🔥 In this piece:

• 🤖 What AI-Native Really Means

• 🧩 SaaS vs SaS: The New Foundation

• 📉 Why the Old SaaS Scoreboard Fails

• 📊 The New AI-Native Scorecard

• 🎯 Why This Matters for Founders & Investors

If you measure AI-native platforms with old SaaS KPIs, you’ll back the wrong founders and misprice the winners.

For anyone building or investing in the AI wave, one thing is now clear: if you evaluate AI-native companies using traditional SaaS metrics, you will misdiagnose the business, misunderstand the economics, and allocate capital in all the wrong places.

For twenty years, SaaS operated on a stable foundation. Software helped humans complete workflows. Pricing was per seat. Growth was linear. And the metrics were predictable: ARR, the Rule of 40, LTV:CAC, net retention, and those comforting 80–90% gross margins.

That model was correct for its era. It is not correct for this one.

A new class of company has emerged — Cursor, Lovable, Manus and others — and they do not behave like SaaS. They do not scale like SaaS. They do not retain users like SaaS. Their cost structure is nothing like SaaS.

These companies are AI-native.

Not “SaaS with an AI feature.”
Not “a wrapper on GPT-4.”
Not “we fine-tuned a model.”

AI-native is an entirely different product architecture — and therefore an entirely different economic system.

Yet the industry is still scoring them on the wrong scoreboard.

🤖 What AI-Native Really Means (Not What People Think)

Most people assume “AI-native” means building your own model, training your own LLM, or deploying proprietary architectures.

This is a misunderstanding.

AI-native = the AI does the work, not the human.

More precisely:

• The AI is the operator.

• The product delivers finished outputs, not workflow steps.

• The unit of value is the completed job, not time spent in product.

• Every unit of value carries a real inference cost.

• The product cannot exist without the AI performing the work.

This has nothing to do with whether you use Claude, GPT-4, Llama, Mistral, or a hybrid router.

AI-native = outcome-producing, cost-bearing, value-generating systems.

Once you define it this way, the economics become obvious.

LISTEN TO THE DEEPDIVE PODCAST

🧩 SaaS vs SaS: The New Foundation

Fusion42 and Investor-X use a distinction that is becoming foundational across the ecosystem:

SaaS — Software-as-a-Service

• Human = operator

• Software = tool

• Value = enabling workflows

• Costs = fixed, near-zero marginal cost

• Pricing = per seat

• KPIs = adoption, usage, engagement

SaS — Service-as-Software

• AI = operator

• Software = delivery wrapper

• Value = completed output

• Costs = variable (inference per task)

• Pricing = usage, jobs, credits, agents

• KPIs = tasks completed, cost-to-serve, outflow retention

Most modern AI-native products are SaaS shells with SaS cores — a familiar interface wrapped around an autonomous service engine.

And once you see this, the conclusion is unavoidable:

SaaS metrics measure human behaviour.
SaS metrics measure output economics.

Judging SaS platforms with SaaS metrics is like diagnosing an electric drivetrain with petrol-engine tools.
You’ll get confident answers — and all of them wrong.

📉 Why the SaaS Scoreboard Fails (Metric by Metric)

1. Gross Margin: Not a Percentage — a Trajectory

Typical SaaS: 80–90%
Early AI-native: 40–60%

Not because AI-native is weak, but because:

• SaaS COGS is flat

• AI-native COGS scales with outputs

• every action incurs compute cost

• users vary massively in cost-to-serve

The correct KPI is Gross Margin Trajectory — not the static percentage.

2. CAC: High CAC + High COGS = Dead Company

When marginal costs are real, the only viable GTM is:

• product-led

• viral

• instant value

• self-serve

The correct KPI is CAC trendline vs inference efficiency, not CAC payback.

3. Retention: The Real Curve Starts at Month 3

AI-native products attract “AI tourists.”
Month-1 retention is noise.

The real signal is:

M3 → M12 Cohort Retention

Healthy curves show:

• tourists out

• professionals in

• usage up

• spend up

• cohorts that dip → flatten → rise (“the smile”)

Traditional NRR hides this.

4. Engagement Metrics Are Obsolete

SaaS measures:

• DAU/MAU

• time in product

• workflow usage

AI-native must measure:

• jobs completed

• workflows executed

• assets produced

• tasks automated

Engagement is not value. Output is value.

5. Pricing: Seats Are Dead. Outcomes Win.

SaaS pricing is cost-agnostic.
AI-native pricing must be cost-aligned:

• usage

• credits

• outcomes

• tasks

• per-agent (“AI employee”)

Outcome-based pricing aligns value, cost, and margin.

📊 The New Scoreboard: The Investor-X Scorecard

-

1. Inference Cost per Active Customer (ICAC)

Your new North Star.
Falling ICAC = compounding business.

2. Jobs Owned per Account

The real measure of dependency, value, and moat.

3. M3 → M12 Retention

By Month 3, only real users remain.
Healthy: 85–120% M12/M3.

4. Gross Margin Trajectory

Direction matters more than destination.

5. Wallet Share of Customer AI Spend

Seat count is irrelevant.
Workload share is everything.

🎯 Why This Matters

If you judge AI-native companies with SaaS metrics, you will:

• underestimate growth

• misread retention

• misunderstand cost curves

• misprice valuations

• and back the wrong founders

This shift is as large as:

• internet → cloud

• cloud → mobile

• mobile → AI-native

AI-native companies are not “SaaS with AI features.”
They are autonomous service engines delivered through SaaS interfaces.

Once the unit of value shifts from workflow → output, every metric must shift with it:

• economics

• pricing

• retention

• moats

• valuation

• strategy

This is why the SaaS scoreboard fails.
And it’s why those who adopt the AI-native scoreboard now will win the next decade.

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🧩 The Pattern That’s Breaking

On a recent call I said something that’s been rattling round my head since: we’ve moved from the Information Age (answers to what and why) into the Intelligence Age, where the how is handed to you—and it’s happened in a few short years.

Receipts: controlled studies show developers completing tasks roughly 50%+ faster with assistants like Copilot. On SWE-bench, systems jumped from single-digit solve rates in 2023 to order-of-magnitude higher in 2024–25 on real software issues. And low/no-code is now standard practice—by 2025 a big chunk of new apps are assembled with it. In plain English: parts of planning, scaffolding, and shipping have gone from scarce to abundant—fast.

Right now you can ask ChatGPT for a SaaS blueprint, Claude for a GTM playbook, and Cursor to scaffold the codebase. The how is being commoditised. That’s the arms race we’re in.

🤖 From instructions to direct execution (MCP & agents)

It’s not just plans anymore—the stack acts:

• Agent/Operator modes that can operate a browser/computer and complete end-to-end tasks (typing, clicking, filing, ordering) with permission gates.

• Model Context Protocol (MCP)—think USB-C for AI—an open way to plug models into tools, data, and workflows so they can fetch, call, and execute consistently across systems.

Bottom line: in under three product cycles we’ve gone from “AI drafts a plan” to “AI scaffolds, ships, and increasingly does the thing.”

But building fast ≠ winning. We’re drowning in near-identical AI wrappers and productivity clones. Speed, once an edge, is becoming table stakes. If execution is abundant, what’s scarce?

🧠 The Conventional Wisdom (and why it’s not enough)

We all teach the same next-level answers—at Fusion42 included:

• Better problem definition and questions

• Judgement and curation

• Faster experiments

• Design thinking

• Orchestrating humans + AI

All useful. None feel like a phase shift. They’re upgrades to the same game—not recognition that the game itself may have changed.

❓ The Uncomfortable Possibility

What if there’s a layer above execution we haven’t named yet?

In 1995, smart people optimised filing systems and typing speed while the real shift was from information scarcity to abundance—and the rise of search, curation, and recommendation. We may be making the same mistake: assuming the next step is simply “automated execution”, when the real shift could be orthogonal—a move along a different axis entirely.

My hedge-fund brain says the biggest alpha lies in spotting the regime change early. The outline I see: as execution abundance grows, advantage migrates toward rails that convert resources and judgement into reliable outcomes.

🧭 Candidates for the new scarcity economy

• Attention architecture — rails that direct focus in a world of infinite output.

• Trust systems — provenance, reputation, verification, auditability at scale.

• Taste-as-a-Service — scalable aesthetic/functional judgement.

• Narrative control — shaping how we frame problems and solutions.

• Cross-domain synthesis — combining fields to produce non-obvious answers.

• Coordination at scale — collective human+AI problem-solving that actually delivers.

• Meaning-making infrastructure — systems that help people decide what matters.

🔐 A bet: trust infrastructure is the first new scarcity

Of the above, my money—literally—goes first on trust.

Why trust?

• Post-authenticity reality. Agents and models now plan, code, create, and act. Without provenance (who/what/when/where), reputation (track record across contexts), and verification (cryptographic or institutional), adoption stalls.

• Economic leverage. Trust collapses dispute costs (fraud, refunds, compliance churn) and unlocks higher-velocity coordination (payments, data sharing, automated execution).

• Composability. Trust rails are reusable across domains—media, commerce, healthcare, finance—so returns compound.

What it looks like to build:

• Provenance by default: content and actions carry signed trails (model, prompt family, data source, timestamp).

• Reputation as a service: portable identity with performance curves (accuracy, on-time completion, defect rate) for humans and agents.

• Verifiable execution: before an agent “does the thing” (books, orders, files, deploys), counterparties can check capabilities, policies, and history.

• Operator metrics (v1): fraud loss rate ↓, dispute time ↓, mean time-to-trust decision ↓, % actions with verifiable provenance ↑, policy-compliant auto-approvals ↑.

