we're not looking for an AI strategy. We're looking for a specific AI use case that makes your business harder to compete with. That's a different question — and usually a more important one.
For traditional businesses watching from the sidelines: the gap between decisive and cautious companies is widening faster than most people realize. The construction founder's competitors haven't automated their estimating yet. That window won't stay open.
