Why Blackbird and AirTree Passed: The ANZ AI Explainability Gap

Three AI explainability startups pitched to Blackbird's portfolio of over 100 companies last quarter. All three walked away empty-handed. Two more got meetings with AirTree — Australia's two largest venture capital firms only to hear the same response: "Interesting technology, but we're not sure about the business case."

This pattern isn't unique to these founders. It's playing out across the ANZ market, where Australia's artificial intelligence ecosystem is entering a rapid expansion phase but investors remain skeptical of AI explainability companies.

Here's what's really happening and why the smartest founders are already adapting.

The Technical Brilliance Problem

Standard advice says a 10–12 slide deck is enough, but for AI startups, that's often too thin to answer the real questions investors have. You typically need 15 core slides to properly cover the problem, solution, market, traction, technical depth, and compliance.

ANZ AI explainability founders make the same mistake repeatedly. They lead with their technical breakthrough — SHAP values optimization, novel attention mechanisms, or groundbreaking LIME implementations. The pitch becomes a computer science lecture.

Blackbird and AirTree partners don't need to understand your algorithm. They need to understand why enterprise buyers will pay for it.

The best venture capital pitch decks have problem slides that are strong enough to make the investor really, really want to meet with you. If you can highlight a customer, and the pain point they had, super awesome! Use this to lead into the solutions slide, where you can explain how you helped them.

The founders who get funded flip this sequence. They start with the business pain that explainability solves, then reveal the technical solution.

The Local Investor Psychology

The firm seeks to invest in companies operating in the technology, space, healthcare, consumer, medical devices, autonomous robotics, artificial intelligence, infrastructure, clean energy systems, and quantum technology sectors across Australia and New Zealand, but there's a crucial difference in how ANZ VCs evaluate AI companies compared to their Silicon Valley counterparts.

U.S. investors have seen AI explainability companies succeed at scale. ANZ investors haven't — yet. This creates a validation gap that technical demos can't bridge.

Investors make decisions based on return and risk: How big of a return could they receive? And what might prevent them from receiving that return? Most VCs want a 10-20x return. As a founder, your job is to convince investors your startup offers the greatest potential return, with the least amount of risk.

For ANZ AI explainability startups, this means proving three things: market timing, defensibility, and path to $10M+ ARR within 36 months.

The Missing Business Translation

The pattern we see in successful pitches is clear: founders who get funded have cracked the translation problem. They don't pitch "model interpretability for machine learning systems." They pitch "compliance automation for enterprise AI deployments" or "risk mitigation for financial institutions using AI."

Ensuring Transparency and Explainability: When AI approves loans or flags fraud, leaders must understand the reasoning. AI's "black box" problem creates real risks. Transparency means explaining how AI reached its conclusions—crucial for responsible use.

This isn't about dumbing down the technology. It's about leading with business outcomes that C-suite buyers understand, then revealing the technical differentiation that makes those outcomes possible.

What Changes the Game

The breakthrough happens when founders demonstrate three elements in their first five slides:

1. Regulatory urgency: EU AI Act compliance deadlines, APRA requirements, or other regulatory drivers creating immediate budget allocation.

2. Revenue proof: Enterprise customer paying $50K+ annually, with clear expansion path to $200K+ contracts.

3. Local validation: Australian or New Zealand reference customer willing to go public with results.

Why it matters: Venture investors only back products that are "painkillers," not "vitamins." Detailed quantification, such as "50% of insurance claims processed manually at $15B/year in overhead", frames urgency.

The Funding Formula That Works

Successful AI explainability pitches in ANZ follow this narrative arc:

Minutes 0-2: Establish business problem with enterprise customer example
Minutes 2-5: Demonstrate revenue traction and expansion potential
Minutes 5-8: Reveal technical differentiation and competitive moats
Minutes 8-10: Show market timing and regulatory tailwinds

The technical depth still matters, but it comes after you've established commercial validation.

Why This Matters Now

Australia's artificial intelligence ecosystem is entering a rapid expansion phase. The AI market in Australia is projected to grow from about AUD 4.8 billion in 2024 to nearly AUD 295.8 billion by 2034, growing at a CAGR of 51% from 2025 to 2034.

The window is opening for AI explainability companies, but only those who can articulate clear business value will capture it. Technical excellence is table stakes — commercial storytelling is the differentiator.

The founders raising Series A rounds in 2026 won't be those with the most sophisticated algorithms. They'll be those who translated algorithmic complexity into funding success by making AI explainability instantly clear to the people writing the checks.

If you're an AI explainability founder preparing to pitch ANZ investors, the narrative framework matters as much as the technology. Learn how to transform technical complexity into compelling investor stories that secure funding rounds and close enterprise deals at Infrairis.