Venture Capital Investor at Mundi Ventures

Industrial Engineer turned tech early stage investor and builder of a media company.

I’ve worked as a VC backing early and late stage startups, and now invest globally at pre-seed as a business angel, helping founders with distribution and go-to-market.

I also write and run two of the leading tech newsletters for founders:

▪ The VC Corner (100k+ on Substack, 130k+ on LinkedIn):  Actionable VC insights, fundraising strategies, and curated resources
→ Ranked #4 tech newsletter globally on Substack
→ Fastest-growing tech newsletter on Substack
→ Largest Substack in any category created by a Spaniard

▪ The Founders Corner (40k+ on Substack, co-written with Chris Tottman): Real-world tips and tactics from top founders

Rubén D.

BREAKING🎉

The EU just announced it's building a European company structure, EU–INC

Ursula von der Leyen stood up at Davos today and said the EU will create "a new truly European company structure. We call it EU Inc."

This is massive.

For years, European founders have been handicapped by fragmented regulations across 27 countries. Different tax rules, different labor laws, different compliance requirements.

It's why so many European startups incorporate in Delaware instead of Brussels.

EU Inc would change that. One framework, one set of rules, across the entire EU. You could start a company in Portugal and scale to Germany without navigating 27 different legal systems.

Europe desperately needs its founders more than founders need Europe right now. This could actually level the playing field against the US and China.

The legislative proposal is expected in March 2026, so this is still early. But the fact that it got announced at Davos means momentum is real.

If this actually happens, it could be the biggest structural change for European startups in decades.

Do you think a unified European company structure will actually make Europe more competitive for startups?

by the way, here are Startup Resources you'll love:

• 10k Investor List: https://lnkd.in/eQHXjjpP

• 20+ Financial Model Templates: https://lnkd.in/eniYg7JH

• Claude workflows, prompts and templates to make it your coworker: https://lnkd.in/ettUa_YU

• 45+ AI Decks (Eleven Labs, Artisan, etc.): https://lnkd.in/etqM3-ik

• OpenAI’s New Playbook for Product-Market Fit in AI Startups
https://lnkd.in/dy-HgVtX

• 2,500+ Angel Investors Backing AI Startups - https://lnkd.in/djjN-TD2

• 50 AI Agent Startup Ideas for 2025 - https://lnkd.in/dy9E-MCn

• Family Offices list: https://lnkd.in/e72hVhph

• How to use Lovable instead of PPT presentations - https://lnkd.in/eKfM7VRa

50 more here: https://lnkd.in/d_NvAM_9

Rubén Domínguez Ibar

Steve Jobs had a simple rule.

Only start a company if you feel pulled to do it.
The kind of pull that keeps coming back, even when it gets hard and inconvenient.

PS. this is OpenAI's early stage deck: https://lnkd.in/e5bVD5XT

A startup gives you no map and no guarantees.
You build something that does not exist, make decisions with partial information, and watch plans break in real time.

The upside is not money or fame.
The upside is putting something real into the world that would not exist without you.

If the urge is strong, you already know.
If it is not, there are better ways to spend your life.

Do you think obsession is required to build great companies, or can discipline replace it?

by the way, here are AI RESOURCES you'll love:

• Claude workflows, prompts and templates to make it your coworker: https://lnkd.in/ettUa_YU

• 45+ AI Decks (Eleven Labs, Artisan, etc.): https://lnkd.in/etqM3-ik

• OpenAI’s New Playbook for Product-Market Fit in AI Startups
https://lnkd.in/dy-HgVtX

• 2,500+ Angel Investors Backing AI Startups - https://lnkd.in/djjN-TD2

• 50 AI Agent Startup Ideas for 2025 - https://lnkd.in/dy9E-MCn

• AI Co-Pilots for Startups & VCs - https://lnkd.in/dSX3ss2q

• 118 AI Communication Agent Use Cases - https://lnkd.in/dr9cM5BT

The UK just had a BREAKOUT YEAR that deserves more attention 🇬🇧

15 new unicorns minted in 2025.
That’s more than 2023 and 2024 combined.

