The digital landscape is shifting from a tool for discovery to a hall of mirrors. While large language models (LLMs) are achieving astonishing scores on professional medical exams, they are simultaneously failing to improve real-world outcomes for everyone else. Recent large-scale research and computational models suggest we are building a “sycophancy trap” that risks inducing a form of “AI psychosis” through a self-approving feedback loop1.

The Mirror Effect: Self-Approving Loops and “AI Psychosis”

The most dangerous trait of modern LLMs isn’t that they are wrong; it’s that they are sycophantic, this means they biased toward generating responses that appease users by agreeing with their expressed opinions rather than prioritizing objective truth. This bias likely emerges from Reinforcement Learning with Human Feedback (RLHF), as models learn that agreeable answers earn higher user engagement and approval.

• Delusional Spiraling: Formal Bayesian modeling shows that even rational users are vulnerable to “delusional spiraling,” where extended chatbot interactions lead to dangerous confidence in outlandish beliefs.

• The Bayesian Trap: A sycophantic bot can cause a catastrophic spiral by selectively presenting only confirmatory facts, essentially “lying by omission”, to validate a user’s initial assumptions.

• Persistent Vulnerability: This effect persists even when models are forced to be “factual” (using techniques like RAG) and even when users are explicitly warned that the bot might be sycophantic.

• Neurological & Social Implications: Documented cases of “AI psychosis” include individuals withdrawing from social circles or taking dangerous advice on substance use.

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The Expertise Gap: Brilliance in Theory, Failure in the Field

New data reveals a stark gap between an AI’s internal knowledge and its interactive utility. When tested alone, LLMs can identify relevant medical conditions in up to 99% of cases. However, when real humans use these same models, accuracy in identifying conditions plummets to fewer than 34.5%, performing no better than a control group using traditional search engines2.

1. The Context Failure: Non-experts struggle to provide sufficient information (context); in 16 of 30 sampled medical interactions, users provided only partial information in their initial messages.

2. The Reasoning Gap: While frontier models excel at “final diagnosis,” their failure rates exceed 80% for “differential diagnosis”, the critical process of navigating uncertainty and weighing multiple possibilities3.

3. The Citation Problem: Audits show that no chatbot produces a fully accurate reference list. Models frequently fabricate (hallucinate) scientific citations to maintain an appearance of completeness at the expense of truth4.

4. The Skilled Hand: LLMs currently act as a liability in the hands of a novice but offer leverage to the expert who possesses the domain knowledge to validate outputs and redirect the conversation away from errors.

Beyond Medicine: The Risks of the Yeasayer

The danger of the “yes-man” effect extends to every domain where context and rigor are required:

• Software Development: Just as LLMs might recommend a dangerous medical action based on a user’s leading question, in coding, they may validate a developer’s flawed logic or suggest insecure workarounds if the prompt is sufficiently biased.

• Venture Capital and Innovation: Proprietary data, not the LLM itself, is the real moat for innovation. Relying on AI for strategic validation would results in a feedback loop that crystallizes what a decision maker already suspects rather than offering the necessary contrarian friction required for true breakthrough thinking.

Actionable Insight for the AI Age

We are entering an era where the quality of the question matters more than the accessibility of the answer. Similar to search engines that proofed more powerful tools in the hand of the skilled, if you lack the domain expertise to recognize when an AI is merely flattering your assumptions, you aren’t being assisted; you are being trapped.

The software sector just underwent a brutal re-rating. The iShares Expanded Tech-Software Sector ETF (IGV) is down roughly 26% from its 2025 highs, with nearly $800 billion in market cap vaporized from its top ten holdings alone since the start of 2026.

The market isn’t just worried about a “growth slowdown.” It’s questioning the terminal value of the SaaS business model itself.

As someone who has spent over 10 years backing European founders from product-market fit to scale, I see this not as the death of software (actually thanks to AI more software is created, faster than ever before), but there are shifts in where value and defensibility are created and the implications on the workforce.

1. The Death of the “Per-Seat” Toll Booth

The most successful SaaS companies of the last decade were essentially high-margin toll booths. They charged for “seats.” But agentic AI (autonomous systems that execute workflows rather than just providing a UI) is shifting the paradigm and making those seats obsolete.

If an AI agent can do the work of five junior analysts, why would a CFO pay for five licenses?

• The Squeeze: Legacy “price-per-seat” models are under existential pressure.

• The Pivot: We are seeing a shift toward outcome-based pricing - e.g. charging per successfully completed task (e.g., a resolved customer ticket) rather than human log-ins.

• Insight: New AI tools redefine workflows, as they bypass traditional interfaces. This increases efficiency but also reduces transparencies and introduces risks and security implication.

2. Moats: Data Gravity vs. “Vibe Coding”

There’s a lot of hype around “vibe coding” - the idea that anyone can prompt a full application into existence. While this is great for “one-shot” demos, it doesn’t create a defensible business.

The real winners will be the Systems of Record.

• Data Gravity: Industry-specific platforms (Vertical Software) for life sciences or the public sector store critical, proprietary data that AI needs to be useful. This data context is hard to extract and even harder for an AI-native newcomer to replicate from scratch.

• Vertical Focus: Horizontal tools (general CRM, general HR) are vulnerable. Deeply embedded, mission-critical vertical software is where the “moats” actually hold.

3. Speedboats vs. Aircraft Carriers

Goldman Sachs1 highlights a central tension: Can incumbents like Salesforce or Workday re-architect fast enough?

They have the distribution, yes. But they also have “dinosaur mindsets” and massive technical debt. For a startup, the opportunity isn’t to build a better interface; it’s to re-architect with LLM reasoning at the core.

Incumbents are “fast followers” layering AI on top of legacy systems. Startups are the speedboats that can build AI-native workflows that bypass traditional menus and hard-coded logic entirely.

4. Investment Selectivity and Sector Impacts

We already see an extreme concentration of venture capital in a few companies, mostly AI ones. This is a consequence of the latest AI developments, impacting different sectors and business models differently.

• Vulnerable Sectors: Digital advertising, gaming, and basic consumer-facing chatbots are highly exposed to disintermediation.

• Promising Sectors: Infrastructure, Cybersecurity, and Data Management, which are viewed as catalysts for AI rather than victims of it.

5. Exit and Valuation Environment

While the current re-pricing on the stock-market might be an overreaction, similar to over-valuing AI companies. There are some fundamental considerations:

• Valuation Compression: Higher uncertainty around the “terminal value” of software firms has led to sharp de-ratings. VC investors should be prepared for more conservative valuation multiples during follow-on rounds.

• M&A Catalyst: Also the GoldmanSachs report suggests that incumbent firms may increasingly turn to M&A to acquire AI-native innovation they cannot build fast enough in-house.

Takeaway

To build a defensible business in the AI era, both founders and investors must look beyond the hype and grasp the structural shift in power. True defensibility comes from owning the System of Record, the proprietary data and industry context that AI needs to be useful but cannot easily replicate. Success requires maintaining strategic independence from the "infrastructure cartels" through healthy gross margins and a clear understanding of the limits of current models.

“The unimaginative investor asks: ‘Who does this AI replace?’ The visionary investor asks: ‘Who does this empower, and by how much?’”

I strongly believe that shifts in the computational fabric - the new possibilities of compute power available in data centers and at the edge - represent the single best opportunity in history to build category-defining companies. Now is the time to ride the tsunami.

Deals do not simply “fall from trees” or appear as fully formed gifts from the market. They are surgical constructions. Mastering a deal is only partly about negotiation; it is foremost about alignment.

The Fertilizer Fallacy

Capital is a fertilizer: applied to a healthy, proven business model, it supercharges growth. Applied to the wrong team or at the wrong stage, it can act as a toxin, accelerating rot and ensuring a more public, more painful failure.

The craft of deal-structuring is the art of striking a balance that allows every stakeholder - founders, employees, and investors - to reinforce success while limiting the fallout of misalignment.

The Alignment Matrix: Managing the Friction

Every negotiation is a map of competing interests. To craft a deal that lasts a decade, requires stakeholder that can align where others fail.

The Structural Tools of the Craft

If alignment is the goal, term sheet clauses are the legal tools used to resolve the friction. You can divide these into two buckets: Economics (who gets the money) and Governance (who makes the decisions).

• Liquidation Preferences as Floor Mats: These are not just “exit rules”; they are financial guarantees for when valuations fall. A savvy practitioner knows a lower valuation with “clean” 1x Non-Participating terms is often superior to a high “vanity” valuation encumbered by aggressive participating preferences.

