How VCs Evaluate Vertical AI Startups
With Kyle Lui, General Partner at Bling Capital
Kyle Lui is a General Partner at Bling Capital, where he leads early-stage investments in product-driven founders across SaaS, consumer, digital health, and fintech. Before Bling, Kyle was a Partner at DCM and two-time founder (his startup ChoicePass was acquired by Salesforce). Bling has done something rare in VC: open-sourced its entire DD playbook. This week on Verticals, Kyle joins us to explore how his diligence framework is evolving in the AI era — and how founders and investors should update their priors.
Today’s Episode
Most VCs have spent the majority of their career in an environment where SaaS was so ubiquitous that strong gross margins were implicit, market sizing centered around a clear IT budget, and “hair on fire” problems outside of what software could do were irrelevant. In the AI era, every one of those shortcuts breaks down. AI margins range from negative to 90%. The TAM for an AI company might capture 5-10x as much as SaaS did. And the standard retention expectations and benchmarks may not apply.
Bling has published a seven-part diligence series that walks through exactly how they stress-test these questions — from hair-on-fire problem definition to customer segmentation, market sizing, and financial modeling. The shared thread is that diligence isn’t a test to pass — it’s an alignment exercise between founder and investor on a believable plan to reach venture scale. Kyle walked us through how each piece of that framework applies to AI-native companies, and where the conventional playbook may be falling apart.
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The Hair-on-Fire Test Gets Harder
Bling starts every diligence process with a deceptively simple exercise: describe the hair-on-fire problem you’re solving, then show — step by step — how the current workflow works and how your product replaces it.
In Vertical SaaS, this was often straightforward: manual process, expensive, error-prone — software automates it. In Vertical AI, the exercise is more revealing because the “10x better” claim is harder to pin down. An AI immigration law firm compressing 40 hours of attorney work into four is a clear 10x. But a voice agent that handles 80% of customer calls and escalates the rest — is that 10x better, or is it delivering more work to the customer when escalations go sideways?
Kyle's succinct advice: if you can’t document the current behavior and the future behavior of customers in concrete steps, with clear ROI math — even if those steps are likely to evolve down the road — you don’t yet understand your own product’s value.
ICP Segmentation is Increasingly Important
Bling’s customer segmentation exercise requires founders to define segments that are mutually exclusive and collectively exhaustive — then attach average customer value, margin, and a go-to-market strategy to each. The most common mistake they see is reliance on top-down TAMs. Especially when you’re offering a net-new value proposition, a bottoms-up breakdown of ICP is critical.
Segmentation is more important in the era of AI. A roofing company with a $30K ACV wouldn’t touch voice agents two years ago — every lead was potentially worth $30K, and the ROI for humans vs. 80%-resolution AI was clear. Today, with a greatly improved voice stack, that same company might adopt at 95% resolution. But let’s examine an adjacent segment: residential HVAC. With its $5K average tickets, different math is at play. With every lead less valuable, it’s more of a volume game — meaning the bottleneck is less conversion and more lead processing. That means, they probably were open to adopting Voice AI sooner.
So if you’re sizing “home services voice AI” as a homogenous market, you’re hiding the fact that your actual addressable segment today might be 10% of that. Bling’s framework forces you to show which segments you can win now, which you’ll expand into, and what has to change for each.
Getting Venture-Scale on Gross Profit
Kyle runs a specific exercise with every company Bling backs: how does this business get to $100M and $500M in gross profit? Not revenue — gross profit. The framework is a fill-in-the-blank sentence: “To get to $100M in gross profit per year, we need X customers paying us $Y per year, where X represents K% of the market.” If you can’t complete that sentence with credible inputs — in Kyle’s mind — you don’t have a venture-scale business.
In the AI era, Bling has added a third tier: $1B in gross profit. Their rationale is that multiples have compressed, competition has intensified, and the power law is steeper than it was two years ago. Founders sizing their TAM around top-line revenue or GMV instead of gross profit is more dangerous than ever because AI inference has created a wildly variable COGS situation from startup to startup.
The gross profit lens also reveals which markets are bigger than SaaS economics suggested. Bling portfolio company Alma, an AI-native immigration law firm, captures the margin delta when AI compresses 40 hours of attorney work into four — without proportionally lowering the price. Miniva, in high-volume manufacturing, replaces quality control hires that factories can’t find. Part Bay is building a vertical AI marketplace for used auto parts — a category most VCs would call too niche, until you size it in gross profit against the labor and inefficiency it replaces. A vertical play that looked too small at $80M in software TAM might now be venture-scale.
The D+ Financial Model
The final piece of the playbook might be the most counterintuitive. Bling’s financial modeling exercise explicitly calls for a “D+” model — a skeletal framework that captures 80% of the signal in 30–60 minutes. It covers four key questions:
Who are you hiring, when?
When does revenue start?
How does burn scale?
How long does the money last?
While the model is simple, there are some nuanced considerations, especially when it comes to the burn question. On some boards, Kyle is seeing companies now combine token spend and headcount into a single R&D line item — a shift unthinkable two years ago. If your negative margins come from compute costs on a falling curve, downstream investors will tolerate it. But if you’re burning because you’ve hired an army to deliver the service, Kyle’s question is pointed: “What’s your actual advantage against the non-AI-native companies?” As we explored in Deus Ex CapEx, the uncertainty around the steady-state of AI margins is more pervasive than ever.
The Takeaway for Vertical Founders
Bling’s playbook offers some great tactical advice for thinking about how you position your business going into a Seed process. The fundamentals remain as important as ever: solving a specific problem with a quantifiable ROI, rationally segmented customers with scalable unit economics, a credible venture-scale gross profit expansion story, and a financial model that shows an understanding of the tech-economics underlying your business. But for AI-native Vertical AI founders, the SaaS-era shortcuts for evaluating those fundamentals — top-down TAMs based on IT budgets, assumed gross margin opportunity, constraints on solvable “hair on fire” problems — must all be thoughtfully reconsidered.
See you next week.
Key Moments from this Episode
00:00 — Intro
03:26 — The frameworks top investors use to spot breakout startups
07:19 — What product-market fit actually means now
13:12 — Why “AI services” are changing software economics
18:05 — The hardest question in AI
24:36 — The hidden advantage of routing across multiple models
31:04 — Are AI models becoming a commodity?
35:47 — How AI companies tap into labor budgets
39:16 — Why “too niche” markets might become massive in AI
40:23 — Will vertical AI become winner-take-all like SaaS?
45:14 — The biggest mistake investors are making right now
52:21 — The niche AI market that surprised even their LPs