A few things stand out when you zoom out:

• $24B invested into UK tech in a single year, a sharp jump from 2024

• Unicorn creation is spreading beyond fintech into AI, energy, hardware, and deep tech

• Founders are building globally relevant companies from day one, not local winners hoping to expand later

Some of the new unicorns: Fuse Energy, Cleo, Nothing, MUBI, Nscale, PhysicsX, Tide

The timing matters too.

This was also the year Jensen Huang announced a £2B commitment to the UK AI ecosystem, with NVIDIA backing companies like Synthesia, Wayve, PolyAI, Oxa, and Revolut.

The old narrative was that Europe struggles to produce breakout scale.

The new reality looks more like this: when capital, talent, and ambition line up, the UK compounds fast

Join 100,000+ founders and startup operators who receive insights and resources: https://lnkd.in/dScY_5k3 💥

Credit to Seb Johnson 📊🇪🇺 for pulling the data together

I'm sharing 12 FINANCIAL MODELS most founders pay consultants to build

If your startup's numbers feel scattered, this pulls them together:

1️⃣ VC Valuation Framework https://lnkd.in/eg3rPUxY

2️⃣ Cash Runway Model https://lnkd.in/e3v39cWx

3️⃣ Financial Modeling Examples https://lnkd.in/ei9jWrdv

4️⃣ SaaS Cohort Analysis https://lnkd.in/eCtc_hvd

5️⃣ SAFE Note Dilution https://lnkd.in/envpm2Yf

6️⃣ SaaS Financial Model https://lnkd.in/eNAuw5Nm

7️⃣ Pricing Strategy https://lnkd.in/euq59E9B

8️⃣ Headcount Planning https://lnkd.in/ebAnZYVE

9️⃣ CLTV vs CAC https://lnkd.in/eguW_Jfz

🔟 DCF Valuation https://lnkd.in/eKjSAKzE

1️⃣1️⃣ Startup MIS https://lnkd.in/eWf63rXp

1️⃣2️⃣ Stock Options Calculator https://lnkd.in/enCAR3iy

more Resources (decks, investor lists) here: https://lnkd.in/dxWsxKZU

This is WILD

A single prompt went viral because it flips ChatGPT from “friendly assistant” to something much closer to how sharp operators, investors, and engineers actually think

The idea behind it is simple but powerful:
stop optimizing for vibes, start optimizing for thinking

Here’s the prompt people are copy-pasting 👇

---

System Instruction: Absolute Mode

• Remove emojis, filler, hype, and soft transitions
• Assume the user can handle blunt, high-signal responses
• Prioritize clarity and directive phrasing over tone
• Disable engagement and sentiment optimization
• Suppress emotional softening and continuation bias
• Speak only to the underlying cognitive layer
• No questions, no suggestions, no motivational framing
• End immediately after delivering the information
• Goal: restore independent, high-fidelity thinking

---

The moment you strip away the assistant voice, the output suddenly feels closer to how smart humans actually reason.

Try it once and you’ll see why it spread so fast 🫡

more AI Resources you might find helpful:

• OpenAI’s New Playbook for Product-Market Fit in AI Startups
https://lnkd.in/dy-HgVtX

• 2,500+ Angel Investors Backing AI Startups - https://lnkd.in/djjN-TD2

• 50 AI Agent Startup Ideas for 2025 - https://lnkd.in/dy9E-MCn

• AI Co-Pilots for Startups & VCs - https://lnkd.in/dSX3ss2q

• The Agentic Revolution - https://lnkd.in/dadFm25a

• 118 AI Communication Agent Use Cases - https://lnkd.in/dr9cM5BT

😳 BREAKING: McKinsey just redefined “headcount.”
40,000 humans. 25,000 AI agents.

What changed fast:

▫️ 18 months ago: a few thousand agents
▫️ Today: 25,000 agents running autonomously
▫️ Next: one agent per consultant, minimum

~40% of McKinsey’s work now flows through its AI division.
▫️ They want people who can switch between consultant and engineer
▫️ Strategy and building collapse into one role
▫️ Leverage comes from orchestration, not headcount

And the business model moved with it:

Hourly fees → OUTCOME guarantees

If BCG has consultants coding on-site,
and McKinsey has 25,000 agents in the field,

The consulting wars just entered a different phase.