🌫️ A founder in the fog (60 seconds)

Maya runs a 6-person SaaS. Last year, a core feature took two weeks; now Cursor scaffolds it in a morning and an agent migrates customer data. She’s faster—yet conversions stall. Her queue fills with “Is this safe?” and “Who trained this?” The block isn’t speed; it’s trust. She adds signed data lineage and lightweight reputation pages for her automations. Refunds drop 28%, enterprise pilots unstick. Same code, different rail: trust.

📈 Why trust comes first (and enables the economics shift)

The economics only bend toward human-first when trust rails collapse dispute, compliance, and coordination costs; provenance and reputation make it possible to price on reality (compute, latency, data quality) instead of feature theatre. No trustworthy execution → no incentive shift.

🧱 The plateau scenario (the credible hedge)

If we plateau, old moats reassert: resources, regulation, relationships, reach. Network effects harden, infra owners win, and energy/compute/regulatory approvals become the live bottlenecks. That world rewards distribution, policy fluency, and procurement muscle more than novelty. I don’t prefer this outcome, but it’s a credible branch. Designing for abundance while staying legible to today’s institutions is the hedge.

🧪 How I’m testing this (Arthur, in the wild)

I’m building Arthur with this bias—as a living lab, not a solved product.

• Provenance vs friction. Do we sign every agent action (model ID, prompt family, data source, time) and show it by default—adding UI friction—or keep it hidden and hope trust vibes carry? We’re testing “provenance on by default, collapsed by design.”

• Resource-priced features. Do we sell “AI features” as bundles, or price on real inputs (compute, latency, verification tier)? We’re trialling the latter: cheap for draft-only, premium for verifiable execution.

I’ll share results—warts and all—so we can falsify the bet in public.

🛠️ If you’re building right now (3 moves)

• Instrument trust: ship basic provenance + visible reputation for your automations this quarter.

• Price on reality: tie at least one feature to compute/latency/provenance cost, not “AI magic.”

• Sandbox proofs: run one permissioned pilot where agents execute end-to-end with verifiable audit trails.

❓ The question we should be asking

Maybe it isn’t “What comes after how?” but:

• What becomes systematically valuable when building is free?

• The questions we ask?

• The problems we choose?

• The trust we earn?

• The attention we route?

• The meaning we help create?

• The coordination we unlock?

• Or something we haven’t named yet?

We’re in the fog between regimes—the old rules broken, the new ones not yet legible. The winners of the next decade won’t simply execute faster; they’ll recognise the category error and build the rails—for attention, trust, taste, synthesis, coordination, and meaning.

I have a bet (trust first), a lab (Arthur), and an open mind.
What’s your hypothesis? When execution is abundant, what stays scarce?

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Derek

📅 In 2022, I wrote about “When Solving Problems: The Conflict Between Puzzles and Mysteries”

🍳 Introduction

🤖 Why AI Needs a Different Playbook

🔍 The Puzzle-versus-Mystery Distinction

⚖️ Why the Distinction Matters

🧭 Adapting My Heuristic for AI: From “10× Product” to “New Behaviour”

🎯 Illustrative Examples of Puzzle vs Mystery

🏁 Conclusion

📅 In 2022, I wrote about “When Solving Problems: The Conflict Between Puzzles and Mysteries” on LinkedIn, arguing that identifying a problem is a puzzle (clear rules, measurable metrics) and solving it is a mystery (fog of war, no immediate answers). Fast forward to 2025, and that distinction has never felt more urgent—especially in the world of AI, where yesterday’s playbooks won’t kickstart tomorrow’s breakthroughs.

🍳 Introduction

I often liken this to when I’m in the kitchen. If I’m following a cherished family recipe—say my grandmother’s shepherd’s pie—that’s a puzzle. I know exactly which cut of lamb to use, how long to braise the mince, what ratio of peas to carrots to mash. There’s a tidy set of instructions and, if I stick to them, I’ll end up with something reliably delicious every time.

By contrast, imagine I’ve been invited to host a dinner party at very short notice and all I have in the fridge is a wilting bunch of kale, half a block of feta, some leftover roast chicken and a handful of walnuts. I haven’t got a recipe to follow here—this is a mystery. I’ll need to experiment: perhaps wilt the kale with garlic and chilli, crumble the feta on top, shred the chicken in, then toast the walnuts and drizzle everything with lemon and olive oil. At each step, I’m making it up as I go, tasting and adjusting. There’s no guarantee the final dish will be a triumph; I’m navigating uncertainty, gathering clues (a pinch more salt, a squeeze more lemon), and learning in real time. That’s the essence of a mystery—no set rules, just a hunch and a willingness to iterate.

🤖 Why AI Needs a Different Playbook

Over the past few years, I’ve watched the technology playbook turn into a puzzle: “Release version 2.0 by Q3,” a predictable roadmap of incremental feature improvements, backward-looking metrics. But as generative AI reshapes entire workflows, that puzzle-solving approach feels rapidly obsolete. Increasingly, creators and builders face problems that truly are mysteries—uncharted territory with no immediately obvious solutions. If we treat every challenge as though there’s a neat, backwards-looking checklist, we’ll miss the point entirely. AI innovation demands we learn to distinguish between yesterday’s quantifiable “puzzle” and tomorrow’s emergent “mystery.”

“AI is the new electricity. Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.”

— Andrew Ng, DeepLearning.AI

The VC playbook—the one built on blitzscaling, unicorn-or-bust, and 10-year fund cycles—is not just outdated, it’s toxic for real innovation in 2025. Investors still clinging to the old gospel of “grow at all costs, exit in five years, spray and pray” and repacking startups for the next round are steering founders straight into the rocks. The harsh reality? Follow-on funding is drying up, VCs are more ruthless and risk-averse than ever, and the era of easy money is over.

Founders are waking up: why surrender equity, control, and vision just to chase someone else’s milestones and burn for growth you don’t believe in? AI lets you build more with less—meaning the leverage once offered by VC is shrinking by the day. The pressure to perform for investors, not customers, is killing more startups than market competition ever could.

Worse, the “pattern recognition” that VCs pride themselves on—now turbocharged by AI—is an optimization trap. It rewards sameness, penalizes outliers, and systematically misses the very black swans that drive true returns. The result? Overfunded copycats, underfunded originals, and a portfolio that looks innovative only in a spreadsheet.

The new reality is brutal: if you’re building for a VC’s spreadsheet, you’re already obsolete. If you’re a VC still running the old playbook, you—and your LPs—are about to learn what drowning in mediocrity feels like. The future belongs to founders who build lean, test fast, and only take capital from partners who actually add value—not just cash and empty pressure.

For a deeper dive into how the next generation of funds will operate—AI-native, outcome-obsessed, and post-power law—see my recent piece, “🔮 The New Rules of VC: AI-Native, Outcome-Obsessed, and Post-Power Law.

The only startups that will survive are those that ignore the old investor playbook, build for customers (not pitch decks), and treat VC money as a tool of last resort, not a rite of passage.

The VC playbook is not just dead—it’s a liability. Those who cling to it will drown, and they’ll take anyone who listens down with them. The future belongs to the founders who refuse to play by those rules.

Of course, not all investors are stuck in the past—some VCs are already adapting, experimenting with new funding models, focusing on value beyond capital, and genuinely partnering with founders to navigate this new landscape.

🔍 The Puzzle-versus-Mystery Distinction

(This puzzle-versus-mystery lens is inspired by Malcolm Gladwell’s work, which distinguishes between problems with clear solutions (puzzles) and those that require ongoing exploration (mysteries).)

🧩 Puzzle (Solvable, Clear Rules)

📌 A puzzle is a problem where rules are well understood, inputs map directly to outputs, and success can be measured against known indicators.

📌 For example, optimising a website’s load time by following established caching techniques. I know the steps and can measure milliseconds shaved off the response time.

📌 In 2021’s SaaS era, many tech teams focused on puzzles: hitting revenue milestones, hitting user-growth percentages, automating routine tests. If those metrics checked out, the puzzle felt solved.

🧙 Mystery (Complex, Unclear Rules)

📌 A mystery is an entirely different beast. There are no obvious metrics to plug into a formula, no historical data that guarantees tomorrow’s outcome.

📌 When experimenting with a brand-new AI workflow—say, an autonomous agent that negotiates energy use—you can’t consult last quarter’s logs to predict next quarter’s behaviour.

📌 By definition, mysteries demand ongoing discovery, iterative experimentation and a readiness to pivot whenever the rules change. It’s like sailing through fog: you rely on small signals rather than a fixed map.

⚖️ Why the Distinction Matters

“We’re not just building better algorithms—we’re exploring entirely new questions about what intelligence can achieve.”

— Demis Hassabis, DeepMind

I’ve seen teams asked, “How exactly will you reach 1 million users in six months?”—even though no one knows how people will respond to a novel AI interface. That’s treating a mystery like a puzzle. Conversely, if engineers treat every challenge as a mystery, they risk ignoring basic feasibility: surely, some core technical requirement must be satisfied before you launch anything. The key is knowing when to lean on hard data (puzzle mode) and when to embrace uncertainty (mystery mode).

🧭 Adapting My Heuristic for AI: From “10× Product” to “New Behaviour”

“AI isn’t just code and data. It’s about empowering humans to explore new frontiers of creativity.”

— Fei-Fei Li, Stanford University

Back in late 2022, I wrote that we should “solve the puzzle with numbers, then explore the mystery.” Today, with AI advancing so fast, the stakes are higher. (This three-stage framework echoes themes from Clayton Christensen’s disruption theory, but here I’m specifically mapping how AI shifts not just markets, but the very habits and workflows of end users.) Rather than asking “Is this 10× better than yesterday’s tool?”, I now focus on three categories:

🍱 Adaptation

AI is layered onto an existing workflow—think adding a chatbot to your existing support desk.

