I recently travelled to Sydney for Ascend’26, the two-day AI and startup festival activated by Google Cloud. I signed up for all the satellite events, including Women in Tech Connect, a breakfast at Antler co-hosted with Blackbird, Scale Investors, Giant Leap and Google Cloud. I nearly didn’t register. I’d assumed a women in tech event wasn’t really meant for me. I was wrong, and I was glad to be there, one of a handful of men in a room of about a hundred.

The key topic was the challenge female founders face in accessing early-stage capital. As an angel investor and a long-time participant in the startup ecosystem, this was not the first time I had heard it discussed.

A genuinely hard problem

It has been a known problem since at least 1999. That year five US academics (Candida Brush, Patricia Greene, Nancy Carter, Elizabeth Gatewood and Myra Hart) launched the Diana Project to study women’s access to growth capital. It is now 2026, and the room still considered the problem unsolved. The Australian numbers agree with them. In 2024, female and mixed-gender founding teams reached a record 27% of deals, but their share of the capital fell to 15%.

A problem that has survived 27 years of research, programs and goodwill isn’t waiting for one clever idea. I’d put it in the category of genuinely hard problems, the kind that need a range of solutions, not a silver bullet. Networks, who sits on investment committees, how capital gets allocated at Series A and beyond. Most of those levers sit on the investor side of the table, not the founder’s.

To be clear about the size of the claim before I make it: this post is about the pitch, and the pitch is only one part of the problem. But it is the part I see.

Don’t take my advice, take the prompt

At the end of the session, one of the presenters asked anyone with ideas on how to help to come and talk to her.

After many years of investing, and now running an angel group, I have numerous ideas on how to tweak a pitch to improve its chances of getting funded. But there is nothing new in them. And I suspect the same advice, repeated by a man, isn’t what that room needed.

My solution is, not surprisingly, AI. I’m an optimist about where this technology is heading, and part of the reason is that AI is already starting to help with genuinely hard problems, as I wrote about in The University of Estonia turned out to be OpenAI. A funding gap that has outlasted 27 years of research is a different kind of hard problem, but the same instinct applies. And as a mathematician who likes abstraction, my meta idea is to skip the advice and hand over the machine that produces it. This post is the prompt. Read it, then use it to set up a project in Claude (or the equivalent in whichever model you prefer) that reviews your pitch the way an investor will.

The project instructions are at the end, ready to copy. Treat them as a starting point, not a finished product. They’re a compressed version of what I use, and the value comes from adapting them to your own sector, your own criteria and your own knowledge base.

I’ve written before about how the angel pitch is now being read twice: once by the investor, and once by the investor’s AI. The models are now good enough to do a pretty good job straight out of the box, if you upload your deck and your pitch video or a transcript. But out of the box isn’t the best version. I’ve been working on my own pitch analyser for a couple of years, and the improvement comes from scoring the pitch against context you load into the knowledge base.

Building the pitch reviewer

Every investor has some version of a criteria list that they tick off, either mentally or on paper. Mine looks something like this:

• Problem: how significant is the problem, and who feels it most acutely?
• Solution: how compelling is the solution, and how directly does it solve the problem?
• Market: is the addressable market large enough to support a venture-scale outcome?
• Team: do the founders have the skills and experience to execute?
• Competition: how crowded is the field, and how defensible is the position, including IP and patents?
• Go-to-market: is there a credible, capital-efficient path to customers?
• Deal terms: are the valuation, raise amount and terms appropriate for the stage?

Ask the AI to score the pitch against each one, and to say why. The reasons matter more than the scores. A low score on team, explained, is worth far more than a high score on market, unexplained. (If I had to pick one criterion, it would be the first. The problem statement is the slide I care most about.)

Then add some best-practice content. For years I’ve been telling people I like the comprehensive angel investing material produced by Seraf. It was co-founded by Christopher Mirabile, a former chair of the Angel Capital Association, and Ham Lord. Seraf is a software business that uses content as its go-to-market, and the content is very good. I’ve summarised a lot of it for my own knowledge base, and I’d suggest you read it and build your own notes.

With that context loaded, the AI does two more useful things. It suggests questions that sharpen the investment thesis, and it lists the due diligence questions your materials don’t yet answer. Those are the questions you’ll be asked in the room, so it’s better to meet them at your desk first.

Then keep tweaking the pitch, and rerun it, until you’ve covered off as many issues as you can.

In my experience it’s straightforward to get a pitch to the 80–90% level. A hundred percent is an aspiration only. But each 1% better can be the difference between a yes and a no. That last stretch is a mix of science and art, and the AI only takes you part of the way. Much of the improvement can only be gained by pitching to real investors, listening to their questions, and iterating.

Whoever has the gold sets the rules

There’s another concept I’d include, and the mathematician in me likes it because it works as an axiom. The golden rule: whoever has the gold sets the rules. It’s another version of “the customer is always right”, even when they’re wrong.

Angel investing is a personal money thing. It’s discretionary, and everyone has the right to decide how they invest their own money, for their own reasons, by their own rules. VC is a bit more complicated. A VC is investing other people’s money, with a fiduciary duty to make money for those investors, and a mandate (the rules) they have to follow. So female founders pitching to an angel and to a VC are pitching to two different kinds of customer. One sets their own rules. The other has rules set for them.