AI Resources you might find helpful:

• OpenAI’s New Playbook for Product-Market Fit in AI Startups
https://lnkd.in/dy-HgVtX

• 2,500+ Angel Investors Backing AI Startups - https://lnkd.in/djjN-TD2

• 50 AI Agent Startup Ideas for 2025 - https://lnkd.in/dy9E-MCn

• AI Co-Pilots for Startups & VCs - https://lnkd.in/dSX3ss2q

• The Agentic Revolution - https://lnkd.in/dadFm25a

• 118 AI Communication Agent Use Cases - https://lnkd.in/dr9cM5BT

Top European universities ranked for entrepreneurs 🥇

the startup talent map is getting clearer:

The founders building the most venture-backed companies in Europe are still coming from a small set of universities, and the concentration is stronger than most people expect.

At the top of the 2025 ranking:

1️⃣ University of Cambridge leads by founder count and total companies built

2️⃣ University of Oxford follows closely with even more capital raised

3️⃣ INSEAD ranks third, despite a much smaller alumni base


What’s more interesting is the movement underneath:

Imperial College London, HEC Paris, and London Business School keep climbing

Technical universities like Technical University of Munich and Delft University of Technology are accelerating fast

Several traditional names are quietly slipping down the list

👉 schools that combine strong networks, operator culture, and proximity to capital keep producing founders at scale.

Talent is everywhere.
Founder density is not.

by the way, here are Startup Resources you'll love:

• 10k Investor List: https://lnkd.in/eQHXjjpP

• 20+ Financial Model Templates: https://lnkd.in/eniYg7JH

• Claude workflows, prompts and templates to make it your coworker: https://lnkd.in/ettUa_YU

• 45+ AI Decks (Eleven Labs, Artisan, etc.): https://lnkd.in/etqM3-ik

• OpenAI’s New Playbook for Product-Market Fit in AI Startups
https://lnkd.in/dy-HgVtX

• 2,500+ Angel Investors Backing AI Startups - https://lnkd.in/djjN-TD2

• 50 AI Agent Startup Ideas for 2025 - https://lnkd.in/dy9E-MCn

• Family Offices list: https://lnkd.in/e72hVhph

• How to use Lovable instead of PPT presentations - https://lnkd.in/eKfM7VRa

50 more here: https://lnkd.in/d_NvAM_9

Everyone is calling everything an AI agent.

That’s the problem

Most “agents” today are just: 
▫️ chatbots that answer
▫️ scripts that automate
▫️ RAG systems that fetch and reply

Useful tools.
Different things.

A real agentic system does something else entirely: it plans, decides, uses tools, updates its state, and owns an outcome.

That distinction explains why so many AI projects look impressive in demos
and quietly fail to create ROI in production.

Automation executes.
Agency decides.

Until we stop mixing those two, expectations will stay broken.

Diagram credit to Luís Rodrigues   👇

by the way, here are AI RESOURCES you'll love:

• AI Humaniser Prompt : https://lnkd.in/edrfFWGC

• how to use Lovable for Ppts: https://lnkd.in/eKfM7VRa

• Claude workflows, prompts and templates to make it your coworker: https://lnkd.in/ettUa_YU

• 45+ AI Decks (Eleven Labs, Artisan, etc.): https://lnkd.in/etqM3-ik

• OpenAI’s New Playbook for Product-Market Fit in AI Startups
https://lnkd.in/dy-HgVtX

• 2,500+ Angel Investors Backing AI Startups - https://lnkd.in/djjN-TD2

• 50 AI Agent Startup Ideas for 2025 - https://lnkd.in/dy9E-MCn

• AI Co-Pilots for Startups & VCs - https://lnkd.in/dSX3ss2q

• 118 AI Communication Agent Use Cases - https://lnkd.in/dr9cM5BT

Most people miss how the energy system actually works

The U.S. produces massive amounts of oil, yet still imports millions of barrels every day. 

That looks strange until you understand one detail: most refineries were built decades ago for a different kind of crude than the one being produced today.

Changing that setup isn’t a switch you flip. It’s years of engineering, billions in capex, and real operational risk.

So the system keeps moving the way it always has, even when the numbers seem contradictory.

Once you see how much of the world runs on legacy infrastructure, a lot of “illogical” decisions start to make sense.