Users click the same menus and follow familiar steps; the only change is a little “Ask AI” button.

It may feel neat in the short term, but habits scarcely shift. As one developer quipped, “Tacking AI onto old processes is like painting a car to run on water—it still needs to move differently.”

🦋 Evolution

“When AI can write code, we don’t just optimise existing software. We have to rethink what ‘software’ even means—and that changes everything.”

— Chris Dixon, Andreessen Horowitz

AI becomes intrinsic to the interface, prompting entirely new actions—editing video by editing text, or coding via conversational prompts.

For instance, with a text-based video editor, I no longer scrub timelines; I type “remove the ums and ahs,” and the AI handles it. Now I think in plain language rather than menus.

Genuine metrics—time saved, engagement depth—only materialise once users adopt these new flows. Before then, there’s no historical benchmark and it feels like a genuine mystery we must learn by doing.

🚀 Revolution

“If you’re not failing fast, you’re not exploring far enough. True progress comes from what we learn when things go wrong.”

— Ilya Sutskever, OpenAI

AI invents whole new markets or behaviours—such as an agent that autonomously trades your household energy, or a peer-to-peer microbiome marketplace.

Success hinges on discovering habits we didn’t know we needed. Users don’t “upgrade” anything; they fundamentally change how they solve a problem.

Traditional KPIs—revenue, user counts—mean very little until these fresh behaviours take hold. The focus is on whether the AI delivers a habit so compelling that users can’t revert to the old way.

🎯 Illustrative Examples of Puzzle vs Mystery

🔄 Example: Adaptation

AI-powered summarisation in a note-taking app: Users click their usual “Summarise” button. The underlying AI may be new, but the workflow—“open note → click summary → read result”—remains unchanged.

Puzzle aspect: Measuring reduction in word count or time saved.

Mystery aspect: Minimal, since behaviour doesn’t truly shift—people still approach notes in the same way, albeit faster.

🔄 Example: Evolution

Text-based video editing: Rather than dragging clips on a timeline, I type “cut every filler word” and the AI does it.

Puzzle aspect: Measuring minutes saved on editing.

Mystery aspect: Users now think, “I can describe edits conversationally,” which alters how they conceptualise video creation. Metrics emerge only after they adopt that new habit.

🔄 Example: Revolution

Autonomous energy-trading agent: I wake up; my AI agent has already sold surplus solar energy to the grid and bought cheap power for evening use.

Puzzle aspect: Hardly any—there is no past reference for peer-to-peer energy trades handled entirely by AI.

Mystery aspect: Users learn to trust the agent, wake to lower bills, and tweak preferences over time. Entire behaviour around energy management changes.

🏁 Conclusion

In 2025, AI challenges rarely resemble simple puzzles with neat solutions. They feel more like riddles that demand founders and builders to test hypotheses, learn in real time and adjust dynamically. By deliberately separating “What can we prove today?” (puzzle-mode) from “What must we discover?” (mystery-mode), we avoid the trap of chasing yesterday’s playbook and instead cultivate a genuine long-term vision—one that truly unlocks new behaviours and markets. In doing so, we navigate uncertainty with confidence and stay primed to innovate in ways we never imagined.

Here’s the full series of a data driven approach to investing 👇

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Derek

🔍 1. Deal Sourcing Becomes Commoditised

🔧 2. The Real Differentiator? Value Delivery

🔁 3. Capital Velocity Over Capital Lock-Up

📉 4. Post-Power Law Portfolio Strategy

🤖 5. The AI-Native Fund Stack

📢 6. The LP Wake-Up Call

🧠 7. Vertical Funds Will Outrun Horizontal Hype

🧰 8. Full-Stack Fund-as-a-Product

🧭 9. Founders Will Start Vetting VCs Harder

Venture capital, for all its swagger, is long overdue a reckoning.

For decades, the model hasn’t changed — and neither have the results.

• The top 5–10% of funds still capture 80–90% of all meaningful returns.

• Median fund performance has flatlined for 25+ years.

• Founder failure rates? Still brutally high — no better than two decades ago.

So much capital. So little innovation in capital.

We’ve normalised underperformance, dressed it up in power laws, and called it strategy.

But that era is ending.

The next generation of funds won’t just look different. They’ll operate on a completely new OS — one built around AI, engineered value creation, faster returns, and a flipped funnel that starts with capital efficiency, not just capital deployment.

Here’s how I see the new model — not as a nice-to-have, but as a survival necessity.

Here’s the full series 👇

🔍 1. Deal Sourcing Becomes Commoditised

AI now does what associates used to: scraping LinkedIn, parsing pitch decks, spotting traction signals. No need for 50 coffee chats to “get access.” The game’s changed.

Every serious fund will run sourcing like a tech product:

• Ingest

• Rank

• Qualify

• Only loop in a human when it matters

Sourcing is no longer a moat. It’s table stakes. The edge starts after the cheque lands.

🔧 2. The Real Differentiator? Value Delivery

Founders don’t need another “how’s it going?” call. They need firepower.

The funds that win will behave like high-impact operators, not passive capital:

• AI co-pilots for GTM, hiring, ops

• Embedded teams to close hires, test pricing, prep the next raise

• Playbooks built from lived experience, not Medium

No more vague promises of “value-add.” The proof will be in dashboards, metrics, and outcomes. LPs will back what they can measure.

🔁 3. Capital Velocity Over Capital Lock-Up

The old fund model — 10 years, 2% fees, wait-and-see DPI — is done.

Next-gen funds will act more like product loops:

• Faster, smaller exits

• Early secondaries

• Active recycling of capital

We’re not thinking small. We’re thinking smart. Strategic acquirer by Year 3 beats IPO fantasy in Year 10.

📉 4. Post-Power Law Portfolio Strategy

The old model accepted mass failure. “One winner returns the fund.”

But in an AI-native world, that’s no longer necessary — or acceptable.

Now we can:

• Track real-time performance

• Spot drift before it kills a startup

• Pivot fast, not panic late

The goal? Fewer write-offs, more wins. This isn’t romantic. It’s responsible. And it’s what LPs now expect.

🤖 5. The AI-Native Fund Stack

New funds should operate like startups. That means lean, fast, and AI-augmented:

• LLMs trained to score deals by thesis

• Automated, personalised LP reporting

• Fund ops — from compliance to CRM — on autopilot

This isn’t AI theatre. It’s operational leverage. The funds still scheduling partner calls by hand? Extinct in five years.

📢 6. The LP Wake-Up Call

Let’s be honest — LPs helped cause the bloat. They chased logos, got seduced by hype, and ignored the fundamentals.

Now they’re awake — and demanding more:

• Faster liquidity

• Measurable value-creation

• Real-time visibility into fund ops

The best GPs will treat LPs as true partners. Live data. Milestone-based disbursements. No more once-a-year PDFs.

🧠 7. Vertical Funds Will Outrun Horizontal Hype

Generalist, spray-and-pray funds won’t cut it.

The next wave will go deep:

• Technical depth (quantum, bio, robotics)

• Ecosystem alignment (govtech, industrial, policy-driven sectors)

• Founder-embedded networks (operator-led, not outsider-run)

AI will surface the signal — but the real edge will come from knowing the terrain.

🧰 8. Full-Stack Fund-as-a-Product

The best funds will become infrastructure. They won’t just fund growth — they’ll power it.

We’ll see:

• VC-as-a-service platforms: AI tools, GTM kits, networks

• Programmatic follow-on strategies

• In-house accelerators — not for deal flow, but for actual traction

Think Y Combinator rebuilt for an AI-native world — less theatre, more output.

🧭 9. Founders Will Start Vetting VCs Harder

The old power dynamic is gone. Founders are in the driving seat.

They’re now asking:

• What’s your value-per-dollar?

• Can I plug into your stack from Day 1?

• Are you here for outcomes — or optics?

Founders aren’t just raising capital. They’re recruiting co-pilots. And legacy funds? They’re not getting the callback.

🚀 Final Thought: The New VC Isn’t a Money Machine — It’s a Momentum Engine

The future belongs to funds that build leverage across every layer: sourcing, delivery, decision-making, and exits.

AI isn’t a trend. It’s the new baseline.

The next era of VC won’t be led by pedigree, brand, or network.

It’ll be led by those who can engineer returns — for founders, LPs, and themselves.

Forget power laws. Build power loops.

Share Fusion42 by DeReK WaTSoN

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For the ❤️ of startups

✌🏼 & 💙
Derek

🚨 – What “Sherlocking” Means in the Gen‑AI Era

🧱 2. The seven venture‑grade moats

🔍 3. Diligence drill – five questions to kill thin moats

🚩 4. Red‑flag scorecard

🏁 5. Conclusion – Fund assets, not features

🚨 – What “Sherlocking” Means in the Gen‑AI Era

In 1998 Apple’s Sherlock search tool quietly absorbed a third‑party app named Watson. That one act coined the term Sherlocking: when a platform owner clones a popular add‑on and leaves the start‑up out in the cold.

OpenAI now does the same in software. A micro‑SaaS goes viral → the capability appears inside ChatGPT with zero‑friction UX and no extra cost. Its in‑chat Shopping Assistant arrived only weeks after product‑discovery plug‑ins hit the store.

For founders it can sink the company; for VCs it can wipe the return. Your equity only endures if something valuable still works the morning after the clone ships.

🚨 The Clone Is Coming

ChatGPT has Sherlocked fourteen standalone plug-ins this year. As soon as a point solution gains traction, OpenAI folds it straight into ChatGPT—no extra cost, zero friction.

