Australian universities publish impressive AI research. Our academics contribute to top-tier conferences. Research groups here have developed genuinely novel techniques in computer vision, natural language processing, and reinforcement learning.

Yet Australia has produced very few successful AI product companies compared to the volume and quality of research output. The gap between research capability and commercial deployment is widening, not closing.

This isn’t an accident. There are structural reasons why Australian AI research doesn’t translate into Australian AI companies. Understanding those reasons might help us fix the problem.

The Research Capability Is Real

Let’s establish that the research foundation exists. Australian institutions are genuinely strong in AI:

CSIRO’s Data61 conducts world-class applied research. The Australian Institute for Machine Learning at Adelaide produces influential work in computer vision. UTS, UNSW, and Melbourne all have strong AI research groups.

Australian researchers regularly publish at NeurIPS, ICML, CVPR, and other top conferences. The per-capita research output compares favorably to much larger countries.

This isn’t about capability. We have the talent and institutional support to do excellent AI research.

The Commercialization Problem

Where we fail is translating research into products and companies.

A 2025 analysis by CSIRO found that Australian universities produce about 3.2% of global AI research publications but Australian-founded companies represent only about 0.8% of global AI company value.

That’s a 4x underperformance on commercialization relative to research output.

Compare this to Israel, which produces about 1.1% of global AI research but whose companies represent about 3.2% of global AI company value. They’re overperforming on commercialization by 3x.

Why the difference?

The Funding Valley of Death

Most Australian AI research is funded through university grants or CSIRO programs. That funding supports publications, not product development.

When a researcher wants to commercialize their work, they enter a funding valley. They need capital to build a prototype, validate market fit, hire engineers, and iterate toward a viable product.

Australian seed funding for deep tech is limited. Investors want to see more traction than an academic prototype provides. But you can’t get traction without building a product, which requires capital.

This valley kills most commercialization attempts before they start.

By contrast, countries like the US, UK, and Israel have more developed deep tech investment ecosystems that fund pre-product AI research commercialization.

The Talent Retention Problem

Researchers who develop valuable AI techniques often get recruited by international companies or move overseas to start companies in ecosystems with better funding and talent access.

Australia trains the researchers, funds the early research, then loses them to San Francisco, London, or Singapore.

This isn’t about individuals making bad decisions. They’re responding rationally to better opportunities elsewhere.

But it means Australian research capability doesn’t translate into Australian company creation.

The Overfocus on Pure Research

University incentives reward publications and citations, not company creation. Researchers advance their careers by publishing in top conferences, not by building commercial products.

There’s some support for entrepreneurial researchers through commercialization offices, but it’s secondary to the core mission of academic research.

The result is that most research stays in academic contexts rather than being pushed toward applications.

Countries with stronger commercialization track records have universities that more actively encourage and support research commercialization as a core mission, not an optional add-on.

The Market Size Question

Australia’s domestic market is small. For AI products targeting enterprises, the total addressable market in Australia is often insufficient to build a venture-scale company.

Successful AI companies need to target international markets from the start. But that requires different go-to-market strategies, business development capabilities, and often physical presence in major markets.

Researchers starting companies rarely have that skillset or network. And there’s limited local expertise and capital to support international expansion.

The Practical Application Gap

A lot of AI research is fascinating but not obviously applicable to near-term commercial problems.

Research focuses on advancing the state of the art. Industry needs solutions to existing problems that can be deployed with current technology.

There’s often a gap between “this is a novel technique that improves benchmark performance by 2%” and “this solves a real business problem in a way customers will pay for.”

Bridging that gap requires deep understanding of specific industries and their problems. Researchers don’t usually have that domain knowledge, and there’s limited infrastructure to connect researchers with domain experts.

What Would Actually Help

Several interventions could close the research-deployment gap:

More deep tech seed funding. Australia needs more investors willing to fund AI research commercialization at the pre-product stage. This is high-risk capital that most current investors avoid.

Their Melbourne team and similar consultancies sometimes help bridge this gap by partnering with research teams to build initial commercial prototypes, but this is ad hoc rather than systematic.

University incentive reform. If universities rewarded commercialization outcomes alongside publications, more researchers would pursue that path.

Industry-academic partnerships. Structured programs that connect researchers with industry partners who have real problems and can provide funding, domain expertise, and commercial guidance.

Talent retention programs. Making it more attractive for researchers to stay in Australia and commercialize here rather than moving overseas. This probably requires both funding improvements and better access to technical talent.

Government procurement. Using government purchasing to create demand for Australian AI solutions, particularly in areas like healthcare, agriculture, and infrastructure where local solutions may have advantages.

International Examples

Israel’s success in translating research to companies comes partly from strong military-commercial pipelines where defense-developed technology transitions to civilian applications.

The UK’s recent improvement comes from targeted deep tech funds like British Patient Capital and initiatives like the Alan Turing Institute’s commercialization programs.

Singapore actively recruits international AI researchers and provides generous funding for commercialization through entities like SGInnovate.

Australia could learn from these models without copying them exactly. We need approaches that fit our context.

The Opportunity Cost

Every AI research breakthrough that doesn’t get commercialized in Australia is a missed opportunity for economic value creation, job creation, and capability building.

When Australian researchers move to the US to start companies, we lose not just the company but the ecosystem development that companies create: hiring, training, mentoring the next generation, attracting capital and talent.

The compound effect over time is that our research capability doesn’t strengthen our commercial ecosystem the way it should.

Not All Research Should Be Commercialized

To be clear: pure research has value independent of commercialization. Not every research project should or could become a company.

The problem isn’t that we should commercialize 100% of research. It’s that we probably should commercialize more than we currently do, and we should make it easier for researchers who want to pursue that path.

What Researchers Can Do

If you’re a researcher interested in commercialization:

Focus on real problems. Partner with industry to understand actual needs, not just interesting technical challenges.

Build prototypes that demonstrate value. Academic code and production-ready products are very different. You need to close that gap.

Find commercial co-founders. Your technical skills are necessary but not sufficient. Partner with people who understand markets, sales, and business operations.

Talk to customers early. Don’t wait until you have a finished product to test market fit.

Look for pre-seed funding specifically designed for deep tech. It exists, but you have to know where to look.

The Bigger Picture

Australia has a choice. We can continue producing excellent AI research that gets commercialized elsewhere, or we can build the infrastructure and incentives to capture more of that value locally.

This isn’t about nationalism. It’s about maximizing the return on our research investment and building sustainable competitive advantage in technologies that will define the next several decades.

The gap between our research capability and commercial deployment is a problem. But it’s a solvable problem if we choose to solve it.

That requires coordinated effort from universities, government, investors, and industry.

Right now, we’re not doing enough. And the gap keeps widening.

We can do better. The question is whether we will.