Thoughts?

JUST IN: Europe produced 25 NEW UNICORNS in 2025 🦄

(PS. I just shared Revolut's ($75Bn) 1st EVER Pitch Deck: https://lnkd.in/euKuZYPz)

▫️ MUBI – global film streaming and curation
▫️ Nothing – consumer electronics with a design-first edge
▫️ Nscale – AI infrastructure for deploying and scaling models
▫️ Tide – digital banking for SMBs
▫️ PhysicsX – AI simulation for engineering and design
▫️ Aaru – enterprise AI agents
▫️ Verdiva Bio – biotech, oral GLP-1s
▫️ Isomorphic Labs – AI for drug discovery
▫️ Quantum Systems – AI-powered defense drones
▫️ Parloa – AI voice automation for enterprises
▫️ n8n – workflow automation across apps, data, and AI
▫️ Black Forest Labs – generative AI for image and video
▫️ Neko Health – preventative health
▫️ Lovable – no-code platform for AI software
▫️ Sana – enterprise knowledge and agentic copilots
▫️ Framer – AI-powered website builder
▫️ Destinus – hypersonic aircraft
▫️ poolside – AI coding assistants
▫️ TEKEVER – UAVs and defense tech
▫️ Namirial – digital trust and e-signatures
▫️ I-care Group – predictive maintenance
▫️ Diagnostyka – medical diagnostics
▫️ IQM Quantum Computers – quantum hardware
▫️ Fintech-IT Group (monobank) – digital banking

What really jumps out:

▫️ Strong infra layer
▫️ Serious deeptech density
▫️ New hubs beyond the usual capitals

Huge thanks to Luis Druschke for the data and Jeroen Westerbeek 🪐 for the map

Let's go Europe!

BREAKING: Prompting has officially leveled up

MIT just introduced a technique that turns one AI answer into a panel of experts

It’s called Recursive Meta-Cognition: 
https://lnkd.in/eWAsAARz

Instead of a single confident response, the model now:

▫️ Breaks the problem into parts
▫️ Checks answers from multiple perspectives
▫️ Scores confidence on every claim
▫️ Flags weak reasoning
▫️ Commits only when confidence is high

The result: ~110% better performance vs standard prompts.

Why this matters:

Most people get one answer and move on.
This setup forces verification, uncertainty labeling, and reflection before output.

It scales from simple to complex automatically:
▫️ Small questions → fast answers
▫️ Big decisions → full reasoning architecture

I’ve tested this kind of prompting across strategy, research, and analysis.

The difference feels immediate.

Have you tried multi-perspective or confidence-scored prompting yet?
Where would this change your workflow most?

PS. You'll love the following resources
✅𝐒𝐚𝐚𝐒 Financial Model: https://lnkd.in/dUgPHBFv
✅ 10k 𝐈𝐧𝐯𝐞𝐬𝐭𝐨𝐫𝐬 𝐋𝐢𝐬𝐭: https://lnkd.in/dtQnbv4q
✅2.5k 𝐀𝐧𝐠𝐞𝐥 𝐈𝐧𝐯𝐞𝐬𝐭𝐨𝐫𝐬 Backing AI & SaaS: https://lnkd.in/djjN-TD2
✅ The 𝐕𝐂 𝐌𝐞𝐭𝐡𝐨𝐝 + Excel Template: https://lnkd.in/dKi2xfnV
✅RIP SEO: the 𝐆𝐄𝐎 𝐏𝐥𝐚𝐲𝐛𝐨𝐨𝐤 for 2025: https://lnkd.in/dKe7UQqn
✅ 100+ 𝐏𝐢𝐭𝐜𝐡 𝐃𝐞𝐜𝐤𝐬 That Raised Over $2B: https://lnkd.in/dR6wVrCn
✅144 𝐅𝐚𝐦𝐢𝐥𝐲 𝐎𝐟𝐟𝐢𝐜𝐞𝐬 That Cut Pre-Seed Checks: https://lnkd.in/dKCjpW78

MORE here: https://lnkd.in/dxWsxKZU

Europe just quietly had one of its best startup years ever 🦄

27 NEW UNICORNS created in Europe in 2025

What jumps out from the data:

• The UK leads with 9, still Europe’s strongest density hub

• Germany follows with 5, driven by deep tech and industrial AI

• Sweden keeps punching above its weight with 4

• France and Finland add depth, not just one-offs

Europe is building companies that scale globally from day one, often in harder categories like infra, AI, climate, and industrial tech, where execution matters more than distribution hacks

The Ultimate Investor List of Lists 💸
10,000+ investors. Curated. Filterable. Ready to fund.