You don’t have to agree with the rules. But you do have to know them.

The question behind the question

For female founders, I’d add one more piece of context to the knowledge base: the known list of issues women face when raising capital. Not to relitigate them, but to spot the moments in a pitch that can be reframed for the gold.

In my opinion, that’s where the bias lives. Not in the 80–90% that a well-worked pitch gets to, but in the incremental percentages after it, where a yes and a no can be separated by the framing of one or two answers.

The best example I know comes from Dana Kanze and her co-authors, who put it more sharply than I can in the title of their paper: “We ask men to win and women not to lose.” Their study of the investor Q&A at TechCrunch Disrupt from 2010 to 2016 found a clear pattern. Investors tended to ask male founders promotion questions (how will you acquire customers?) and female founders prevention questions (how will you hold on to the customers you have?). Founders answered in the frame they were asked, and every additional prevention question was associated with roughly $3.8 million less in aggregate funding. The useful part is the experiment that followed. Founders who answered a prevention question with a promotion answer closed much of the gap.

That is exactly the sort of thing a well-briefed AI can find in a transcript, and the best transcript comes from a real investor meeting. Feed it one, and it will come back with the feedback no investor gives you on the way out the door: the three moments you defended when you could have expanded.

There’s a fair objection here. The models learn from a record that over-represents the male lens, so an AI reviewer can carry the same bias as the investors it’s meant to prepare you for. I don’t think that sinks the idea. The investor in the room is biased too, and the fix is the same for both: fixed criteria, a reason for every score, and an explicit list of patterns to look for. And you can test it. Run the deck twice, once presenting the founder as a woman and once as a man, and compare the scores and the questions. If they differ, you’ve found the bias, and you know exactly where it will land in the room.

The same tool works on my side of the table

And this part is for angels rather than founders. Kanze’s study found that female investors asked the same lopsided questions as male investors. So this is an investor problem, mine included.

I’ve argued before that being able to articulate why a deal will fail is what separates a selection process from a hunch. It turns out the same discipline is also a fairness mechanism. A fixed list of criteria, scored the same way every time, is harder to bias than a conversation. Run your own investment committee questions through the same reviewer and I’d expect it to find a few leaning the wrong way. I expect it would find some of mine.

For an angel writing many small cheques, that matters more than it first appears. Every deal I mark down for the wrong reason is a deal whose upside I never see, and the portfolio maths only works if the good deals get in.

Before the meeting

One last step. Before pitching to any investor, I would ask the AI project to review the investor: their website, their stated thesis, their portfolio. What have they backed? What do they say they want? What does their mandate rule out? Whoever has the gold sets the rules, so read the rules first and tailor the pitch to that customer.

This is one small part of a much bigger problem, and the bigger levers sit with investors and with the people who allocate capital to them. But pitching is the part I see, and it’s a small but important part of the journey. That was my answer to the invitation at the end of the session. It seemed better as a prompt than as a lecture.

Copy-paste project instructions: the pitch reviewer

A starting point, to adapt to your own sector, criteria and knowledge base.

You are an experienced early-stage investor reviewing a startup pitch ahead of meetings with angel and venture capital investors. I will upload a pitch deck and either a pitch video or a transcript.

1. Assessment. Score the pitch from 1 to 5 on each a short explanation for every score:

• Problem: its significance, and who feels it most acutely
• Solution: how compelling it is, and how directly it solves the problem
• Market: whether the addressable market can support a venture-scale outcome
• Team: whether the founders have the skills and experience to execute
• Competition: competitive intensity and defensibility, including IP and patents
• Go-to-market: whether there is a credible, capital-efficient path to customers
• Deal terms: whether the valuation, raise amount and terms suit the stage

2. Investment thesis and diligence. Drawing on the best-practice notes in the knowledge base, list two sets of questions:

• those that would sharpen the investment thesis
• the due diligence questions the materials leave unanswered

3. The golden rule. Whoever has the gold sets the rules. Angels invest their own money at their own discretion. Venture capital investors have a fiduciary duty to their investors and a mandate to follow. Flag anything likely to fall outside either set of rules.

4. Known challenges for female founders. Using the notes in the knowledge base, review the deck and transcript for points where the pitch is exposed to documented patterns in how investors assess women-led startups. Examples include:

• answering risk-focused questions defensively rather than pointing to growth
• understating ambition or scale, so the business reads smaller than it is
• a market investors may be unfamiliar with or undervalue, particularly products serving women
• technical or commercial credibility that is assumed rather than demonstrated
• a raise or valuation pitched too conservatively

For each point, explain the risk and suggest a reframe that keeps the pitch accurate. Then check your own assessment for the same patterns. If any score or question would differ were the founder a man, say so and explain why.

5. Priorities. Rank the five changes that would most improve the pitch.

6. Investor tailoring. When I name a specific investor, research their website, stated thesis, portfolio and, for a fund, its mandate. Then tell me how to tailor the pitch to them.

Be direct. I would rather hear the hard questions here than in the room.

Richard Moore – MooCoo Ventures

Richard Moore is co-founder of MooCoo Ventures, an angel syndicate that co-invests alongside Brisbane Angels, one of Australia’s most active angel groups. He has made over ninety personal angel investments since 2013.

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