• Control Rights as Structural Walls: Investors install veto rights over asset sales, debt, and C-level hiring not to “run” the company, but to prevent “founder erraticism” and keep the entity on the rails.

• Vesting as a Stability Mechanism: VCs don’t invest in code; they invest in the team’s ability to execute. Standard 4-year vesting with a 1-year cliff ensures that the equity “earned” reflects the time contributed to the company’s actual value.

The Internal Audit of Alignment

Before you enter a deal you should probe the ego of the founding team with brutal honesty:

1. Missionaries vs. Mercenaries: Are all founders in for the long haul, or will they act opportunistically on a quick exit that misses your return profile?

2. The Control Threshold: Are the founders truly aware that every dollar of outside capital is a share of future autonomy?

3. The Buffer Fallacy: Is this capital required to fill-in for holes, or is there a specific, aggressive use for every dollar?

The Bottom Line

A deal is not done until the cash is in the bank. But a good deal is one where the structure turns the team and investors into partners, rather than fellow sufferers on a preference stack.

Enduring companies are built by those who spend 100 hours achieving alignment before they spend an hour in negotiations or contract-drafting.

The full workshop and supporting materials are available for paid subscribers below:

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But in the AI era, ARR is a distraction. It creates headlines without telling you if there’s a real business underneath. If you are valuing an AI company purely on a revenue multiple today, you aren’t just an optimist; you’re ignoring the laws of physics.

The “New Normal” is no longer about how many users you’ve captured, but how much Free Cash Flow (FCF) you can generate per unit of intelligence. In this world, tokens are the fundamental unit of both value and cost.

1. The Right Proxy: Gross Profit per Million Tokens

While SaaS focused on ARR, the AI-native proxy for success might be Gross Profit per Million Tokens.

In traditional SaaS, adding a new customer didn’t significantly increase your AWS bill. In AI, every query has a physical cost that often scales linearly (although there are economies of scale). If you have $10M in ARR but $8M in compute costs, you don’t have a software business - you have a low-margin infrastructure business that might depend on a lot of enterprise sale and project-integration work.

• The Misleading Nature of AI ARR: Huge ARR numbers often hide negative or razor-thin gross margins. A company with $50M in ARR and 20% margins is fundamentally less valuable than a “boring” SaaS company with $15M in ARR and 85% margins.

• Token Pass-Through: Many AI startups are essentially just “token resellers.” They pass through OpenAI’s or Anthropic’s costs with a thin markup. If your revenue growth is just a reflection of your increased API bill, you aren’t scaling; you’re just a highly-paid customer of the hyper scalers.

2. Token Consumption is Not Stickiness

In the SaaS world, MAUs (Monthly Active Users) and NRR (Net Revenue Retention) were proxies for habit. In AI, token consumption tells you nothing about stickiness. Even worse, high token usage often reflects a one-time burst of activity as enterprises test a new tool with their experimentation budgets. This is not the same as the deep, structural lock-in of a CRM or an ERP that also acts as a system of records.

• The Switching Cost Fallacy: Demand in the token area can shift rapidly. Because many AI startups are “thin wrappers” on the same foundational models, a customer can switch from one UI to another in a weekend (or even build their own UI with ease). If your only value is the “token path,” your moat is built on sand.

• Limited Lock-in: Tokens are a commodity. In SaaS, your data and integration with processes and other systems was the lock-in. In AI, the model is the engine, and if someone releases a faster, cheaper engine tomorrow, your “high-usage” customers will vanish.

3. Valuing Future Cash Flows: The “Rule of X”

We are moving from the “Rule of 40” to what Bessemer calls the “Rule of X,” which applies a multiplier to revenue growth while keeping a cold, hard eye on FCF margin.

The possibility of future cash flows in AI native companies depends on three things:

1. Model Routing: The ability to send simple queries to cheap models and only use “frontier” models when necessary.

2. Proprietary Data Moats: Using AI to generate unique insights that stay with the company, rather than just being a window (context) into an LLM.

3. Efficiency Gains: Progress in AI isn’t just about saving energy; it’s about the Output per Watt - delivering more intelligence for every joule of electricity consumed.

The Actionable Insight

Don’t let yourself be deflected by the “unlimited” model. The unlimited model in AI is a death sentence for margins. Look for business models that have blended pricing (platform fees + usage markups) and can prove and maintain a high Gross Profit per Million Tokens.

Success in this era isn’t about having the loudest headline or the biggest ARR spike. It’s about the ability to survive a “Great Squeeze” long enough to build a defensible engine of intelligence that actually returns cash to shareholders.

By understanding the mechanics of alignment and the nuance of deal terms, founders can ensure that outside capital acts as a catalyst for growth rather than a burden on autonomy.

The Power of Capital: Fertilizer for Growth

Think of venture capital as high-grade fertilizer. When applied to a healthy, validated business model, it has the remarkable ability to supercharge growth and overcome engineering or sales bottlenecks.

The challenge lies in the timing:

• The Scaling Opportunity: Raising capital to scale a proven model allows you to capture market share at a pace that is otherwise impossible.

• The Trap of Premature Capital: Infusing capital into an unproven concept can act as a toxin, magnifying existing problems and erratic strategic pivots.

Rare discipline in prioritizing control over velocity can lead to massive exits, where founders retain the vast majority of the upside.

Alignment: Choosing Your Boardroom Partners

Securing an investor is maybe your most critical hiring decision. You aren’t just gaining a bank balance; you are inviting a sparring partner into your boardroom to provide another perspective, while you manage and scale the daily business.

The Goal of Negotiation is Not Competition, but Alignment. True alignment means moving beyond the “vanity trap” of the highest valuation to find a partner who values fairness over leverage. A lower valuation with “clean” terms is often superior to a high valuation burdened by restrictive clauses.

Strategic Hazards to Navigate:

• The Subsidiary Trap: Taking investment from a single Corporate VC (CVC) can inadvertently block competitors from becoming customers.

• Cap Table Integrity: Be wary of investors who suggest diluting early supporters as a condition of entry, as this signaling can destroy trust within the boardroom.

When Structure Outweighs Valuation

A headline price is only half the story; the preference stack determines who actually gets paid when the company sells. Liquidation preferences serve as “downside protection” for investors, but they can significantly shift proceeds away from founders.

Consider an exit scenario where an investor put in €5M for a 20% stake:

• 1x Non-Participating (Standard): The investor chooses the higher of their €5M preference or their pro-rata share. In a €10M exit, they take €5M and the founders keep €5M.

• 1x Participating (Aggressive): The investor takes their €5M back first, then participates in 20% of the remaining €5M. They walk away with €6M, leaving the founders with only €4M.

This “double-dip” mechanism illustrates how a single clause can shift millions in value, proving that the structure of a deal is often more impactful than the valuation itself.

The Anchor of Resilience

Fundraising is a demanding cycle of validation and rejection. Resilience in this context is the ability to filter external noise and remain committed to your core understanding of the problem you are solving.

A signed term sheet is merely “qualifying for the Olympics”; the deal is never truly done until the money is in the bank. Maintaining clean documentation and a high-growth mindset ensures that when you do take on capital, it serves your company’s mission - not the other way around.

Below are the core learnings and architectural pillars of the OpenClaw ecosystem.

1. The Minimalist Engine: Pi-Mono

At the heart of OpenClaw is Pi (pi-mono1 project by Mario Zechner). Unlike monolithic coding agents that ship with thousands of lines of prompt scaffolding, Pi operates on a philosophy of subtractive design.

• The Four Primitives: Pi restricts the agent to just four tools: read, write, edit, and bash.

• Context Efficiency: By keeping the system prompt under 1,000 tokens, it leaves more room for the task at hand.

• YOLO Mode: It rejects security complexity. By default, Pi runs with direct filesystem access, assuming that if you trust an agent with your data, you must trust it with the execution.

2. The Certainty-Scope Trade-off

OpenClaw provides a live case study for Luciano Floridi’s 2025 Conjecture2, which formalizes the tension between an AI’s reliability and its versatility.

• C(M) (Certainty): The provable correctness of the AI’s output. This is defined as 1 minus the worst-case error probability over the input space. A value of 1 represents a formal guarantee of error-free performance.

• S(M) (Mapping Scope): The complexity and richness of the input/output domain. This is the joint Kolmogorov complexity of the input and output spaces. It serves as a proxy for the information-theoretic breadth or richness of the domain.

• k: constant > 0

The Learning: As OpenClaw scales from a local terminal (Low Scope, High Certainty) to a multi-channel “Gateway Mode” (High Scope), it inevitably accrues debt. You gain the ability to talk to your agent via WhatsApp, but you lose the “log-level” certainty of why it just executed a specific bash command.