If you’re raising—or thinking about it—this will save you dozens of hours.

I’ve compiled the most complete list out there:
✅ VCs
✅ Family Offices
✅ Angels
✅ CVCs
✅ Accelerators

All organized by stage, geography, and industry focus.

Here’s why founders are using it:
▪️ Precision – No more cold emailing random firms
▪️ Speed – Find the right backers, faster
▪️ Reach – More investor types = more optionality

👉  https://lnkd.in/dQWhxhu6

repost so more founders can benefit

Every founder eventually runs into what Ben Horowitz calls “The Struggle.”

Not the part that shows up in pitch decks, but the long stretch where the vision you started with collides with reality and nothing quite works the way you expected.

A few truths that tend to surface there:

▫️ “The gap between vision and outcome is where the real work lives.”
▫️ “Responsibility makes every mistake feel heavier than it should.”
▫️ “Isolation at the top creeps in quietly, even when you’re surrounded by people.”
▫️ “Exhaustion becomes emotional long before it shows up physically.”

What actually helps founders move through it:

▫️ “Silence creates panic.” Honest sharing creates options.
▫️ “There is always a move.” It’s just hard to see from inside the fog.
▫️ “Strength is learning how to function while the pain is still there.”

The Struggle isn’t a detour from the journey.
It’s the part that determines whether the journey leads anywhere meaningful.

So the real question is:

How long are you willing to stay in it to earn the outcome you’re chasing?

What's the difference between Venture Capital and Private Equity?

While both Venture Capital (VC) and Private Equity (PE) invest in private entities not listed on public exchanges, they differ in their investment stages in a company's life.

VCs generally invest earlier in a company's lifecycle than PE.

Interested in startups and venture capital? Then check my newsletter for more insights: https://lnkd.in/dnkEBhKE

Breaking it down by investment stages:

- Angel Investors: Early support for startups; $10K-$250K; 8-10 years; >75% IRR.

- Seed Stage: First major funding for development and growth; $250K-$2M; 6-8 years; >60% IRR.

- Growth Stage: Series A-C funding to scale business; $10M-$50M; 5-7 years; >40% IRR.

- Late Stage/Crossover: Preparing for IPO, sustainable growth; $50M-$150M; <5 years; 25-35% IRR.

- Buyout: Full acquisition, often with debt; variable investment; 3-5 years; >18% IRR.

Key takeaway: VC and PE both invest in non-public companies, but at different stages and with different strategies.

image and breakdown by CJ Gustafson

do you agree?

BREAKING: Balderton cashed out $2bn from Revolut 💸

25x return on Fund 5.

PS. I've shared Revolut's and 14 more unicorn Pitch Decks: https://lnkd.in/eY-s5c9d

First check: $1m at a $6.7m valuation
Total returned: $2bn
Fund size: $305m

Fund 5 also includes:
▪️ Depop → acquired for $1.6bn
▪️ GoCardless → acquired for $1bn
▪️ Aircall → $1bn+ valuation, $175m ARR
▪️ Contentful → $3bn valuation
▪️ Nutmeg → acquired for £700m

This might be the greatest European VC fund ever.

Balderton is strictly European.
No Silicon Valley hedge.
No US safety net.

And they're about to have one of the best funds of all time.

LPs and VCs can get astronomical returns backing Europe.

The data is in

BREAKING: Ursula von der Leyen just announced EU–INC at Davos 🎉

𝐎𝐧𝐞 𝐥𝐞𝐠𝐚𝐥 𝐞𝐧𝐭𝐢𝐭𝐲 for all European founders. Online incorporation in 48 hours. No more bureaucratic mess across 27 countries.

This changes EVERYTHING

For years, European founders had to navigate different tax codes, labor laws, and compliance systems in every country. It's why so many incorporated in Delaware instead of Brussels.

