Craig Astill IP documents the transition from observation to invention, and from concept to registered intellectual property.
Investment has always involved uncertainty.
No matter how experienced the investor, there is a point where a decision has to be made using incomplete information.
Is the market really as large as the founder claims?
Are the financial assumptions realistic?
Is the competitive advantage genuinely defensible?
Does the management team understand the risks?
And perhaps most importantly: what have we missed?
I’ve been thinking about this considerably while we’ve been developing the Investor Due Diligence capability within IQ Pitch.
Because I don’t think AI’s most interesting role in investment is telling investors what to invest in.
I think it may be helping investors ask better questions before they make that decision.
Investment due diligence requires information from many different areas.
Financial performance.
Market size.
Competition.
Technology.
Customers.
Management.
Intellectual property.
Regulatory exposure.
Capital requirements.
Valuation.
Each individual piece may be understandable.
The difficulty is connecting them.
A company’s financial projections might look reasonable until compared with its customer acquisition assumptions.
Its market opportunity might appear enormous until competitors are properly mapped.
Its technology might be impressive while its commercial moat remains weak.
Good due diligence isn’t simply collecting more information.
It’s understanding the relationships between information.
There is only so much information a human team can examine within a reasonable amount of time.
That naturally creates prioritisation.
AI changes some of those economics.
A system can examine a pitch deck, interrogate assumptions, identify inconsistencies, compare claims and organise questions for further investigation far more quickly than traditional manual analysis alone.
That doesn’t make the machine the investor.
It makes the investor better equipped.
This distinction is important.
IQ Pitch originally began from the founder’s side of the table.
A founder uploads a pitch and receives an assessment of investor readiness: strengths, weaknesses, financial realism, market opportunity, competitive moat and areas likely to concern investors.
But there is another side to that equation.
What does the investor need to know?
That’s where Investor Due Diligence becomes interesting.
Instead of simply asking whether a founder has presented the company effectively, the system can begin asking whether the underlying investment proposition withstands scrutiny.
Those are two very different questions.
A brilliant presentation can represent a weak investment.
A poorly presented company can potentially represent an excellent one.
The challenge is separating presentation quality from investment quality.
I don’t think useful investment AI should simply produce another summary.
Investors don’t need artificial intelligence to shorten a pitch deck.
They need it to challenge assumptions.
What happens if growth is slower?
What happens if margins don’t develop as forecast?
Who are the competitors the founder hasn’t mentioned?
How defensible is the technology?
How much additional capital could actually be required?
Which assumptions determine the valuation?
Where does the investment thesis break?
Those questions are much more valuable.
There will always be things a model struggles to understand.
Founder character.
Trust.
Timing.
Industry relationships.
Cultural dynamics.
The intuition developed after decades inside a market.
AI shouldn’t pretend those things don’t matter.
The objective should be combining computational analysis with human judgement.
Machines can expand the investigation.
Humans remain responsible for the decision.
We’re entering an interesting period in investment.
For years, access to information created an advantage.
Increasingly, almost everyone will have access to extraordinary amounts of information.
The advantage will shift toward understanding it.
That’s why I think the future of investment technology won’t simply be about finding more opportunities.
It will be about understanding opportunities more deeply.
And perhaps the most useful thing AI can tell an investor isn’t:
“You should invest in this company.”
It’s:
“Before you invest, here are the questions you haven’t asked yet.”
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