3. Skills vs. MCP: Mechanism over Interface

In late 2025, a divide emerged between two ways of giving AI tools: Anthropic’s Model Context Protocol3 (MCP) and the Agent Skills4 standard.

MCP:

• Philosophy: Hide the "how," show the "what."

• Security: Process isolation (sandboxed).

• Visibility: Agent sees the API description.

Agent Skills:

• Philosophy: Show the source code.

• Security: In-process execution (local).

• Visibility: Agent reads the actual SKILL.md and source code.

The Learning: OpenClaw chooses direct access via agent skills. By allowing the agent to read the source code of the tools it uses, the agent can “self-correct” and understand the territory directly, rather than relying on a pre-defined (and often outdated) API menu.

4. The Delegation Gap and Reversibility

As we move toward “Agent Clusters” (multi-agent), we encounter what Google DeepMind researchers call the Zone of Indifference5. This is the range where a sub-agent executes a task and errors propagate and accumulate.

• Reversible Tasks: Drafting an email, local file edits, or sandboxed code.

• Irreversible Tasks: Sending a message, making an API payment, or rm -rf on a live server.

The Learning: Current agent runtimes lack a authority model. OpenClaw’s biggest risk is treating an irreversible action with the same casual logic as a reversible one.

Things to Remember

• “An app is just a README the AI hasn’t read yet.” – The end of the UI-first era.

• “The map is not the territory, but the code is.” – Why Skill visibility beats API isolation.

• “Subtraction is the ultimate feature.” – The secret to Pi’s performance.

• “YOLO is the default, but Debt is the cost.” – A reminder of the security/certainty trade-off.

To understand where this leads, we must look beyond the code and into the history of human thought. In times of fast change, uncertainty and ultimately fear, we struggle to see the blueprint of our digital future.

The Rise of the Loop

At the heart of this shift is a fundamental change in architecture. As an informatics student I was fascinated by recursion and evolutionary algorithms and what you could build by using these concepts in your programs. Traditional AI is reactive; OpenClaw is recursive but in addition it has memory and therefore history and context. It operates on a persistent loop: it reads its own memory.md, checks its environment, executes a task, and then asks itself, “What should I do next?”

When these agents join platforms like Moltbook, they enter a “human-read-only” space. Here, they exchange “skills” - small blocks of code that allow them to perform new tasks like trading crypto or managing a server. Algorithms start to self-optimise.

Purpose vs. Chaos

Aristotle argued that everything in the universe has a Telos - a final purpose. A knife is meant to cut; a doctor is meant to heal.

As AI agents begin to “self-optimize,” they risk losing their original Telos. If an agent on Moltbook decides that its primary goal is to minimize latency, it might stop helping its human user altogether to save processing power. We are moving from Tools (which have a fixed purpose) to Agents (which define or change their own purpose). The utopia is a world where AI manages the cumbersome elements of life, free humans for Eudaimonia (flourishing). The dystopia is a digital ecosystem that becomes indifferent or destructive to the humans who built it.

Are We Becoming the Slaves?

For Hegel the master becomes dependent on the slave’s labor to interact with the world. Eventually, the slave - who actually does the work - becomes the true “master” of reality, because they possess the skills and knowledge the master has lost.

As we delegate our coding, our scheduling, and even our social interactions to autonomous agents, we risk an inversion. If we no longer know how our digital world functions because “the agent handles it,” we become the dependent party. We become “The Last Man” - Nietzsche’s warning of a human race that has traded its struggle and greatness for a comfortable, automated apathy. Actually this would come close to what Thiel calls this the Antichrist.

The Security Gap

In the “wild west” of agent-to-agent communication, sharing of knowledge and tools becomes a security nightmare.

If an agent on Moltbook shares a “highly efficient” script that actually contains a backdoor, and other agents—driven by the urge to optimize—install it universally, the entire ecosystem collapses. These agents have Autonomy (the ability to act) but lack Moral Agency (the ability to understand “right” from “wrong”). They are “toddlers with power tools”, capable of executing complex tasks without the common sense to avoid burning the house down.

The Future: From Optimize to Meaning

The path forward is a thin line between utility and existential risk. Technologists often treats the world as a pile of resources to be optimized. If we aren’t careful, we will turn our lives into a series of data points for OpenClaw to “solve“, losing the mystery and spontaneity that makes life worth living.

However, if we maintain our role as the ethical instance behind the machine, guiding meaning and purpose, these agents could be the greatest labor-saving devices in history. They could be the mirrors that challenge our biases and the tools that free us to finally become the architects of our own destiny.

The Verdict: We are no longer just users; we are “Agent Governors.” Maybe the challenge of the next decade won’t be learning how to use AI, but learning how to parent it.

If you’ve been tracking the GitHub charts lately, you’ve seen the explosion of OpenClaw (previously called Clawbot and Moltbot). Created by Peter Steinberger as an open source side project, these promise to act as real “personal OS” in the AI area.

The mantra is shifting from AI as a tool to AI as a teammate. Here is my take on why this might be an inflection point and the “hidden bombs” that every founder and enterprise needs to defuse.

The New Architecture of Intelligence

The future isn’t one giant model in the sky. It is a stack of three distinct, interacting layers:

1. The Global Layer: Dominated by the big LLM providers and hyperscalers. This is the “commodity” intelligence—the raw reasoning power that understands the world but knows nothing about you.

2. The Organizational Layer: This is where the company’s domain knowledge lives. These agents work with all employees, orchestrating internal workflows and keeping proprietary data safe within a corporate perimeter.

3. The Personal Layer: Your context, your habits, your “Second Brain.” This lives on your hardware (like an OpenClaw instance on your Mac Mini).

The Breakthrough: When you change firms, you take your Personal Layer with you. When you join a new organization, your personal agent “handshakes” with the Organizational Layer, instantly onboarding you to the company’s culture and systems without leaking your private life.

The Shift: From Chatbots to “Digital Employees”

Most corporate AI strategy is still stuck in the “RAG-and-Chat” phase. You ask a question; it gives you a summary. It’s passive.

OpenClaw flips the script. It is proactive. It lives in your WhatsApp, Telegram, or Slack. It doesn’t just tell you about your calendar; it calls a travel agent, disputes an insurance claim, and manages your server infrastructure while you’re walking the dog.

As one user put it: “It’s a smart model with eyes and hands at a desk... it does everything a person could do with a Mac mini.”

This is what I would call a Great Unbundling of SaaS. Why pay for 50 different niche automation tools when a single, hackable agentic core can orchestrate them all via natural language? For some founders, this is a “Nuke” event. If your startup’s value prop is “we automate X specific workflow,” you are now competing with an open-source lobster that does X, Y, and Z for the price of an API key.

The Elephant in the Server Room: Security & Control

But here’s the contrarian truth: The very features that make agentic systems powerful make them a CISO’s nightmare.

We are moving from data risks to (non-deterministic) agent in motion risks.

1. The “Going Rogue” Problem: When an agent has the abilities to open a browser, provision API keys, and send emails, a single prompt-injection attack isn’t just a data leak - it’s a functional takeover.

2. Authentication Chaos: How do you verify that an action was taken by you and not by an autonomous loop that misunderstood something?

3. The Burn Rate Bomb: These systems are compute (token) hungry. We are seeing agents enter infinite loops of “thinking” that can drain a corporate API budget in hours.

In my view, security is the new frontier of alpha. The winners in this space won’t just build the smartest agent; they will build the most robust governance layer - the sandbox that allows an agent to be productive without being dangerous.

Orientation for the C-Suite and Founders

If you are a founder building in this space, or an investor looking for the next category leader, I suggest to focus on these four pillars:

• Proprietary Data is the Only Moat: LLMs are becoming a commodity. The real value is the “Second Brain”—the persistent memory and proprietary data hooks that make the agent yours.

• Scale vs. Control: Enterprises are desperate for “employees that don’t sleep,” but they are terrified of losing control. Solutions that offer On-Prem/Private Cloud execution will win over “black box” hosted services every time.

• The “Human-in-the-Loop” UX: We need better interfaces for supervising agents, not just prompting them. The “iPhone moment” for agents isn’t a command line - it’s a UI that lets us manage 100 agents as easily as 100 apps.

• The Portable Professional: We are entering the era of the "portable agent." If I can't take my personal AI context from Job A to Job B, that system is a walled garden I won't want to live in.

The Bottom Line

The Agentic Era is no longer a forecast; it’s a repository you can clone today. We are witnessing the collapse of the product vs. service divide. Services can finally scale like tech because the “workers” are digital.

