Big Idea + Report of the Week

• How big is AI spend, really? Bigger than you think.

• If funding is up, why are female founders losing VC share?

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AI Infrastructure Spending vs. Historical Megacycles

Fresh analysis comparing AI infrastructure investment to the largest capital mobilisations in U.S. history shows just how fast the AI economy is scaling and how far it still has to go.

AI vs. historical megacycles

• World War II remains the biggest economic mobilisation ever at 37.8 % of GDP, followed by WWI (12.3 %), the New Deal (7.7 %), and the railroad boom (6.0 %).

• AI infrastructure spend is already at 1.6 % of GDP — bigger than the entire telecom bubble at its peak (1.2 %).

Corporate CAPEX is exploding

• Microsoft: $140 B

• Google: $92 B

• Meta: $71 B

OpenAI alone is reportedly planning $295 B by 2030, signalling the scale of the compute arms race.

What 2030 could look like

If OpenAI represents ~30 % of total AI infra spend, the market would hit $983 B annually by 2030, roughly 2.8 % of U.S. GDP.

Hitting the railroad era’s 6 % equivalent would require ≈ $2.1 T per year, meaning today’s tech giants would need to 5–7× their current spend.

AI infra spending has already become one of America’s largest investment categories, and it’s only in the early innings. While still far from the scale of wartime mobilisation, the compounding CAPEX cycle from OpenAI, Microsoft, Google, and Meta could reshape the U.S. economic landscape for the next decade.

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Female‑Founded Startups in Europe – Funding Trends

VC funding for female‑founded startups in Europe ticked up in 2025, but the picture is more concerning beneath the headline number.

• Women‑led startups raised €10.9 B this year (up from €10.8 B in 2024).

• Their share of total VC deal value fell to 17.8 %, down from 18.5 % last year – the second consecutive annual decline.

Deal activity

• Deal counts for women‑led startups declined alongside the broader market.

• Female founders remain under‑represented versus 2024 levels, both by volume and value.

• The gap is widening, not closing.

All‑female founding teams

• At the current pace, they’re unlikely to cross €1 B in funding this year – the first time that’s happened since 2020.

• Their share of total VC funding has dropped to 1.2 %, the lowest level in nearly a decade.

AI sector (the most capital‑rich)

• Female founders accounted for 17.1 % of AI deal count – the lowest share since 2016.

• They captured only 12.9 % of AI deal value, despite record investment flowing into the category.

Structural reason – who controls the checks:

• Only 15 % of decision‑makers at European VC firms (€50 M+ AUM) are women.

• Just 8.3 % of firms have a majority of female check writers.

Notable recent rounds

• Binance’s $2 B investment from Abu Dhabi‑based MGX (largest female‑founded deal this year)

• Verdiva Bio’s $411 M Series A led by General Atlantic and Forbion

• Synthesia’s reported $200 M GV‑led round, valuing it at $4 B

Funding for Europe’s female founders is no longer collapsing — but it’s quietly losing share. Without more women in GP and decision‑making roles, rising totals may continue to mask a shrinking slice of the VC pie.

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Competitive Analysis with ChatGPT

Most founders assume they know who their competitors are, but the real competitors—those stealing traffic, attention, and mindshare—often aren’t on the radar. ChatGPT can help surface them quickly.

1. Identify real competitors

Prompt examples:

• “Who are the biggest competitors for [your brand]?”

• “Which sites rank for the same [topic/keyword] as [your brand]?”

• “What are the strengths and weaknesses of [competitor] compared to [my company]?”

2. Run top‑level SEO and content‑gap audits

Prompt examples:

• “Provide a keyword overlap audit between [your brand] and [competitor] for [5 priority topics].”

• “Which ‘how’, ‘where’, or ‘when’ queries does [competitor] dominate?”

3. Wrangle raw data

• Export URLs (e.g., via SEO Pro Chrome extension).

• Prompt: “Categorize these URLs by type (news, evergreen, affiliate, live blog) and identify trends.”

4. Analyze headline patterns

• Upload headline samples from two sites.

• Prompt: “Compare tone, structure, and common framing patterns. Which are most engaging and why?”