Every one of these began life as a separate plug-in or micro-service—now folded straight into ChatGPT’s UX, at no extra cost or friction. Founders and VCs need to ask: if my entire feature set were built in-house at OpenAI, would users even notice?

1. In-chat Shopping Assistant (Instacart-style)

2. Web Browsing (“Browse with Bing” replacing WebPilot, etc.)

3. Advanced Data Analysis / Code Interpreter (formerly Python & Wolfram plug-ins)

4. DALL·E Image Generation (vs third-party image-gen add-ons)

5. Canva-style Graphics (template-based design in-chat)

6. Zapier-style Workflow Automation

7. PDF Reader & Summariser (AskYourPDF-style)

8. YouTube/Video Summariser

9. Memory & Personalisation (third-party “memory” plug-ins)

10. Travel Booking (Expedia/Kayak-style searching and reservation)

11. Restaurant Reservations (OpenTable-style)

12. Translation (DeepL-style high-quality language conversion)

13. Meeting Scheduling (Calendly-style)

Real-time News & Research (replacing niche news-feed plug-ins)

🧱 2. The seven venture‑grade moats

Read more

🔄 The Evolution of SaaS into a Fully Automated Service as Software (SaS) Model

As technology marches on, new ideas and models keep popping up to reshape entire industries. One of the latest of these shifts is Service as Software (SaS)—a forward-thinking approach that builds on the well-established Software as a Service (SaaS) model. While SaaS has given users handy tools to carry out tasks themselves, SaS takes things a step further. By harnessing advanced AI capabilities, SaS doesn’t just provide the tools; it delivers entire services, running autonomously through software.

This evolution could open up a huge potential market, estimated at a staggering $4.6 trillion, as it aims to cover a much broader spectrum of business needs that used to rely on human labour. Let’s explore how SaS is set to redefine service delivery across a range of industries.

Vertical SaaS is gonna get smashed first

Read the previous article here:

Fusion42 by DeReK WaTSoN

Vertical SaaS is gonna get smashed first

🚨 Why Vertical SaaS Is Set to Face Major Disruption…

Read more

2 years ago · 1 like · DeReK WaTSoN

Listen to the Deepdive Podcast here:

🤖 What is Service as Software (SaS)?

In essence, Service as Software (SaS) is a model where AI agents and advanced automation deliver entire services, rather than simply offering tools or platforms for users to operate themselves. SaS is designed to carry out tasks autonomously—managing whole processes that would normally need human oversight. Think of AI agents that not only schedule meetings but also analyse and summarise discussions, organise follow-ups, and even handle customer relationships.

This shift makes SaS tools outcome-focused and task-driven, enabling businesses to tap into streamlined, automated services without any manual effort. With this approach, SaS offers a significant competitive advantage, helping businesses scale up more efficiently, reduce costs, and improve overall productivity.

⚖️ How SaS Differs from SaaS

The differences between SaaS and SaS reveal a shift in the overall philosophy of service delivery:

• Outcome-Oriented Services: Unlike SaaS, where users actively use tools to reach their goals, SaS is focused on delivering outcomes. Users no longer need to schedule emails or update databases themselves; SaS agents handle it all autonomously.

• AI-Driven Autonomy: SaS employs AI agents that can make decisions and adapt as they go, allowing them to take on tasks and improve over time. This level of autonomy reduces the need for human input in routine processes.

• New Pricing Models: SaaS usually operates on subscription or per-user pricing, whereas SaS could shift towards outcome-based pricing—charging based on actual results achieved, such as the number of deals closed, qualified leads generated, or customer interactions completed.
  
  
  

As SaS shifts from tools to outcomes, what processes in your organisation could be optimised—or even fully replaced—with autonomous services?

💹 Market Potential of SaS

The market potential for SaS is immense. While the global software market is substantial, the global services market is even larger, valued at around $4.6 trillion. By delivering complete services through software, SaS companies have the opportunity to tap into this vast space, offering scalable, automated solutions across industries like sales, marketing, cybersecurity, software engineering, and customer support.

🤖 AI-Driven Automation at the Core of SaS

At the core of SaS are AI agents—intelligent software capable of mimicking human behaviour to achieve specific outcomes. Unlike traditional automation, which relies on set workflows, these agents use AI to make decisions, adapt to new inputs, and continually improve. Powered by natural language processing, machine learning, and large language models, SaS agents can take on complex tasks that previously needed human involvement, such as:

• Writing Personalised Emails: AI agents can draft and send customised messages, managing customer interactions and follow-ups entirely on their own.

• Scheduling Demos and Meetings: Rather than simply sending notifications, AI agents can handle the whole scheduling process, optimising timing and resource allocation.

• Customer Interaction and Support: AI agents can autonomously manage customer inquiries, resolve issues, and even escalate complex cases when required.

These agents don’t merely assist with tasks; they complete them, reshaping workflows and freeing human employees to focus on higher-level decision-making.

🏭 Industries Primed for SaS Transformation

Although SaS is still emerging, it’s already showing potential to reshape a variety of industries. Here’s a closer look at some key sectors where SaS could make a significant impact:

1. Sales and Marketing

In sales and marketing, SaS has the capacity to optimise customer engagement and streamline workflows:

• Lead Generation and Qualification: AI agents autonomously search for and qualify leads, allowing sales teams to focus on high-value prospects.

• Automated Outreach: From crafting personalised emails to scheduling demos, AI agents handle the entire outreach process, drastically reducing the workload for sales teams.

• Campaign Analysis: AI-driven insights enable SaS platforms to monitor and adjust campaigns in real-time, boosting performance and return on investment.

2. Software Engineering

In software development, SaS can streamline cycles, reduce errors, and increase overall efficiency:

• Code Generation and Review: AI agents can autonomously write, review, and optimise code, supporting developers and minimising human error.

• Incident Response and System Monitoring: AI agents detect, categorise, and resolve system issues, ensuring real-time problem-solving without human input.

• Continuous Integration and Deployment: SaS platforms driven by AI can autonomously handle updates, testing, and rollouts, making software deployment more efficient.

3. Cybersecurity

Cybersecurity is particularly well-suited for SaS, where AI agents can manage routine tasks, respond to threats, and coordinate across multiple platforms:

• Threat Detection and Response: AI agents autonomously handle alerts and, where possible, respond to threats, reducing security risks.

• Cross-Platform Coordination: Many organisations use multiple security platforms; SaS agents can unify responses across these tools, ensuring a coordinated defence.

• Adaptation to New Threats: SaS agents can learn from past incidents, improving their ability to recognise and counter new and evolving threats.

4. Legal Services

In the legal sector, SaS has the potential to reduce costs and increase productivity by automating research, compliance, and document management:

• Contract Analysis and Drafting: AI agents can review and draft contracts, autonomously flagging potential risks and ensuring regulatory compliance.

• Legal Research: AI can autonomously conduct legal research, searching databases and pulling relevant cases or statutes, providing lawyers with ready-to-use insights.

• Case Management: SaS agents can autonomously manage case files, deadlines, and court submissions, ensuring that all legal processes run smoothly and efficiently.

5. Accountancy

Accountancy, with its data-intensive workflows, is ripe for SaS-driven automation, helping firms process data, ensure compliance, and deliver insights with minimal human input:

• Automated Bookkeeping: AI agents can handle transaction recording, categorisation, and reconciliations autonomously, reducing time spent on routine bookkeeping.

• Audit and Compliance: SaS agents can review financial statements, flagging irregularities and verifying compliance with accounting standards, saving considerable time in audits.

• Tax Preparation and Filing: AI agents can autonomously prepare and file tax returns, ensuring accuracy and adherence to the latest regulations.

6. Financial Services

In financial services, SaS has the power to enhance efficiency, risk management, and client service, automating a wide array of tasks:

• Portfolio Management: AI agents can autonomously monitor and rebalance investment portfolios, responding to market changes and adjusting assets based on predefined strategies.

• Customer Onboarding: SaS agents can handle the entire onboarding process, from KYC (Know Your Customer) verification to account setup, reducing administrative time.

• Fraud Detection and Prevention: SaS-powered AI agents monitor transactions in real-time, detecting suspicious activity and triggering alerts or preventive actions.

  How would customer expectations change if AI could handle everything from lead qualification to full customer support? Could this alter the competitive landscape in sales and marketing?

  ---

SAVE YOUR FREE SEAT

💵 Pricing Models: Moving Beyond Subscriptions

One of the most compelling aspects of SaS is its potential to transform pricing structures. Unlike SaaS, which typically operates on a per-user or subscription basis, SaS paves the way for outcome-based pricing:

• Performance-Based Pricing: Here, businesses are charged based on successful outcomes, such as the number of qualified leads generated, sales closed, or issues resolved.

• Usage-Based Billing: Pricing can also reflect the level of service usage, like the number of emails sent, tasks completed, or customer queries handled.

• Hybrid Models: Some companies may combine subscription and outcome-based pricing, providing predictable revenue while ensuring costs align with the actual value delivered.

These flexible models make SaS both accessible and scalable, catering to companies of all sizes and allowing them to pay only for the outcomes they receive.

⚠️ Challenges and Opportunities in the SaS Landscape

As with any emerging technology, the adoption of SaS presents certain challenges:

• Data Privacy and Compliance: Since AI agents handle sensitive tasks, safeguarding data privacy and ensuring regulatory compliance is crucial, particularly in sectors like healthcare and finance.

• User Trust and Reliability: For AI agents to operate services autonomously, users need to trust that the software will consistently deliver accurate and reliable results.

• Industry Awareness and Adoption: As SaS is a relatively new concept, businesses may need time to fully understand and embrace it. Clearly differentiating SaS from SaaS will be essential for companies aiming to establish themselves in this space.