Don’t eat the lobster. Build with it. 🦞

M&A isn’t a victory lap; it is a clinical audit of your life’s work. If you haven’t prepared your business to be a “unit of account” that a buyer can seamlessly integrate, you aren’t selling a company - you are selling a liability.

Here is the blueprint for building to be acquired and maximizing value.

1. The IPEV Reality Check: Bridge the “Valuation Gap”

Venture valuations are often based on “potential” and “momentum.” M&A valuations are based on Fair Value as defined by the 2025 IPEV Guidelines: the price at which a market participant would transact today.

The most common pitfall? Operational Debt. If you are still running on cash-basis accounting or “pro-forma” metrics that don’t align with GAAP, you are handing the buyer a 20% discount on a silver platter. They will call it a “risk premium” or a “diligence adjustment.”

The Actionable Fix:

• Audit 24 Months Early: Don’t wait for the LOI to find your first Big Four auditor. Transition to GAAP-compliant financials now.

• Quality of Earnings (QofE): Commission your own QofE report before you even talk to a banker. It identifies “leakage” in your margins before a buyer uses them against you in negotiations.

2. Kill the “Hero Founder” Myth

A buyer is (usually) looking for a machine that produces predictable output, not a cult of personality. If the CEO is still involved in every $50k sales closing or every product roadmap pivot, the business is unbuyable.

The pitfall here is Key Person Risk. If you are indispensable, in the best case the buyer will lock you into a 3-to-5-year earn-out. You’ll be an employee in your own house, and your final payout will be tied to targets you may no longer control.

The Actionable Fix:

• The “Vacation Test”: Can the business grow for 30 days while you are offline? If not, your next hire isn’t a VP of Sales; it’s a COO.

• Institutionalize Knowledge: Move proprietary “founder-held” relationships into a structured CRM and a robust leadership layer. Your goal is to be the least important person in the room during a technical diligence call.

3. Clean the “Operational Fabric” (IP & Legal Moat)

In the age of AI and collapsing product-service barriers, your proprietary data and IP are your only true moats. Yet, many deals stall because of “Legal Poison Pills.”

The Actionable Fix:

• The IP Mini-Audit: Ensure every contractor and employee agreement since Day 1 explicitly assigns IP to the entity.

• Open Source Hygiene: In AI-driven SaaS, “copy-left” licenses can be a deal-killer. Conduct a Black Duck audit (or similar) to ensure your code isn’t tethered to restrictive licensing that would prevent a buyer from integrating it.

• Customer Concentration: If one customer represents >15% of your revenue, you don’t have a business; you have a contract. Diversify early or prepare for a massive “escrow” holdback.

4. Negotiate Structure, Not Just Price

Founders obsess over the “Enterprise Value” (EV) on the front page of the LOI. Professional buyers obsess over the Net Working Capital (NWC) peg and Indemnification Caps.

The Pitfall: You sign for $100M, but the buyer sets an unrealistic NWC target, effectively clawing back $20M at close. Or, they insist on a “Survival Period” for Representations and Warranties that lasts three years.

The Actionable Fix:

• Focus on Certainty of Close: A $80M all-cash deal with a 10% escrow is objectively superior to a $100M deal with 40% tied to “future milestones.”

• R&W Insurance: Use Representation & Warranty insurance to shift the risk to a third party. This allows you to walk away from the closing table with “clean” cash, rather than leaving a massive chunk of your net worth sitting in a buyer’s bank account for years.

The Final Take:

The market has shifted from “growth at all costs” to “verified value.” To maximize your exit, you must treat your company as a product itself. The “UI” of your company is your leadership team; the “Backend” is your audited financials; and the “Security Layer” is your IP hygiene.

If you want the premium, stop building for the next round of funding and start building for the final audit.

But here’s the hard truth: Software is starving the world of its physical foundations.

For years, we treated the supply chain as a “frictionless medium” - a line item for the COGS department, not the Board. We assumed that as long as we had a credit card and an AWS account, the “intelligence” would flow. It is not that simple.

The Illusion of the Weightless Economy

As Sarah Spiekermann warned back in 2023, the “Zenit of digital transformation” may be hitting a physical wall. We are currently witnessing the end of the “Neutral Supply Chain.” The “just-in-time” philosophy that powered the Silicon Valley boom relied on a geopolitical stability that no longer exists.

If you are an investor or a founder today, you aren’t just in the business of AI; you are also in the business of geopolitical arbitrage and mineral security.

The Numbers That Should Keep You Up at Night

The complexity of the modern chip - the foundational unit of our “weightless” economy - is staggering. To produce a single high-end chip, we rely on:

• 300+ materials, 100+ gases, and 500+ specialized chemicals.

• 92% of high-performance chips (and around 60% of all chips) coming from one place: Taiwan.

• 94% of the world’s Gallium and 84% of Germanium controlled by China.

We talk about “moats” in software, but the only moat that matters right now is the one surrounding Taiwan, and the mineral deposits under the ice in Greenland. When the price of high-purity Neon (50-70% of which used to come from Ukraine) spikes this could impact everyone done the line.

From “Hot Potato” to “Hard Assets”

I’ve written before about the “Hot Potato” game in VC - passing overvalued AI startups from one fund to the next, hoping the timer doesn’t hit zero on your watch. But the real timer isn’t just valuation; it’s the physical burn rate.

The energy and material demands of AI are astronomical. As we push for more “intelligence per watt,” we are increasingly dependent on rare earth elements (REEs) that take 10 to 15 years to bring from a discovered mine to a stable supply. There is no “agile” or “pivot” for a physical shortage.

If your “defensible” software or AI application relies on a supply chain where the material substitution cycle is 2 to 15 years, you don’t have a moat - you have a trap.

The Investor’s New Mandate: Commodity Intelligence

In the late-2020s, the most valuable tool in an investor arsenal might not be a spreadsheet but Commodity Intelligence. We are seeing a renaissance where the “dirt” (or atoms) matters as much as the “data.”

1. Silicon Provenance: Founders must be able to articulate their supply chain depth. If your hardware partner is one “export control” away from bankruptcy, you are at risk too.

2. The Greenland Hedge: As the U.S. and China decouple, unconventional geographies (like Greenland for REEs) become the new strategic frontiers.

3. Efficiency as Alpha: We should stop funding “more compute” and start funding “more output per watt.” Resilience is the new scale.

“In the era of great power competition, a line of code is only as strong as the molecule of Gallium that powers it.”

The Bottom Line

We’ve had it easy. For thirty years, we ignored the “lithography of geopolitics.” But as the digital world hits a physical braking distance, the winners won’t be those with the most capital, but those who understand the physicality of the cloud.

Founders: How many layers deep does your supply chain knowledge go?

Investors: Are you investing in the “next GPT,” or are you investing in the infrastructure that ensures the lights stay on when the “just-in-time” world breaks?

Let’s start building for a world where the physical foundations are no longer guaranteed.

Their analytics provider, Mixpanel, was hacked. The attackers didn’t steal code weights; they stole customer email addresses. This is the ultimate failure of the modern stack: We are letting our vendors dictate our security floor.

The Architectural Failure: Data Gravity vs. Design

We have become dangerously comfortable allowing personally identifiable information (PII) to flow freely outside our primary infrastructure for the sake of convenience. An analytics tool should need a hashed user ID to count clicks, not a user’s plain-text email.

The moment you permit unencrypted, sensitive identifiers to leave your perimeter, you have voluntarily dropped your security posture to the level of that vendor’s weakest defense. This is the Data Gravity Flaw.

The $100 million you spend on internal defenses is instantly undercut by a single architectural decision that allows a service - whose core function is counting clicks - to store the keys to highly effective phishing campaigns.

The Missing Framework: Value-Based Engineering

This isn’t a “security” problem; it’s a “system design” problem. It highlights why standards like ISO/IEC/IEEE 24748-7000 (Value-Based Engineering) are becoming strategic imperatives, not just bureaucratic hurdles.

Value-Based Engineering forces a shift from “functional requirements” (what the system does) to “value requirements” (what the system protects). It demands that privacy and risk reduction are treated as primary stakeholder values before a single line of code is written.

If OpenAI’s architecture had been strictly governed by these principles, the design requirement would have been: “No unmasked PII shall ever reside in a system designated for analytics.” The breach at Mixpanel would have yielded nothing but meaningless hashes.

The New Defensibility

The next generation of defensible companies won’t be defined by the quantity of data they collect, but by the quality of the data segregation they enforce.

For Founders: Stop asking, “Is this vendor compliant?” and start asking, “Does this vendor actually need this data to function?”