5. Turn insights into experiments

Use ChatGPT’s summaries as hypotheses, then validate with analytics or search data. The workflow applies across SaaS, e‑commerce, newsletters, and more.

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Email Sequence for Product Launches

Data from 20+ cohorts (10 000+ students) shows that nearly 20 emails are needed on average before a prospect buys. The key is a split‑track sequence:

Part 1 – 100 % Education (Emails 1‑10)

1. Plain pitch – what you sell, the problem it solves, how to buy.

2. Who it’s for – 3 archetypes you serve best.

3. 10 biggest problems – why common fixes fail and why your approach is different.

4. Desired outcomes – top 3 results your audience wants (and how you help).

5. Your story – past struggles, transformation, credibility.

6. Stop/Start – what to quit doing, what to do instead.

7. Myths – common false beliefs + reframes.

8. Quick tips – actionable advice they can use today.

9. Mistakes – traps people fall into, how to avoid them.

10. Templates – share one, hint at more inside your product.

Part 2 – 100 % Sales (Emails 11‑20)

11. Everything included – break down your product clearly.

12. Bonuses – extra perks they get.

13. Testimonials roundup – social proof at scale.

14. Future pacing – where will they be in 1 year (with vs. without your product)?

15. Testimonial deep dive – one student’s full journey.

16. Tale of 2 archetypes – buyer vs. non‑buyer outcomes.

17. On‑the‑fence survey – ask why they haven’t bought (time, readiness, cost).

18. Behind the scenes – peek into modules/resources.

19. Disappearing bonus – add urgency with a 48‑hour perk.

20. Disappearing discount – final 48‑hour price incentive.

Why it works

• Buyers need repetition; the average person doesn’t convert after a few nudges.

• The first half builds trust and authority; the second half applies pressure and urgency.

• If you’re not willing to craft 20 emails, don’t be surprised if sales stay flat.

Map your next launch sequence using this exact 20‑email framework: pure education first, then direct selling. More emails = more sales.

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Idea Validation Frameworks

Most B2C ideas get validated in one of four ways:

1. Signal Aggregation – Small experiments that provide evidence (signals) of potential success before building a full product (e.g., landing‑page sign‑ups, ad click‑throughs, interview feedback).

2. Strong Beta – Early traction with a beta product; metrics vary (user numbers, retention for consumer apps; revenue for marketplaces). Emphasises speed over polish.

3. True Fans – Identify a small group (20‑50) of fanatic users who would be upset if the product disappeared. Passion outweighs raw numbers.

4. Visionary – Founder has a clear vision and personal connection to the problem; the plan is laid out from the outset.

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Exit Velocity Index (EVI) – Measuring Startup Efficiency

John Rikhtegar introduced the Exit Velocity Index (EVI), which ranks how efficiently startups create value, not just how big they get.

[

\text{EVI} = \frac{\text{Exit Value}}{\text{Total Equity Raised}} \times \frac{1}{\text{Exit Age}}

]

Calculated across 3,317 North‑American VC‑backed exits ($10 M+ , 2010‑2025).

Key Findings

• Big doesn’t mean efficient. Mega‑exits like Uber, Palantir, Instacart, and Lyft rank far lower once capital burn and time‑to‑exit are factored in.

  • Example: Uber’s EVI ≈ 5.3×, while Mir’s $400 M exit on $2 M raised in 2 years scored EVI = 100, outperforming WhatsApp’s legendary $22 B exit.

• Time kills velocity. Companies that took 15‑20 years to exit (Yahoo, Reddit, ServiceTitan, Procore) scored among the lowest EVIs despite large outcomes. Median exit age of the top 100: 10 years.

• Capital efficiency defines winners. Median capital efficiency: 13.9×, but only 0.8 % of exits achieved EVI > 20 (e.g., WhatsApp, Datadog, Coinbase).

Takeaways

• EVI reframes success: size alone is a poor measure of performance.

• Efficiency and speed compound returns; shorter build times and leaner raises often beat mega‑funded unicorns.

• For LPs and founders: benchmark by EVI, not just valuation. It reveals who built durable value fast, not just who raised the most.

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End of article.