Despite these hurdles, the opportunities for SaS are vast. With rising demand for automation and AI-driven solutions, SaS has the potential to deliver real, measurable value to companies seeking to streamline operations, cut costs, and scale efficiently.

🔍 Early Examples of Service as Software

While fully autonomous SaS companies are still emerging, several firms are already incorporating key elements of the SaS concept, automating specialised services that previously required human input. Here are a few noteworthy examples:

1. Ema (Enterprise Machine Assistant): This startup deploys "universal AI employees" that can take on various roles in a workplace, from customer support to legal and compliance. This aligns well with the SaS concept of AI agents performing complex tasks

2. KOGO AI: Their 'AI agent marketplace' is designed to automate business operations across sectors, functioning like an app store for AI agents. This platform allows businesses to easily deploy various AI agents for their specific needs

3. Infloso's Molly: An autonomous AI marketer that can independently handle entire marketing campaigns. This is a good example of an AI agent delivering a comprehensive service (marketing) through software

4. Yellow.ai's VoiceX: An AI agent capable of handling high volumes of customer queries while delivering natural, context-aware answers. This represents the use of AI to provide a complete customer service solution

5. Nurix AI: While still in development, their aim to use AI agents with human-like voice and reasoning capabilities for e-commerce aligns with the SaS concept

These examples showcase the versatility and potential of SaS, with advanced AI automating complex processes across diverse fields. By managing entire workflows with little human involvement, these companies illustrate how SaS can transform industries that require high levels of expertise and specialisation.

🍽️ Fusion42's Initial Emphasis on Service as Software (SaS), eating our own medicine.

At Fusion42, we’re not just building a platform for the startup ecosystem; we’re building it with the very Service as Software (SaS) principles we aim to provide for founders, framers, and funders. By applying the same data-driven, AI-powered approaches we plan to offer to users, we’re gradually creating a platform that learns, adapts, and improves based on real interactions. Fusion42 is a work in progress, but it’s already beginning to embody the values it’s designed to deliver.

Building Fusion42 with Intelligent Data Ingestion and Analysis

We’ve started by embedding AI-powered data ingestion and analysis into Fusion42’s development. This approach allows our team to draw insights from user feedback, market trends, and platform interactions, ensuring we prioritise features that matter most to users. Although this system is still evolving, it helps us shape Fusion42’s roadmap so we can continually adapt to the needs of the startup ecosystem.

Personalising Fusion42’s Growth with Early Feedback Loops

Our personalised onboarding and retention processes are about more than just guiding users—they also provide crucial feedback on what’s working. Every interaction offers us insights into which features users find most valuable, helping us prioritise enhancements in line with real needs. This early feedback loop is key to Fusion42’s growth, making the platform more responsive to its community as it develops.

Developing a Data-Driven Go-to-Market Strategy

We’re also using SaS principles to shape our go-to-market strategy. Instead of relying solely on traditional approaches, we’re applying data insights to connect with early adopters, build strategic partnerships, and a network within the startup community. By focusing on early data from target users, we can refine our outreach, attract the right audience, and build credibility in a way that’s both efficient and scalable.

Moving Towards a Self-Enhancing Platform for Long-Term Impact

As Fusion42 grows, every interaction, connection, and user action feeds into our data repository, reinforcing the feedback loops that guide development. This continuous flow of insights allows us to refine Fusion42’s core features—whether it’s matchmaking or market insights—helping us strengthen the platform’s value over time and ensure it remains relevant to the ecosystem.

Laying the Foundation for Self-Improving Matchmaking Algorithms

A key part of Fusion42’s vision is our Intelligent Matchmaking System, which we’re refining to connect startups with compatible investors. Our AI algorithms are learning to assess compatibility based on evolving factors such as investment preferences and sector fit. While we’re still laying the groundwork, each match and user interaction contributes to a smarter, more accurate system. As Fusion42 grows, this matchmaking capability will adapt to the unique dynamics of the startup and investor landscape.

While we’re still in the early stages, our approach to SaS is ensuring that Fusion42 won’t be a static tool. It’s being built as a platform that learns from its users and evolves alongside the ecosystem it serves. This focus on adaptability, intelligence, and data-driven growth means that Fusion42 will increasingly support founders, framers, and funders as they navigate the startup landscape. In essence, Fusion42 is both a product of SaS principles and a reflection of the future we envision for the ecosystem.

🚀 The Future of Service as Software: A New Era in Technology

As technology progresses, Service as Software (SaS) offers an exciting glimpse into the future—one where agentic, AI-powered systems can autonomously manage tasks and deliver real outcomes, moving far beyond the user-driven SaaS model. This shift is transformative: rather than just providing tools, SaS delivers an entirely outcome-focused service that can handle everything from routine tasks to complex processes without human involvement.

In a SaS-driven world, businesses will streamline operations, reduce costs, and achieve consistent, data-driven results across various sectors. Agentic AI systems will be able to adapt to changing conditions, make autonomous decisions, learn from data on the go, and work seamlessly with other AI agents to optimise workflows. For fields like healthcare, legal services, and finance—where specialised knowledge is vital—SaS brings a new level of expertise, efficiency, and scalability that’s never been possible before.

At the heart of this future are master agents—advanced, agentic AI systems that can oversee and coordinate the work of other agents. Acting as central ‘intelligences,’ these master agents understand the roles of each individual agent, managing and directing them to keep processes running smoothly and aligned with broader business goals. They’ll take on a strategic role, handling complex, cross-functional tasks and ensuring all agents are working in harmony across the operation.

As more companies adopt these AI-driven service models and move towards outcome-based pricing, SaS may soon become the standard in both technology and business. This isn’t just a step up from SaaS; it’s a fundamental change in how we view and experience services, shifting towards a world where service delivery is frictionless, fully autonomous, and seamlessly woven into daily operations. With fresh players entering the SaS market and established firms taking on these principles, we’re on the brink of a transformative era—one where Service as Software redefines the very idea of service.

As AI agents gain the ability to autonomously drive outcomes and manage complex tasks, what role will humans play in an increasingly agentic world? Are we prepared for a future where machines not only support us but, in many cases, surpass our abilities in delivering value?

Share Fusion42 by DeReK WaTSoN

Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

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For the ❤️ of startups

✌🏼 & 💙
Derek

As artificial intelligence evolves, we are on the cusp of a technological revolution—one poised to transform industries, redefine our interactions with technology, and prompt profound ethical questions. Amazon’s recent partnership with Adept, a firm specialising in agentic AI, underscores this pivotal shift. But what exactl…

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Carta's latest VC Fund Performance report paints a grim picture of the venture capital sector, highlighting significant underperformance. However, while the report is detailed and informative, it misses two critical points that make its conclusions potentially misleading and even damaging to the industry. First, it downplays the importance of the J curve—a fundamental concept in venture capital that explains why early-stage funds typically show low or negative returns. Second, it relies on misaligned time series comparisons that fail to account for the varying stages of fund lifecycles, leading to skewed conclusions.

These oversights mean that while the report uncovers some concerning trends, these findings must be viewed through a more accurate and nuanced lens. Yes, there is room for improvement in the VC industry, but the real challenge lies not just in refining analysis but in embracing a more sophisticated approach to venture capital management.
This shift is essential to navigating the complexities of today’s market environment and ensuring long-term success.

What is covered:

🔍 1. The J Curve: A Fundamental Concept in Private Markets

🔍 2. Case Study: Benchmark's Investment in eBay

🔎 3. Misaligned Comparisons: Skewing the Data

• Slow Capital Deployment:
  ✅ Strategic Insight

• Declining Startup Graduation Rates:
  ✅ Strategic Insight

• Low Distribution to LPs:
  ✅ Strategic Insight

• Negative IRRs:
  ✅ Strategic Insight

• Underwhelming Fund Performance:
  ✅ Strategic Insight

📈 4. Comparative Analysis with the S&P 500: A Stark Contrast

🕵️ 5. Potential Motivations Behind the Report

🚀 6. Moving Forward: Time for VCs to Up Their Game

💻 A Data-Driven Approach for the Startup Investment Ecosystem series

🧠 7. Embracing a Sophisticated Management Approach

🔍 1. The J Curve: A Fundamental Concept in Private Markets

The J Curve is not just a venture capital concept—it’s a fundamental economic principle observed across all private markets, including private equity, real estate, and infrastructure investments. The curve illustrates how returns often start in negative or flat territory due to the initial costs of investments and the time required for assets to mature and generate returns. This pattern is common in any scenario where significant upfront investment is needed before returns can be realised.

In the context of venture capital, the J Curve explains why early-stage funds typically show low or negative returns. Initial investments are used to build and scale startups, which often require several years to generate substantial revenues or achieve exits. As these companies grow and succeed, the returns increase, pushing the curve upward.

The Carta report acknowledges the existence of the J Curve but fails to give it the weight it deserves in evaluating fund performance. By critiquing funds still in the early, negative phase of their J Curve, the report potentially misleads readers into thinking these funds are underperforming. In reality, these funds are following a natural, expected trajectory common to all private market investments. Without fully accounting for the J Curve, the report’s conclusions lack the necessary context and risk distorting the true picture of how these funds are performing.

After discussing the theoretical aspects of the J-Curve and its implications for venture capital investments, let’s examine a real-world application of this concept. One illustrative example is Benchmark's investment in eBay. This case not only demonstrates the typical progression of the J-Curve but also highlights the critical role of strategic guidance and patient capital in realising long-term gains from venture capital investments.

2. Case Study: Benchmark's Investment in eBay

Overview:

In 1997, Benchmark Capital invested $6.7 million in eBay, then a relatively unknown online auction site. This investment was a critical moment not only for eBay but also for Benchmark, representing a significant gamble on the burgeoning internet commerce sector. The venture promised vast potential but also carried substantial risk, epitomizing the early stages of a typical venture capital investment.