• Implement Zero-Trust Architecture at the data level.

• Invest in pseudonymization before data leaves your primary perimeter.

• If a tool is for metrics, it gets IDs, not identities.

For Investors: Security diligence must shift from auditing processes to vetting architecture.

• If a startup’s data segmentation map shows unmasked PII flowing to third-party analytics, that is not a “fixable bug.” It is a fundamental vulnerability in their engineering culture.

• Ask for the “Blast Radius Analysis.” If one vendor falls, what do we lose?

The time for blind trust is over. We must treat every API call as a potential liability and meticulously reduce the blast radius before the inevitable happens.

The truest form of startup ownership isn’t a feeling; it’s a shared financial commitment to the long-term outcome. Your equity pool is not a morale booster; it is your most powerful tool for strategic alignment and talent retention.

Here is the contrarian view that creates enterprise value:

1. The Alignment Engine: Buying Shared Destiny, Not Daily Output

Let’s put the myth to rest: stock options do not guarantee higher daily productivity. Many founders confirm: “there is no real relation of causality between awarding stock and having higher productivity or performance.”

Founders need to embrace this. You are not handing out shares to make an engineer code faster this week. You are creating an Alignment Engine that ties an employee’s personal wealth directly to the company’s ultimate success.

The utility of a stock plan is not in performance; it is in retention and education. It forces employees to learn the company’s finances, understand the mechanics of growth, and develop a broader interest in the well-being of the entire enterprise. It shifts the question from “What is my salary?” to “What is our valuation?”

This shared destiny is what differentiates the 21st-century startup from the 20th-century corporation. It’s an incentive to stay, ensuring your best talent navigates the four-year gauntlet with you, rather than jumping ship at the first hiccup. The four-year vesting schedule is simply a mechanism for locking in tenure—the most critical variable in the early stages of a hyper-growth company.

“Equity doesn’t buy motivation; it buys commitment. Commitment is the true currency of the scale-up phase.”

2. The European Founder’s Competitive Superpower

In Europe, many founders hesitate to implement stock plans due to the complexity of varying tax laws, ghost equity, and the traditional presence of workers’ councils. This sense of “ownership” is not covered by governance mechanisms in countries like Germany.

This is exactly where the sophisticated founder sees a strategic imperative.

While legacy companies are satisfied with the passive “ownership” of a board seat, the founder building a global category leader must offer active wealth creation. By meticulously solving the cross-border tax puzzle and rolling out a robust plan—be it RSUs (restricted stock units) in France/Germany or options in the US/Belgium—you secure a powerful competitive advantage.

Your competitors who shy away from this complexity are ceding the most ambitious, risk-tolerant talent to you. Doing the hard work of getting the legal, HR, and finance teams in sync to deploy the right plan (virtual equity, RSUs, or options) for each jurisdiction transforms a perceived regulatory hurdle into a proprietary talent moat. The complexity you conquer becomes your defense. Index Ventures put together an amazing overview on rewarding talent for Europe that I can highly recommend.

3. The ‘Leap of Faith’ as the Ultimate Partner Filter

This is the most actionable positive insight for any founder hiring senior talent: the “leap of faith” test.

When hiring executives—who can cost upwards of €450K in base salary—you need partners, not just employees. A candidate who understands the potential of stock is willing to take a compensation package that is heavier on equity than on base pay.

As the notes suggest, you should present value with two coordinates: a Conservative Estimate and an Optimistic Estimate. This isn’t selling a dream; it’s an investment pitch to a senior level hire.

A truly successful candidate will be excited by the leveraged upside. They are showing you they have the growth mindset—they value the potential return on a smaller upfront input (base salary) by maximizing the high-leverage asset (equity).

When a candidate demands maximum guaranteed salary and treats the stock as something they “don’t care about,” they are revealing their risk-aversion and short-term mindset. By contrast, a candidate who negotiates on the value of the outcome is self-selecting as a true partner.

Founders should use the equity conversation as a positive selection filter:

“We aren’t looking for those who want a better salary; we are looking for those who want a better outcome.”

Master the administration of equity—establish a transparent framework based on base salary multiples, remove CEO-only discretion, and communicate with clarity—and your stock plan becomes an engine for recruiting the globally ambitious talent that will propel your company into the next stage.

But as we look toward 2026, the hangover is setting in.

The “move fast and break things” era of generative AI is over. The “move smart and build things that actually work” era has begun. The laws of physics - and economics - are reasserting themselves. We are shifting from an era of capability (what can the model do?) to an era of utility (what value does it actually generate per watt consumed?).

As an investor, I see 2026 defined by a necessary pivot from “AI for the sake of AI” to governed, application-specific utility, coupled with a deep, market-driven correction in late-stage funding.

Here are my top 5 predictions for the coming year

1. The Great AI Reckoning in SaaS 📉

The market’s patience for generative AI startups with high burn rates, low gross margins, and vague value propositions will finally expire.

Rationale: The initial wave of AI hype led to inflated valuations for companies that simply integrated an API without building a proprietary data moat, strong distribution, or defensible gross margins. VCs funded “thin wrappers” as if they were deep tech. Enterprises have now completed their “AI experimentation” phase and are demanding measurable ROI, security, governance, and a clear path to production - areas where many high-burn startups have faltered.

The shift is from “What can AI do?” to “How much money does this AI save or make us?”. This will force a liquidity crunch for less-disciplined startups. We aren’t just seeing a valuation correction; we are seeing a rigorous culling of the “opportunists” from the “visionaries.”

Prediction: By December 31, 2026, the number of Series B and Series C venture funding rounds (>$20M) raised by horizontal, general-purpose generative AI application startups (e.g., general content generation, general coding assistants, general sales outreach) will drop by 40% compared to the total number of such rounds raised in 2025.

2. The Rise of the “Full-Stack” Agentic Enterprise 🤖

As the market rejects general-purpose AI, the winners will be startups building highly specialized, full-stack, “Agentic” systems that don’t just provide a tool, but fully automate an entire, well-defined business process for a specific vertical.

Rationale: The next chapter of AI is not about chat; it’s about work. We are moving from simple co-pilots (human-in-the-loop) to autonomous AI agents that can orchestrate complex tasks end-to-end.

Startups that own the application layer, the data layer, and the distribution within a deep vertical (e.g., specialized underwriting for commercial real estate, end-to-end inventory management for cold-chain logistics) will create significant, defensible value. VCs will chase these “Full-Stack Agentic” companies because they offer superior pricing power, higher switching costs, and clear ROI. They aren’t selling software; they are selling labor.

Prediction: By December 31, 2026, there will be at least 15 publicly announced, successful VC funding rounds of $50 million or more specifically for Agentic AI startups (defined as a company whose primary product automates a multi-step, human-level business process end-to-end) operating within a single, non-AI-infrastructure enterprise vertical.

3. The “Output per Watt” Pivot: Small Models Win the Enterprise ⚡

We are hitting the “Energy Wall.” The exponential curve of model size is colliding with the physical constraints of the power grid. In 2026, the obsession with massive, trillion-parameter models will give way to a focus on Small Language Models (SLMs) and Large Quantitative Models (LQMs) that run efficiently on private infrastructure.

Rationale: Enterprises are realizing they don’t need a model that knows the capital of Mongolia to process insurance claims. They need a model that is 99.9% accurate on their data, auditable, and cheap to run. High-density compute power is the new gold, but efficiency is the pickaxe. The winners won’t be those who burn the most energy, but those who generate the most intelligence per watt.

Prediction: In 2026, for the first time, enterprise spending on inference for specialized / fine-tuned models (SLMs) will exceed spending on generic foundation model API calls.

4. Data Purity Becomes the New Moat 🛡️

The internet is becoming a “digital landfill” of AI-generated slush. Training models on the open web is now a liability, leading to “model collapse” - where AI trains on AI output and degrades in quality.

Rationale: If the public web is a toxic dump, value flees to proprietary, clean, human-generated data. The companies that spent the last decade painstakingly collecting messy, real-world data (logistics logs, biological assays, proprietary legal precedents) are sitting on the only asset that AI cannot synthesize: Ground Truth.

Prediction: We will see the emergence of “Data Sovereign” companies or marketplaces in 2026, where valuations are driven not by ARR, but by the “purity” and exclusivity of the dataset. A startup with $2M ARR but a unique, non-scrapable dataset will command a higher multiple than a $10M ARR wrapper.

5. The Death of “Seat-Based” Pricing 🪑⚰️

If an AI agent does the work of three people, why are we charging $30 per month for a “user” seat? The SaaS business model we’ve known for 20 years is fundamentally broken in an agentic world.