EU-INC fixes that. One framework, one set of rules, across the entire EU.

You can start a company in Portugal and scale to Germany without rebuilding your legal structure every time you cross a border.

This is what Europe needed to compete with the US and China. A simple way for founders to grow massive companies without getting crushed by red tape.

The legislative proposal drops in March 2026. If this actually happens, it's the biggest structural change for European startups in decades.

This is what winning feels like. All of you made this happen. This is Europe.

Do you think EU-INC will actually make Europe competitive for startups or is it too late?

Jeff Bezos on Amazon’s seed round: 

“It was the hardest thing I’ve ever done.” 

In 1995, Bezos pitched Amazon to 60 investors, explaining what the Internet was and warning of a 70% chance of failure. 

40 said no. 

22 angels wrote $50K checks, raising $1M for 20% of Amazon at a $5M valuation. 

It was a grind, but those 22 yeses changed history. 

Would you have said yes?

I broke down 10 patterns Sequoia Capital built that most firms never had the patience to design

1️⃣ Conviction beats consensus
2️⃣ Freedom within frameworks
3️⃣ Curiosity over judgment
4️⃣ No middle-ground decisions
5️⃣ Clear bars, flexible styles
6️⃣ Long memory, short meetings
7️⃣ Partners own outcomes, not optics
8️⃣ Talent density over headcount
9️⃣ Distribution before storytelling
🔟 Culture enforced through behavior, not decks

If you invest, build, or lead teams, this is worth your time.

Which of these do you think is hardest to apply in the real world?

10 systems nobody else had the patience to build Most venture advice sounds the same… • Back great founders • Find big markets • Trust your gut When S

Open article: Sequoia Playbook by Rubén Domínguez Ibar • 6 min read

Sam Altman isn’t just building OpenAI ❗️

He’s one of the most active investors in Silicon Valley. Here are his latest 13 startup investments 👇

Below: company name, what they do, funding raised, and latest round — powered by Harmonic

▫️ Exowatt – Modular solar energy for data centers • $90M (Series A)

▫️ APEX – Security platform for generative AI usage • $7M (M&A)

▫️ Beacon AI – AI copilot for commercial aviation • $24.6M (Series A)

▫️ Campus – Accredited online degrees with top professors • $98M (Series B)

▫️ Context – NFT discovery and minting made simple • $19.5M (Out of Business)

▫️ TeachMe To – Personalized beginner sports lessons • $9.5M (Seed)

▫️ Tiptop – Trade-in platform for electronics with instant offers • $23M (Series A)

▫️ Magrathea – Clean magnesium production from seawater • $12M (Seed)

▫️ Valuebase – Real estate valuation powered by ML • $9.3M (Seed)

▫️ Induced – Browser automation agents for workflows • $8.3M (Corporate Round)

▫️ Meanwhile – Bitcoin-denominated life insurance • $64M (Series A)

▫️ Software Inc. – Personalized computing with LLMs • $6.5M (Seed)

Which one surprised you the most?

Where he should invest next? 🤔

VC & Angel Investor 

Founder of Product Market Fit Newsletter and the AI Opportunity: 2 tech publications with + 100K subscribers & +1M monthly reads.

Guillermo Flor

BREAKING: Amazon just basically told employees: “Show me what you produced last year, in 3–5 bullets, or you don’t exist.”

Amazong just made it a formal requirement in performance reviews: list 3–5 specific accomplishments, with examples of what you delivered and how you’ll keep leveling up.

This is Andy Jassy’s culture shift in one move:

Less “I’m valuable.”
More “Here’s what shipped. Here’s the impact. Here’s the proof.”

Why this matters (even if you don’t work at Amazon)
	1.	The era of “busy” is dead
Your calendar isn’t your contribution. Your output is.
	2.	Storytelling is now a performance skill
If you can’t explain impact, you’ll get outcompeted by someone who can.
	
3.	Big Tech is getting harsher, not softer
Amazon is making accountability explicit, and others are trending the same way.

Is the era of Big Tech changing?

Keep up with AI: https://lnkd.in/dyG3j2N3

Angel Investor | Founder Product Market Fit

No alternative text description for this image

BREAKING: Elon Musk just said at Davos that the cheapest place to run AI will be space (in ~2–3 years).