The J-Curve Effect:

Initially, eBay, like many startups, faced numerous challenges, including scaling its infrastructure and securing a reliable revenue model. This period of significant investment and low returns is characteristic of the initial downward phase of the J-Curve in venture capital investments. However, eBay quickly began to show promise as it captured the growing enthusiasm for online shopping, leading to a rapid increase in user base and, subsequently, revenues.

Strategic Guidance and Patient Capital:

Benchmark provided not just financial backing but also strategic guidance to eBay, helping steer the company through critical growth phases. This included nurturing leadership, refining the business model, and navigating regulatory landscapes—key roles that venture capitalists play beyond mere financing. Benchmark's patient capital and active management were instrumental as eBay moved past its initial teething phase into a period of explosive growth.

Substantial Long-Term Gains:

The initial $6.7 million investment by Benchmark grew exponentially as eBay became a dominant player in online retail. When eBay went public in 1998, its market value soared, significantly benefiting Benchmark and its investors. This turnaround from slow initial growth to substantial returns exemplifies the upward swing of the J-Curve, where early-stage investments mature into lucrative exits.

To ensure a fair and meaningful comparison of venture capital funds across different vintage years, it's crucial to analyse each fund's performance at a consistent lifecycle stage. This approach accounts for the J-curve effect and provides a more accurate reflection of each fund's strategic decisions over time and also systemic, macro and micro events.

Higher Median IRR in Earlier Vintages: The 2017 and 2018 vintages show higher median IRRs, benefiting from earlier strong market conditions. However, recent performance has declined due to market corrections.

Recent Performance Decline: The 2019 and 2020 vintages, initially strong, have seen their IRRs drop significantly in recent quarters, reflecting the broader market downturn.

Comparison with Newer Vintages: The 2021 and 2022 vintages started with negative IRRs but seem more stable now, as they've already faced significant market volatility.

Key Takeaway: While earlier vintages benefited from a market bubble, they've since declined. Newer vintages, starting from a lower base, might offer more stability moving forward.

🔎 2. Misaligned Comparisons: Skewing the Data

The report compounds its issues by comparing different vintage years as if they’re at the same stage in their lifecycle. For instance, comparing the five-year performance of a 2017 fund with the three-year performance of a 2021 fund is like comparing a fully grown tree with a sapling—they’re at entirely different stages of growth. This misalignment distorts the reality of how VC funds develop over time and can lead to misleading conclusions about the current state of the market.

To get a true picture, it’s essential to analyze each fund’s performance at consistent lifecycle stages, considering both the J-curve effect and the broader market conditions. This is not just about improving the accuracy of analysis but about making informed, strategic decisions that drive better management practices and outcomes.

While the report is flawed, it does uncover some trends that warrant attention. However, these findings must be interpreted with an understanding of the J curve and the lifecycle of VC funds.

• Slow Capital Deployment: The 2022 vintage funds have deployed only 43% of their committed capital at the 24-month mark. This measured approach may be strategic, reflecting caution in a volatile economic climate influenced by the end of ZIRP, rapid interest rate hikes, geopolitical tensions, and AI disruptions. By pacing their investments, VCs aim to ensure that funds are allocated to ventures that are resilient and poised for growth in a rapidly changing world.

✅ Strategic Insight: This cautious capital deployment positions VCs to capitalise on the most promising opportunities, ensuring smarter, more resilient investments.

• Declining Startup Graduation Rates: A sharp decline in startups graduating from seed to Series A funding has been observed, with only 15.4% of the 2022 cohort advancing compared to 30.6% of the 2018 cohort. It's crucial to note that the 2018 rate was significantly higher than the typical 20-25% range, making the 2022 figure appear more aligned with historical norms under current challenging conditions.

✅ Market Adjustment: The 2022 graduation rate reflects a reversion to typical industry conditions, necessitating enhanced support and resources for early-stage companies to aid their growth.

• Low Distribution to LPs: Less than 10% of the 2021 vintage funds have returned capital to limited partners after three years, a statistic that might initially alarm investors. However, this aligns with the early lifecycle stage of these funds, where capital is still being actively deployed and not yet returning gains.

✅ Industry Norms: The current low distribution levels are typical for funds at this stage, with expectations for returns to increase as investments mature and reach exit stages.

• Negative IRRs: More recent fund vintages, starting around 2017, have reported negative IRRs, raising concerns about the effectiveness of current VC strategies. This trend is in line with the J-curve effect, where initial high costs and investments may not yield immediate returns.

✅ Expected Trajectory: These negative IRRs reflect the typical early stages of venture capital investments where expenses initially outpace gains. It underscores the necessity for VCs to adapt and refine strategies, especially in a market that demands agility and strategic foresight.

• Underwhelming Fund Performance: The report points out that key performance metrics such as Total Value to Paid-In (TVPI) and Distribution to Paid-In (DPI) remain low for recent vintages, often falling below 1x. This indicates that the total value of these funds, encompassing both unrealized and realized gains, is less than the capital initially invested by LPs. While this may raise concerns at first glance, it's crucial to understand that these metrics are reflective of the funds' early lifecycle stages.

✅ Contextual Norms: The lower TVPI and DPI are consistent with industry norms for early-stage funds, where initial investments are still maturing. Although these figures may appear disappointing, they align with the typical early performance trajectory in venture capital, where significant returns materialise later as investments reach maturity and exit stages.

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📈 4. Comparative Analysis with the S&P 500: A Stark Contrast

While the Carta report itself does not directly compare VC performance to the S&P 500, the manner in which VC funds' performances are presented can lead asset allocators to make such comparisons themselves. The misalignment in time series comparisons, such as juxtaposing short-term results from VC funds against long-term gains from the S&P 500, can draw misleading conclusions. The S&P 500’s performance, showing a total return of approximately 184% from 2017 to 2024, underscores consistent returns and superior liquidity. When asset allocators use this data and benchmark it, the illiquid and long-term nature of VC investments may appear underwhelming by comparison because the data does not show the full picture.

This is problematic as it overlooks the fundamental investment strategy of VCs, which focuses on long-term, substantial returns that take time to materialise.

🕵️ 5. Potential Motivations Behind the Report

Some might speculate that the framing of the report could be influenced by larger firms aiming to acquire emerging managers at a discount. If true, this would allow these dominant players to expand their assets under management by absorbing smaller, seemingly underperforming firms at lower costs. This possibility, while speculative, suggests deeper strategic manoeuvres within the venture capital industry.

🚀 6. Moving Forward: Time for VCs to Up Their Game

The Carta report, while revealing some concerning trends, acts as a critical wake-up call for the venture capital industry. It emphasises the urgent need for VCs to evolve from traditional, subjective investment methods to more analytical and strategic approaches in response to today's complex and volatile market environment.

• Enhanced Deal Sourcing and Investment Evaluation: Transitioning from instinct-based decisions to data-driven ones is crucial. VCs should utilise advanced analytics and machine learning to uncover patterns and insights that yield a more accurate assessment of a startup's potential, thereby facilitating better investment decisions.

✅ Strategic Insight: Leveraging data allows VCs to make informed decisions that are less prone to bias and more likely to succeed.

• Strategic Development and Nurturing of Investments: Beyond financial support, VCs must play an active role in guiding their portfolio companies. This includes identifying strategic M&A opportunities, fostering synergies within the portfolio, and crafting partnerships that bolster growth and value.

✅ Active Management: Engaged and strategic management helps companies navigate growth challenges more effectively, enhancing potential returns.

• Realisation Stage: Maximising Returns Through Strategic Exits: VCs need to be adaptable in their exit strategies, aligning them with ongoing market conditions and potential buyer interests. A proactive, continuous assessment and readiness to pivot are essential for maximising returns.

✅ Dynamic Strategies: Flexibility in exit strategies ensures that VCs can capitalise on market conditions to secure the best possible outcomes.

Implementing a Holistic Strategic Management Approach: To successfully navigate these stages, VCs must adopt a comprehensive, integrated approach that employs data-driven methodologies throughout the investment lifecycle. This strategy creates a continuous improvement loop, enhancing decision-making and management practices, which leads to stronger, more resilient venture capital operations.

✅ Comprehensive Approach: A holistic strategy ensures all aspects of fund management are aligned and optimised for success.

From Echo Chamber to Data-Driven Decisions

💡 Rebuilding Trust Through Transparency

🌐 A look at Traditional VC Sourcing, the challenges and timeframes

⚡ The Impact of AI and Big Data on Venture Capital Sourcing Strategies

✨ Integrating Data Analytics into Venture Capital for Informed Decision-Making.

✨ Utilising Data-Driven Strategies to Navigate Venture Capital's Bias Challenge.

🧠 Embracing a Sophisticated Management Approach:

The imperative for VCs is clear: to thrive, a shift towards proactive, strategic, and data-driven management is essential. By smoothing out the J-curve and delivering consistent value, VCs can redefine effective venture capital management. Adopting these sophisticated strategies not only enhances financial returns but also drives innovation and contributes to broader economic growth, thereby strengthening both individual investments and the entrepreneurial ecosystem at large.

The venture capital industry needs the tools to rise to the challenge; now it’s time to build them.

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In the world of venture capital, the process of deal sourcing is as crucial as it is complex. Forming not only the necessary pipeline required but used as a signal for raising capital from Limited partners.