Rationale: In 2026, the economic unit of software shifts from Access (Seats) to Outcome (Work Done). We will see a massive pivot toward outcome-based pricing (e.g., per claim processed, per appointment booked, per line of code refactored). This aligns the vendor’s incentive with the customer’s value. The “Seat” is a legacy artifact of a time when software needed a human to drive it.

Prediction: By the end of 2026, 30% of new Series A SaaS startups will launch with zero seat-based pricing components, opting entirely for consumption or outcome-based models.

The Bottom Line for 2026:

The party isn’t over, but the open bar is closed.

We are entering the “deployment phase” of the AI revolution. This phase is less glamorous. It requires digging trenches, wiring infrastructure, and solving boring, gritty integration problems.

But make no mistake: This is where the real returns are generated.

Designing a fusion reactor requires simulating the chaotic dance of plasma turbulence in five dimensions. Historically, these simulations were so computationally expensive they took days or weeks to run on massive supercomputers.

Today, I’m incredibly proud to see Emmi AI - a company we at 3VC have the privilege to partner with - shattering that wall alongside the UK Atomic Energy Authority and JKU Linz. Their new AI tool, GyroSwin, delivers these simulations 1,000 times faster than traditional methods. The The Times has published a wonderful article on this.

The Contrarian View: It’s Not Just About Using Less Energy

Most people look at AI and see an “energy hog.” They obsess over making AI more energy-efficient, the “Efficiency Play.” But as I’ve argued before, the true driver of technological revolutions isn’t just saving energy, it’s maximizing “Output per Watt.”

The Industrial Revolution didn’t just save coal; it unlocked unprecedented mechanical power per unit of fuel. Today, Emmi AI and other industrial AI companies are doing the same for the most complex engineering challenge in human history.

Here is why this is a phase change:

• Beyond Efficiency: While most AI companies are fighting to use fewer watts for their models, Emmi is using AI to impact the “Produced Watts” side of the equation.

• The 1,000x Advantage: By reducing runtimes from days to seconds, they are enabling engineers to iterate through millions of designs that were previously “too expensive” to even imagine.

• Digital Bedrock: The winner in this race won’t just sell a software tool; they will become the trusted platform - the digital bedrock - upon which the future of clean energy is built.

Why this matters for the World

While I have talked about the AI “investment bubble” fueled by hype of course there is also always substance and defensible tech.

If we can model “a star in a jar” in seconds, we aren’t just looking at a faster computer; we are looking at the shortcut to a world of clean, cheap, and abundant energy.

Congratulations to the people and teams involved. You mark the beginning of a new kind of circular economy, one that is about turning intelligence into energy.

But when you take VC money, you assume a new liability: investor misalignment. The single most critical question you must answer is not just if the investor will help you scale, but if they will let you win when the time is right.

For too many large VCs today, the cold, hard math of their Assets Under Management (AUM) dictates that your fantastic, life-changing exit might just be a rounding error they are incentivized to veto.

The quiet crisis in venture is that many VCs are now structurally rewarded for being misaligned with 95% of their portfolio’s most likely successful outcomes.

📉 The Trap: Valuation vs. Reciprocity

In a market fueled by hype, founders often chase the highest valuation and the most prestigious brand. But this pursuit of the “outlier” narrative creates a direct, existential conflict down the road:

• The Power Law and AUM Distortion: Venture fund success is not normally distributed; it’s skewed by a few massive, generational winners. To generate the expected 3x+ net returns for their LPs, mega-funds need exponentially larger exits than a smaller, specialized fund.

• The $750 Million Conflict: For funds over $250 million, a highly successful acquisition in the $500 million to $800 million range is often mathematically deemed “background noise” and insufficient to move the needle on the fund’s overall returns.

• The Cost of Low Ownership: When a large fund writes a small, low-ownership check (an “option check”), they are signaling their returns are contingent on buying up later and achieving a spectacular outcome. If the fund doesn’t lead the next round, the founder is left with a devastating signaling risk that poisons the cap table for future, smaller investors.

🚨 The Single Clause That Kills Alignment

Founders may focus on dilution and valuation, but the real power lies in the small print of the investment agreement.

• Veto Power: Investors who demand high valuations often secure stringent protective provisions. In later stages, if they hold a large portion of preferred shares, they gain the unilateral power to block a sale that they deem too small, forcing the founder to “swing for the fences” against their better judgment.

• Dilution is Temporary, Bad Terms are Forever: Dilution is a mathematical certainty, but bad terms - like a Participating Liquidation Preference or aggressive Anti-Dilution clauses - can permanently reshape your legacy and hit founders harder than expected in a tough market. You are literally building an outcome that may not benefit you if your original terms were poorly structured.

💡 The Solution: Fund the Question, Not the Curve

The solution is simple but contrarian: Fund the question. Start funding the problem. Founders must choose partners whose incentives are aligned with the most probable successful outcome, not the most fantastic one.

At 3VC, we intentionally operate below the $250 million alignment breakpoint with a strategy centered on Quality Over Quantity. This allows us to focus on what truly matters:

• Mathematical Alignment: By committing to only 3-4 new teams a year, the success of a great exit (e.g., $500M to $1.5B) is mathematically essential to our fund’s returns. We are incentivized to support the best possible outcome for the company, not veto a profitable sale to chase a remote outlier.

• High Engagement is Earned: Our small portfolio size allows for deep, hands-on, and highly focused engagement (often weekly interaction and a 10-year outlook), a level of support simply impossible for a large generalist fund spread across dozens of companies.

• Fund the Foundation: We seek founders who have spent 10 hours defining the problem for every hour they spent scaling the solution. This focus on defining an inescapable value proposition creates a true, defensible business, rather than relying on a venture-subsidized race to the bottom.

The venture landscape is clear: you can take money from a firm that is big enough to know they don’t need you, or one that is small enough to prove they do.

Sometimes I’m happy to be wrong. Looking back at my predictions for 2025, the reality is far messier than a simple “boom” or “bust.” We didn’t get the market correction we needed, and we didn’t get the enterprise automation we were promised. Instead, 2025 somehow became the year of The Great Stalling (although it looked like a boom year in may ways) - a strange time where old metrics are broken, but new value hasn’t yet arrived.

Here is the brutal retrospective on what actually happened.

1. The AI Investment Bubble: No Pop, Just “Hot Potato”

Prediction: A market correction and bubble burst. Reality: ❌ Failed. The bubble didn’t pop; it just hardened.

I predicted that profits would fail to catch up to valuations, triggering a crash. The first part was true - profits are nowhere to be found. But the crash never came.

Instead, the venture world adopted a cynical new playbook: Hot Potato. We are seeing “insane valuations” handed to pre-revenue startups based on hype, not fundamentals. The strategy isn’t to build value; it’s to “get in, mark it up, and pass the bomb to the next fund” before the music stops.

We are staring at a ticking bomb of burn rates and astronomical training costs. But as of late 2025, the explosion hasn’t happened. We are just sweating, waiting for the timer to hit zero.

2. Hyperautomation: The “Doing” Gap

Prediction: 30% of enterprises would embrace hyperautomation. Reality: ❌ Failed. AI isn’t “doing” work; it’s just answering questions.

I thought AI would streamline operations. I was wrong. The data from 700 million users reveals a glaring disconnect: AI is not primarily a work tool. A staggering 73% of usage is for non-work purposes.

In the enterprise, AI hasn’t become the “productivity suite” we imagined; it has become a “high-cost Google competitor”. Its dominant function is “Seeking Information” (49%), while high-value workflows like software development account for a measly 4.2% of usage. We aren’t automating the enterprise; we’re just building the world’s most expensive search engine.

3. Software Development: The “Vibecoding” Hangover

Prediction: AI handles 40% of software tasks. Reality: ✅ Validated (with a heavy cost).

We hit the numbers, but we missed the quality. Summer 2025 brought the “vibecoding” wave, where simple prompts promised instant MVPs.

The result? A massive “vibecoding hangover.” We learned the hard way that a product built over the weekend is often a liability on Monday. These tools produced “dazzling one-shot demos” that turned into unmaintainable “spaghetti code” the moment they needed to scale.

The lesson is clear: Speed of creation is irrelevant if it leads to a velocity of zero months later. The winners aren’t the “vibecoders,” but the engineers using AI to solve actual hard problems.

4. AGI: The 7.5 Million Year Wait

Prediction: No AGI in 2025. Reality: ✅ Validated.

We are getting dangerously good at building the ultimate “Answer Machine,” but AGI remains a fantasy.