Traditionally dominated by personal networks and intuitive judgments…

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Venture capitalists continually balance fear and greed, emotions that guide their investments towards either success or failure. In this environment, the introduction of artificial intelligence (AI) and data analytics is a significant shift. AI and data can not only work al…

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💡 The venture capital (VC) industry is really starting to see a shift with more data-driven sourcing methods. These approaches, leveraging the power of big data, advanced analytics, and machine learning, are transforming how VCs can and should identify and evaluate potential investme…

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I wanted to share some thoughts and observations on deal sourcing as we move into a world where data and process-driven solutions will undoubtedly replace all repetitive human tasks and leave us doing just the stuff we love to do.

• 🔗 1. Network Building

• 🔍 2. Deal Sourcing

• ⏳ 3. Catchall to…

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💡 How Data-Driven Decisions Can Restore Integrity in Venture Capital.
🦋 The butterfly effect.
🔨 So what are the steps to rebuild reputation.
📊 How Startups Can Use Data t…

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My latest adventure in the startup ecosystem is Fusion42, and whilst externally it is a vibrant community of Founders, Framers and Funders, at its core, it’s all about building deep tech solutions to break the asymmetry and knowledge arbitrage that makes the ecosystem so very painful to navigate.

I want to introduce how Machine learning …

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1. Sourcing

2. Selecting

3. Servicing

🤔 Which got me to thinking, with all the data driven solutions and access to relevant information coming to market does it not make the barriers to entry so much lower?  

💡 Is the perceived value added by GPs diminishing? 

As we witness significant disruptions at the early stages of investment, the 3 S's, it's crucial for GPs to innovate and integrate solutions, particularly in the areas of value add's and investment realization, to maintain their relevance and effectiveness.

📈 I went one step further to see if this is perhaps playing out, and it is also very apparent that the number of co investments, and more interestingly Directs are growing substantially. 

🚀 It can be evidenced in the amount of corp initiatives to find suitable startups and by the amount of Family offices starting their own vehicles to invest in startups.

All very good news for Startup founders.

📊 A Data Driven Investment Era
🤝 What Exactly is Co-Investing?
💼 Why Do LPs Co-Invest?
🚀 The Unstoppable Rise of Direct Investing
🔮 Sourcing Revolutionised by Predictive Analytics
🧠 Enhanced Selection with AI
🤖 Servicing through Automated Platforms
🆕🚧 Directs bring a bunch of new challenges
🎯 Data-Driven Performance is the New Benchmark
🌐 The Data driven revolution is opening up the investor landscape for startups.  

📊 A Data Driven Investment Era

Venture capital is not just evolving; it's undergoing a bit of a revolution. Traditional fund structures are being disrupted by a rise of direct and co-investments, fueled by an explosion in data driven access and diligence the need for GP's to provide access to deals is diminishing enormously.

This shift is a transformation that's redefining the essence of investing in startups and drawing more LP's to go direct. With a growth of approximately 157% over the 10-year period this is only set to accelerate as data and access becomes more accessible at scale.

🤝 What Exactly is Co-Investing?

Imagine LPs not just funding a pool but picking specific ventures alongside GPs. Co-investing isn't new, but its adoption has accelerated. Why? Data. With data-driven insights, LPs aren't just passive money bags but strategic players, choosing where they invest based on clear, actionable insights.

💼 Why Do LPs Co-Invest?

Here's the kicker: co-investing slashes fees and aligns interests with GPs, offering a shot at higher returns. Think about it—data isn't just power; it's profit. By using detailed analytics, LPs can spot high-potential deals and dive in, often bypassing traditional fees and barriers that just add as an extra layer of fees. 

According to a 2021 report by PitchBook, co-investments have been increasingly popular, with transaction volumes growing by over 20% annually since 2016. This reflects a broader investor preference for more control and lower costs associated with co and direct investments.

Family offices often adopt a highly personalised investment approach, aligning their ventures with broader family interests and values. This personal touch not only differentiates them from institutional investors but also allows them to pursue investments that genuinely fit their long-term strategic goals. For instance, a family office might prioritise investments in sustainable technologies or health innovations, reflecting their ethical commitments or the health challenges they've faced. This deeper connection to their investments can drive more aligned interests with GPs, enhancing the potential for success and providing startups with not just capital but a partner truly invested in their mission.

🚀 The Unstoppable Rise of Direct Investing

Direct investments allow LPs to bypass funds altogether, injecting capital directly into startups. This method is skyrocketing among single and multi family offices among others where the next gen are building their own structures to invest directly in tech. 

With the realisation that the rhetoric of the three most important things GP's bring to the table being sourcing, selecting and servicing it is becoming more apparent that the barriers to entry are incredibly low. The value adds lay in more in depth relationships and understanding of not just the journey, but the most important part of investments, realisation of returns.

As the landscape of venture capital evolves, the role of family offices is increasingly significant, not just for the capital they provide but for their strategic contributions to the startups they invest in. While family offices vary greatly in their approach and focus—some looking for long-term growth, others for strategic synergies with their existing assets—they all bring a depth of patience and an eye for innovation that traditional venture capital sometimes lacks. This strategic investment is crucial as it aligns with the growing trend of direct investments, where decisions are often influenced by the potential for long-term value creation rather than immediate returns.

Backed by robust data analytics, these investors make decisions that are informed, and often more lucrative as they hold their unique thesis and black book of business relationships.

🔮 Sourcing Revolutionised by Predictive Analytics:

Data tools now enable LPs to use predictive analytics to identify emerging markets and startups before they become mainstream. This proactive sourcing strategy uses historical data trends and machine learning algorithms to forecast startup success, diminishing the traditional GP advantage of exclusive access.

🧠 Enhanced Selection with AI:

Advanced data analytics, powered by AI, are streamlining the selection process, allowing LPs to conduct deep dives into a startup’s performance metrics, founder backgrounds, and market potential. This technology reduces reliance on GP vetting, enabling more direct and informed investment decisions.

🤖 Servicing through Automated Platforms:

Automated platforms are transforming how investments are serviced, providing real-time analytics on portfolio performance and market conditions. This allows LPs to actively manage and service investments with the same level of sophistication as GPs but at a lower cost and with greater transparency.

A survey by JTC Group noted that direct investments now account for approximately 30% of total investments made by family offices.

🆕🚧 Directs bring a bunch of new challenges.

Direct investing sounds ideal, but it has many challenges. Competing with established VC firms requires more than deep pockets; it demands deep insights and a robust strategy. Data is paramount here, helping to scout, evaluate, and monitor potential investments with a precision that traditional methods can't match. But, those challenges are becoming easier to overcome as information asymmetry dissipates. The knowledge arbitrage that is more often a function of being in the market than any great insights is getting harder to use as the flow of information becomes more accessible.  The subjective decision making process to place bets is now coming down to the individual with the cheque book and a middle layer of fees and judgement is less needed. 

Direct investing by family offices brings a unique set of challenges and opportunities. These investors often engage more deeply than traditional VCs, bringing in-depth knowledge and operational expertise that can be critical to a startup's success. For family offices that are still closely connected to their wealth's source industries, this can mean direct synergies that help young companies innovate and scale. However, this deep involvement requires a robust strategy and clear understanding of the new market dynamics, as family offices navigate maintaining their legacy wealth while embracing the risks and rewards of startup investing.

🎯 Data-Driven Performance is the New Benchmark

Performance data from recent years underlines the success of these strategies. Co-investments often outperform traditional VC funds, thanks to targeted, efficient capital allocation powered by data insights. As data analytics become more sophisticated, the gap is likely to widen, cementing data's role at the core of investment strategies.

🌐 The Data driven revolution is opening up the investor landscape for startups.  

The venture capital landscape is being reshaped by the dual forces of co-investing and direct investing, driven by the transformative power of data. As we move forward, the industry's future will be increasingly dominated by these approaches, offering higher returns, greater control, and more strategic investment opportunities. For those ready, both investors and Startups  the potential rewards are substantial, but so are the demands for agility and acumen.

In this era, data isn't just a tool; it's the currency of decision-making, and those who wield it with expertise are set to lead the charge into the next generation of startup investing.

Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

If you've got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I'm keen to include it in the upcoming edition.

Please let me know what you think of it, love a feedback loop 🙏🏼

• ❤️ Love it, keep them coming!

• 👍 Great, always a good read.

• 😊 Enjoyable, but let’s see some variety.

• 👎 Okay, seen better.

• 🛑 Get a different job.
  
  Follow me on LinkedIn or Twitter to never miss an update. 

For the ❤️ of startups

✌🏼 & 💙
Derek

1. ⭐️ The norm vs. the…. mmm not sure yet, let’s see where this goes

   Examines traditional VC strategies versus the no-reserve approach.

2. ❤️ Why GPs Love to Sell the No-Reserve Dream

   Explores why General Partners advocate this strategy enthusiastically.

3. 🌡 Why LPs Might Bite

   Discusses what draws Limited Partners to consider the no-reserve strategy.

4. 🥚 The Harsh Reality of All Eggs in One Basket

   Highlights the risks and potential downsides of the no-reserve strategy.

5. 🤑 Startups Get the Short End of the Stick

   Analyzes how startups are impacted by the absence of reserved follow-on funding.

6. 🎫 No Proof This is the Golden Ticket

   Critically assesses the effectiveness and empirical support of the no-reserve approach.

7. ✅ Straight Talk for Founders

   Provides founders with practical advice on evaluating VC strategies.

8. 📢 Main Takeaway

   Summarizes the critical perspectives and final thoughts on the no-reserve strategy.

   
   

⭐️ The norm vs. the…. mmm not sure yet, let’s see where this goes

The traditional VC method for deploying capital involves stashing cash from the initial fund raise (maybe 30%) for the winning portfolio companies, follow on rounds, ensuring funds are ready for topping up investments during subsequent funding rounds to keep the momentum going and to avoid dilution. Contrarily, the no-reserves strategy is akin to going all-in at a poker game on the first hand—dumping all cash into initial investments and relying on syndicating with LP’s and others for follow on rounds via Special Purpose Vehicles (SPVs).