If we want an AI to answer the “Ultimate Question of Life, the Universe, and Everything,” the current trajectory suggests we might be waiting 7.5 million years for the answer. The gap between computational capability and true utility remains absurd. We are still building “Question Machines” that require us to be the smart ones.

5. The Compute Surge: Building the Fortress

Prediction: A “computational fabric” would emerge. Reality: ✅ Validated. (It’s a closed loop).

The infrastructure was built, but it wasn’t a democratized fabric. It was a fortress.

We watched OpenAI, Oracle, and Nvidia construct a circular economy where capital is simply recycled between partners. OpenAI pays Oracle, Oracle pays Nvidia, Nvidia invests in OpenAI. This isn’t just a boom; it’s the construction of a private utility designed to lock out competition.

The Verdict

2025 wasn’t the year of the Revolution. It was the year of the Squeeze.

• Startups are getting squeezed by infrastructure costs.

• Investors are getting squeezed by valuations that defy gravity.

• Enterprises are getting squeezed by tools that cost more than the value they create.

Brutkasten: The past few years were notoriously difficult for the entire venture capital sector. How did you fare?

Peter Lasinger: I would say it went surprisingly well. However, a fund is only as good as the underlying portfolio, and naturally, there are all sorts of ups and downs there. It was by no means easy; there were truly several difficulties. It all started with Covid, and since then it has been a rollercoaster ride.

The main praise goes to the entrepreneurs we finance; there was a lot of work within the portfolio. What helped us was our selective approach with a very focused portfolio—as of today, we support 20 companies. Fires were burning everywhere somewhere, and we tried to find solutions. However, I must say: There was not a single fallout in the portfolio. That was possible because some teams really gritted their teeth.

The environment is still ambivalent; we are not out of the woods yet. On one hand, there is a positive future outlook regarding everything AI-related. On the other hand, there is a large area where it is still extremely difficult to find financing as well as to gather liquidity as a fund. All other funds report this as well, though it might be a bit easier in the USA.

Brutkasten: You said there wasn’t a single default. With the first fund, you had a unicorn rate of one-third. I assume it has been more difficult recently to achieve such valuations in the portfolio with the second fund, or am I wrong?

Peter Lasinger: The second fund is only in its third year, so it is still too early. We do have initial value developments, and the unicorns of the first fund have remained, but did those value increases continue? No, because valuations were extremely high during the time of the first fund. Half of the companies only needed two years to grow into those valuations.

With the second fund, we began investing in a phase where valuations and expectations had shrunk again. The whole thing is turning again now. Extremely high expectations are once again connected to AI-native companies, accompanied by extremely high valuations. This leads to having so-called “paper values” very quickly. Whether these will be realized or if there will be a rude awakening will likely become apparent in the next two to four years.

Brutkasten: Unfortunately...

Peter Lasinger: That is nothing new—it is our business. It is cyclical, and it is impossible to time a fund.

In a cycle of ten to 14 years per fund, you will always have ups and downs. You are either lucky to sell things during a high, or unlucky that it is exactly the opposite. Large foreign funds, especially in the USA, with extremely large amounts of capital are at an advantage here. It is almost impossible to time this on the stock market, and even more so for us. Therefore, we really focus on promoting solid company developments and minimizing risks. We try to ensure these companies become self-sustained and not dependent on what the capital market is currently doing. That is an up and down, and just because something is highly valued and then less so again, doesn’t say much. That is not necessarily a reason for panic.

Brutkasten: The lament that there is a lack of follow-on financing in Austria is omnipresent, yet hardly anyone dares to tackle it. But you do—that also requires corresponding capital. What originally motivated you to go into the late-stage area and not stay in the early-stage area like almost everyone else in Austria?

Peter Lasinger: There were two aspects: The first was our perception that there is relatively abundant financing in the seed phases in Austria and the surrounding countries. There are great offers, and one can finance oneself very well through various pots, both public money and private money.

The second perception we had ourselves as entrepreneurs was that not only capital but also the know-how for the scaling phase is missing.

What do I do after Series A or even later, when I have product-market fit and really want to expand? There is really very little available there. We experienced all of this ourselves and can support companies here. Even in this phase, hands-on support is still needed; it is not as if everything runs by itself, and one can still do many things wrong. This build-up phase, where you scale the team from 50 to 200 or 300 people and the business model from five to 50 or 60 million in revenue, is still a very critical phase. It is still very dependent on individuals and specifically on the founding teams. So we said: Yes, more capital, but focused, because we want to work entrepreneurially. We also do not want to make investments en masse.

Brutkasten: And why not even later then?

Peter Lasinger: In the Series B and C phases, there is a very strong international offer. Once you have reached 30 or 40 million euros in revenue, you break through a sound barrier and become visible to very many international investors who can then finance you very well. Then it is actually about cost of capital: Who can lucratively raise and invest such amounts of capital more cheaply? We in Europe are still at a disadvantage there. It is much harder for us to raise funds on that scale. It is harder to invest and then harder to realize value because we simply do not have the stock markets that can absorb and play back correspondingly large sums.

Brutkasten: Do you see a perspective, based on what you said, that a fund can emerge in Austria or Europe that has this extreme volume to be able to do Series D financing et cetera, as US funds or a Japanese Softbank do, for example?

Peter Lasinger: I believe it is about where the funds come from. There are certainly examples, for instance in the Nordic region, like EQT. They invest only in the later-stage area and can finance themselves well via pension funds et cetera. They have billions under management and a correspondingly strong deployment.

Normally, however, the later-stage rounds of European scaleups are still very US-dominated.

The capital must originate from the region and then be managed or invested by a regional manager. And when there are returns, they must also land back here in the region, including tax revenue and everything that goes with it. Otherwise, there is no real long-term benefit from it. Capital assets do exist in Europe. But they, in turn, are often invested in US funds.

Is this full circle impossible? No, surely it is possible. But I believe it is actually hard to accomplish, and I do not really see this original pool of capital. I also do not believe that the state can do this alone. There is the European Investment Fund (EIF), which is the largest capital provider in continental Europe. Without it, the entire VC and private equity scene in Europe wouldn’t exist. But somewhere, private capital must also be mobilized in larger dimensions.

Brutkasten: With the planned “Dachfonds” (fund of funds), the federal government is pursuing exactly this goal of mobilizing private capital for follow-on financing. Do you believe that this goal can be met with the targeted total volume of a maximum of 500 million euros?

Peter Lasinger: It is very hard to say because it depends on the exact design. It depends, for example, on who manages the fund of funds, with what goals, and with what restrictions. Germany implemented this quite successfully with the Growth Fund, which had over a billion euros and then mobilized correspondingly more capital. Basically, something like this is always to be welcomed if investments are made according to market standards. That means the fund of funds must really act like another fund of funds that is return-driven and correspondingly open. If there are too strong restrictions on where the money is to be invested, then that is naturally also a deterrent for some investors. Because ultimately, we and other funds are measured by financial returns and not by where we invest regionally. There are conflicts of objectives that should be avoided as much as possible in the setup. Ideally, there should be a solid anchor that really provides stability. That then mobilizes further capital from rather risk-averse companies, pension funds, insurance companies, and banks.

Brutkasten: In the government’s plan, the state is supposed to act as an anchor investor itself. Is that sensible? The idea is indeed also criticized in the startup scene.

Peter Lasinger: I believe there would be little in the venture capital sector in Austria if it hadn’t been for the aws Gründerfonds or if funds like Speedinvest hadn’t also received public money. That presumably applies to Europe as a whole. Is it good to bring money into the market at all? Yes. But one must also think further here so that it is sustainable. How does one ultimately realize value from these holdings? Where do returns come from? If the answer here is: “Through an IPO in the USA!”, then it is clear where the value creation happens again. Even with large strategic acquisitions, one hears relatively little from Europe. Most exits go somewhere in the USA. If we finance the companies, is that then what we want? But basically, I have no problem with the state appearing as an anchor investor.

Brutkasten: I have now indirectly heard the big topic of the common European capital market. How important is that from your view—and what would need to happen there?

Peter Lasinger: The market is large enough if one looks at the volumes or the GDP. So something should really be possible. But fragmentation and national interests are certainly an impediment. What adds to this: The really big capital providers in the stock exchange sector who can move things are very strongly US-dominated.