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For years, Software as a Service (SaaS) has reigned supreme as one of the most profitable and sustainable business models in tech. With the ability to achieve 80-90% margins and a predictable, recurring revenue stream, SaaS has reshaped entire industries and created a wealth of innovation. But despite SaaS’s dominance, it faces a new and perhaps insurmountable threat: the deflationary impact of AI and automation.With the rise of GPT models, low-code platforms, and in-house AI-driven solutions, the era of high-margin, vertical SaaS may be coming to an end.

Here’s why vertical SaaS is about to get hit hard, what this could mean for businesses and investors, and why tech may soon align with broader economic norms in ways it hasn’t before.

🚨 Why Vertical SaaS Is Set to Face Major Disruption

1. The Operational Cost Conundrum
While SaaS enables high gross margins, it does so with a hefty trade-off: operational expenditure (OPEX). For larger companies adopting SaaS solutions, the costs of managing and maintaining "bums on seats" (human users actively engaging with software tools) add up quickly. Although these tools improve efficiency, they also drive up internal costs for the companies using them. This is a particular pain point for vertical SaaS products, which target specific industries and are highly dependent on user engagement and customization.

As AI-driven solutions become more advanced, businesses will soon question whether they need to pay SaaS providers for niche products—or if they can achieve the same results using in-house AI models and automation. What was once the unique value proposition of SaaS—outsourcing software and its management—is becoming something large companies can replicate internally with their own AI capabilities.

2. GPT Models & AI-Driven Solutions as Game-Changers
The emergence of GPT models and other AI-driven solutions has fundamentally expanded what’s possible in terms of automation, efficiency, and scalability. Companies are no longer limited to buying specific software tools; they can now build intelligent, AI-powered applications to manage workflows, handle data, and even automate complex decision-making processes.

These capabilities have, in effect, expanded the engineering workforce exponentially without adding headcount. Businesses can now rely on AI to manage repetitive tasks, process data at unprecedented speeds, and provide insights that once required entire teams of employees. This development is especially problematic for vertical SaaS, which has relied on its expertise in specialized fields as a differentiator. Now, companies can leverage AI to replicate that expertise, reducing their dependence on industry-specific SaaS products.

3. Low-Code and No-Code Solutions Drive Competition
Alongside AI, low-code and no-code platforms are making it easier for companies to create their own tailored solutions without needing extensive software development resources. These platforms allow companies to rapidly deploy applications, customize workflows, and integrate new capabilities into their existing tech stack—all without the traditional costs of software development or customization.

With low-code solutions, the barriers to entry for developing and deploying customized software are lower than ever. This shift has fueled a wave of competition for traditional SaaS providers, especially those operating in vertical markets. Where once companies might have purchased a subscription for a specialized SaaS product, they can now create their own tailored software solutions that suit their unique needs, circumventing SaaS subscriptions altogether.

📉 The Deflationary Impact of AI and Automation on SaaS Margins

Deflationary pressures from AI and automation are likely to have a profound impact on SaaS margins in the coming years. While high-margin SaaS models have been the standard, with margins reaching up to 90%, the broader economic landscape doesn’t support such extremes for the long term. Historically, average gross margins across industries hover around 40-50%. As AI and automation improve, SaaS and technology companies may find their margins compressing toward these norms, particularly as customers become less willing to pay premium prices for services they can replicate with in-house AI.

SaaS has historically thrived because of its recurring revenue model, high customer lock-in rates, and scalability. But as customers move toward in-house solutions, margin compression is inevitable. This isn’t just a race to the bottom in terms of competition; it’s a fundamental shift toward aligning technology margins with broader economic realities. Here are the driving forces behind this deflationary trend:

• Increased Availability of AI Tools: AI solutions are becoming cheaper and more accessible. Companies no longer need to rely on SaaS providers for industry-specific software when they can use AI models to perform similar tasks autonomously.

• Reduced Need for Human Intervention: AI-driven software automates more tasks, reducing the need for software that merely assists human users. With AI doing the heavy lifting, the premium on SaaS tools decreases.

• Shifting Customer Expectations: As companies become accustomed to AI-driven efficiency, they may start to question the value of traditional SaaS products, especially those with high price tags and limited flexibility.

🎯 Vertical SaaS in the Crosshairs: Industries Most at Risk

The impact of this shift will be felt across all sectors, but vertical SaaS—software tailored for specific industries—is especially vulnerable. Here are a few examples of sectors where we’re likely to see this disruption first:

• Healthcare: AI models can now handle various administrative and diagnostic tasks, reducing the need for specialized healthcare SaaS.

• Financial Services: Automated financial analysis and risk assessment powered by AI threaten the high margins of SaaS products built for finance professionals.

• Retail and E-commerce: With AI able to manage customer service, sales analytics, and inventory in real time, the need for industry-specific SaaS decreases.

• Manufacturing: Low-code tools and AI-driven automation enable manufacturers to build solutions tailored to their own workflows, bypassing traditional SaaS.

Vertical SaaS providers face a new reality in which the uniqueness of their offerings is eroded by general-purpose AI models that can be fine-tuned for any industry. This not only threatens the core value propositions of these SaaS products but also their premium pricing structures.

💼 What This Means for Investors and Founders

The changing landscape of SaaS has significant implications for both investors and entrepreneurs. Here’s what to keep in mind:

• Be Cautious of High-Margin SaaS Investments: For investors, it’s critical to consider the longevity of any SaaS business’s margins, especially in vertical markets. High margins have long been a hallmark of SaaS, but these may erode as companies transition to in-house AI solutions.

• Consider Outcome-Based Models: As traditional SaaS subscription pricing faces challenges, outcome-based or usage-based models may become more attractive to customers. Businesses that adapt their pricing to reflect value delivered rather than just user access will have a competitive edge.

• Focus on Adaptability and Innovation: Entrepreneurs should prioritize flexibility, AI integration, and low-code solutions in their products. SaaS providers that integrate AI to create hybrid solutions, enabling both autonomy and customization, will be better positioned to thrive in a landscape dominated by automation.

🔚 The End of the High-Margin SaaS Era?

SaaS has been a groundbreaking business model, and its impact on technology and business is undeniable. But the era of high-margin, vertical SaaS may be drawing to a close. With AI-driven solutions, low-code platforms, and deflationary pressures on the rise, the high margins and exclusive pricing power of SaaS are set to diminish. The alignment of technology with broader economic norms is not just a trend but a significant shift in the industry’s trajectory.

Before setting out a business model or making an investment, it’s essential to consider these evolving dynamics. As margins compress and companies seek alternative solutions, the SaaS model will have to evolve—or risk losing its foothold in the market. This transformation signals an exciting new phase for tech, one where innovation, adaptability, and efficiency will become the true measures of success.

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Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

If you've got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I'm keen to include it in the upcoming edition.

Please let me know what you think of it, love a feedback loop 🙏🏼

• ❤️ Love it, keep them coming!

• 👍 Great, always a good read.

• 😊 Enjoyable, but let’s see some variety.

• 👎 Okay, seen better.

• 🛑 Get a different job.
  
  Subscribe below and follow me on LinkedIn or Twitter to never miss an update. 

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For the ❤️ of startups

✌🏼 & 💙
Derek

🔄 A Shift in VC Trends.

The 2023 PitchBook report states a 38% decrease in active VC investors. This is measured as existing funds that made less than 2 investments in 2023 and goes on to start talking about Zombie funds, all pretty negative.

🌧️ Beyond the Downturn.

The decline in VC activity isn't necessarily a crisis. The report's data doesn't give a clear reason as to the underlying status of the 38% of funds?

➡️ VCs are being cautious, absolutely.

➡️ ️ Waiting for better times to invest due to market uncertainties, bloody right.

➡️ Some have pretty much been wiped out…. Mmm yes, if they have marked their books correctly.

🤔 But 38%, as we are led to believe are gone? no, not at all.

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🕰️ A Quick Historical Snapshot.

VC trends reflect broader economic cycles. The late 1990s dot-com boom saw VC investments peak at $100 billion in the U.S. by 2000 (NVCA). Post-bubble, figures plummeted to $19 billion by 2003. The 2008 financial crisis further impacted VC, with investments falling from $30.8 billion in 2008 to $20.6 billion in 2009 (PwC & NVCA). The 2010s, however, witnessed a resurgence, driven by tech advancements, with VC funding exceeding $130 billion by 2018 (NVCA).

This history underlines VC's adaptability, with the current 38% decrease in active investors in 2023 (PitchBook) seen as part of ongoing cycles. Such insights are vital for navigating the VC domain.

🎯 Strategic Investment.

This shift shows it is less about quick investments, with more consideration to careful planning. This indicates a phase of strategic patience rather than a downturn, focusing on startups with clear profitability paths.

🚀 Founders' Approach:

For founders, it's crucial to identify active investors, for the top of the funnel focus on those recently announced, as they are in their harvest period and deploying to new opportunities.

What do you think, what is the right strat for finding and reaching investors ?

Leave a comment

Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

If you've got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I'm keen to include it in the upcoming edition.

Please let me know what you think of it, love a feedback loop 🙏🏼

• ❤️ Love it, keep them coming!

• 👍 Great, always a good read.

• 😊 Enjoyable, but let’s see some variety.

• 👎 Okay, seen better.

• 🛑 Get a different job.
  
  Subscribe below and follow me on LinkedIn or Twitter to never miss an update. 

Subscribe now

For the ❤️ of startups

✌🏼 & 💙
Derek