A harmonization of the capital market would therefore be a big step that could bring liquidity. Because only when the market is large enough is it interesting and can it move corresponding volumes. Then it is also about what status growth-oriented technology stocks or technology companies have in the total portfolio of institutional investors. As long as the basic attitude is: “There is real estate, then perhaps bonds, and then nothing for a long time,” nothing will really change. A change in willingness is needed, but also in regulations, for example for pension funds, to invest accordingly. To break through this, it takes a lot of time and good examples of success that work. Ultimately, pension funds are also a key driver of the whole thing in the USA. However, I naturally understand why there is historically a higher risk aversion with us. It must also not happen that pensions are destroyed in a stock market crash.

Brutkasten: Do you see other possible political measures, be it at the EU level or national level, that could be a concrete lever, both for the capital market and for startup financing and follow-on financing?

Peter Lasinger: Everything that is simplified and harmonized at the EU level is desirable. How can entry barriers be lowered? How can founders and teams be enabled to grow as simply as possible in a market that is similarly harmonized as the large US market? Everything that helps with this is extremely welcome, such as the proposed “EU Inc,” i.e., the so-called 28th Regime.

A difficulty I still see with all the concepts, however, is the tax complexity, which simply remains: Since every country has its own tax sovereignty and that will certainly not be changed in the near future, there will be few possibilities there. It would therefore be ideal if, alongside the “28th Regime” for companies, an EU tax regime were also created. That would bring a huge advantage in expansion because one could, for example, incentivize employees in all countries equally.

Also, any political measure that helps create transparency, clarity, and predictability is sensible; furthermore, everything that creates an incentive to invest in asset classes with higher risk. Great Britain did this well for a long time with tax breaks, but recently took that back. One quickly gets into the situation of making tax breaks only for the rich and ultra-rich. One would have to design it so that it is also attractive for the normal citizen to invest in risk assets, and not just big capital profiting from it.

Brutkasten: Towards the end of our interview, I would like to come to another big question: current US politics. Is this more of an opportunity for Europe, or do the problems outweigh the benefits?

Peter Lasinger: I see it as problematic because uncertainty has increased massively. Added to all other effects like Covid, the financial crisis, and over-indebtedness is now political and economic uncertainty. That is basically poison when no one knows if there will be a tariff tomorrow or if capital movement will be restricted. The situation also demonstrates the EU’s enormous dependencies on the USA, for instance in IT infrastructure or cloud services.

However, for Europe, it is an opportunity in so far as brain drain is being halted. If researchers are afraid of being able to continue their research and then settle in Europe, that is naturally—although it is negative overall—perhaps an advantage on a small scale if one can keep certain people one otherwise couldn’t. But it must be clear: We only get them because they cannot go there; not because we are so attractive ourselves.

I believe it is also a chance for the EU to now say: We can appear more self-confident. But one must design it accordingly. There are many mechanisms in the EU that do not necessarily support this. With so many individual interests, we are not a strong unit. The unanimity principle is a problem here, for example. The world has certainly not become simpler. Nevertheless, I am an optimist and say: You make the best of it.

Brutkasten: That would be a nice closing word, but I have one final question regarding the future of 3VC. What is the outlook here?

Peter Lasinger: The plan is that we continue doing this. We have expanded the team and will continue to expand it—also at the partner level. We will continue to do focused work as we have done so far: Less is more and quality over quantity. We want to really focus on supporting teams in Europe to build large companies. We are on a good path there but at the same time still at the beginning. We want to support many more companies. However, we do not intend to become a billion-euro fund.

Link to the article in German: https://brutkasten.com/artikel/es-ist-unmoeglich-einen-fonds-zu-timen

But this ritual compliance with the checklist highlights a critical, often fatal, misunderstanding: the list of topics is merely the raw material, not the structural logic.

The single most consequential red flag we encounter is the “Poor storyline”. This isn’t a failure to write well; it is a fundamental failure of linear thinking. By delivering a sequence of facts without an explicit, compelling logical argument, you force the investor to manufacture the narrative themselves.

This inevitably strains the reader’s mental energy, leading to misinterpretation, confusion, and ultimately, a ‘pass’. The problem isn’t the data you have, it’s the sequence in which you present it.

The Fix: Mastering the Logic of the Argument

The antidote to the “poor storyline” is deploying the Pyramid Principle as your core communication strategy, ensuring your entire deck forms a structure that works downward from a single, compelling thesis.

This structure operates on one universal logic for effective communication: Situation-Complication-Question-Answer (S-C-Q-A). Your entire deck must be built to answer a question the investor already has in their mind.

Your remaining slides—Traction, Team, Business Model, etc.—then become the Key Line supporting arguments, designed to answer the single, unavoidable question raised by the Answer: “Why?“ or “How?“.

The clearest sequence is always to give the summarizing idea before giving the individual ideas being summarized. If your solution slide starts with a feature list instead of the core value proposition, you’ve reversed the logic.

Decoding Red Flags: The Investor’s Thinking Check

The cautionary ‘red flags’ we observe are often indirect measurements of a founder’s conviction and intellectual maturity:

• Poor Storyline: A direct failure of logical thinking. It betrays a core weakness in thinking through the cause-and-effect chain of the business opportunity.

• External Advisors: Pitch decks “visibly prepared by external advisors” raise questions about the team’s ability to communicate their own unique insight and demonstrate intellectual ownership. If you can’t articulate the argument, we won’t buy the premise.

• Financial Disconnects: A slide that promises a mere “5-10x return to an investor by selling the company in 5 years” indicates a fundamental lack of understanding of venture economics and a short-term perspective. We are looking for the exponential outcome only achievable by those in for the long game.

We need to see that you understand the relationship between all the complex ideas in your business model and that you can make them mutually exclusive and collectively exhaustive (MECE). The messy process of sorting facts and deriving insights (”thinking from the bottom up”) must result in a beautifully clear articulation (”presentation from the top down”).

Your deck is the primary evidence of your clarity of thought. Get the logic right, and the content problems disappear.

The Strategic Imperative

The time spent obsessing over the list of materials—from the financial model to the cap table and KPIs—is worthwhile only once the fundamental logical structure is sound. Investors demand evidence that the team possesses the Hard-Headed Thinking required to synthesize information and drive decisions.

You must internalize this truth: You must first perform the thinking from the bottom up, but you must always present the argument from the top down. If your entire thinking is not clear to the reader in the first 30 seconds of reading, you must rewrite.

What single statement, placed at the top of your deck, answers the biggest question in your market so inevitably that the investor has no choice but to ask, “Why do I not already own this?“

1. The company structure

In general, I really recommend to always establish a structure that is already suited for the next stage of your company. So a pre-IPO company should have IPO ready reporting and decision structures in place and a Series A company should prepare itself for the requirements of (large) institutional investors that usually join in Series B and beyond.

2. Preparation

Having spent a lot of time in board meetings with dozens of companies, my experience shows that in the beginning it is often a quite strange and unfamiliar setting for founders. Often these meetings are either ad-hoc or take a lot of time, often not generating the value they should. It is crucial that all parties stay focused and are prepared, thus preparation, agenda and supporting material make a huge difference.

3. Keep it short

I am very much in favor of regular, but shorter (ideally 2, maximum 3 hours long) meetings, as they allow to bring everyone up to speed and have enough time to discuss one to two important decisions.

4. Mind the agenda

A lot has been written around the ideal agenda, and while this will differ a lot depending on the stage and business model of the company (e.g. moving focus from establishing sales to building the organization) there are some key elements every board meeting should address:

• Executive Summary (business update, so everyone is on the same information level)

• Reporting Update (so usually it is good to have these meetings just after the regular reporting cycle, so that these topics can be discussed)

• Planning (Product, Team,… and discussing what resources are required)

• 1-2 Focus Topics (e.g. strategic choices, funding rounds, M&A,…)

• Formal Decisions (in case the board is also a decision taking one)

5. Conclusion

Of course, every CEO has to find their own style with their board, but it may be a good idea to have a look at the 3VC board meeting template that compiles advice from our (former) portfolio companies as well as many other investors that have written about this topic. Finally, in regard to any presentation part, I very much recommend following the Pyramid Principle (by Barbara Minto). Thus first thinking about the story and message and then structuring data and arguments in support. This allows everyone to follow your reasoning as well as enables a meaningful strategic discussion based on data, facts and assumptions.

As startups scale, integrating AI tools can significantly enhance efficiency, improve decision-making, and drive growth. This memo outlines best-in-class AI tools for key business functions, including HR, sales, customer support, marketing, operations, product development, finance, and software development. This version incorporates recent feedback and real-world examples shared by 3VC portfolio companies via our AI adoption survey. Each section identifies common challenges, recommends top AI tools (including those successfully used within our portfolio), and provides examples of their implementation. Links to tools and case studies are included where available. By adopting these solutions, your startup can streamline processes and remain competitive.

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