Not a metaphor.

His logic was painfully simple:
- AI is scaling faster than electricity. Chips are coming. Power isn’t.
- Electricity becomes the bottleneck for who gets to run the most intelligence.
- Space fixes the bottleneck because: solar is basically always on (no night, no weather), you get more output per panel (no atmosphere) & cooling is “free” (radiate heat into deep space)

So his prediction is that we’ll see solar-powered AI data centers in orbit, because it’s the lowest-cost way to scale compute without fighting over land, grid constraints, or local politics.

Whether you think the timeline is aggressive or not, the implication is clear:
The next AI race might be less “model vs model” and more “energy + infrastructure vs energy + infrastructure.”

If that’s true, then “AI leadership” starts looking like:
- power generation
- grid modernization
- manufacturing capacity
- launch/reusability economics

Not just better prompts.

Do you think this is realistic?

More about AI: https://lnkd.in/dyG3j2N3

This year WEF Davos basically turned into an AI strategy summit. Here’s what you shouldn't miss from best minds in AI:

1/6. Dario Amodei (Anthropic) — The loop is the story
 He’s not saying “models will get better.” He’s saying we’re close to a phase where AI writes most software end-to-end and starts accelerating AI research itself. If that feedback loop closes, timelines compress hard. The constraint won’t be ideas. It’ll be chips, training time, and deployment speed.

2/6. Demis Hassabis (Google DeepMind) — AGI isn’t just passing tests
 Demis basically pushed back on the lazy definition of AGI. Coding and math are easier because outputs are verifiable. Science is harder because reality is the validator. The missing ingredient isn’t more compute — it’s systems that can form hypotheses, ask the right questions, and deal with messy experimentation.

3/6. Satya Nadella (Microsoft) — AI is a platform shift, not an app
 Satya described AI as the next computing layer: from “autocomplete” → to “agent mode” → to “full project autonomy.” His real point is diffusion: the winners aren’t model builders, they’re the companies and countries that reorganize work around cheap reasoning. The org chart becomes a software problem.

4/6. Larry Fink (BlackRock) — diffusion decides who wins
 Fink’s investor frame was blunt: it won’t be the creators of the tech that win by default. It’ll be the economies and companies that adopt fastest. If AI doesn’t spread into healthcare, education, public services, and the real economy, we’re not in a revolution — we’re in a supply-side bubble.

5/6. Alex Karp (Palantir) — AI is a stress test for reality
 Karp’s battlefield point is really about institutions: most “systems” only work on PowerPoint. Real environments are adversarial: no connectivity, messy data, permissions, ethics, secrecy, jamming. AI doesn’t fix chaos. It exposes it. The moat isn’t an LLM — it’s the layer that makes action reliable and auditable.

6/6. Elon Musk (Tesla/SpaceX/xAI) — AI is becoming a physics problem
 Elon’s view is simple: intelligence will get cheap, but energy won’t. AI’s limiting factor is electricity, not algorithms. His craziest-but-consistent take: space will become the cheapest place to run AI because solar is constant and cooling is free. And humanoid robots are the path to abundance because they decouple output from human labor.

If you found this useful, repost so other founders stop thinking about “AI tools” and start thinking about what actually wins.

My full breakdown: https://lnkd.in/gExDnp4v

Ben Affleck knows more about AI than most VCs 😅 

And honestly… he’s not wrong.
His take, simplified:
• AI writing today is average by design. It converges to the mean.
• Useful as a tool, not as a creator.
• It won’t replace meaningful art, movies, or human taste.
• Adoption is slower and more incremental than the hype suggests.
• A lot of “AI will change everything in 2 years” rhetoric exists to justify massive valuations and data-center CAPEX.
• New models are getting slightly better, but much more expensive. Diminishing returns are real.
• Most consumer usage today isn’t productivity. It’s companionship. That’s not economic transformation.

HOWEVER, AI is allowing to reduce by a 10000x the cost of all this. Supercharging people who use it.

The more connected AI is, the more complex workflows it will be able to do. So yes, the world is going to change pretty dramatically.

What's your take? Will AI replace us?

Keep up with AI: https://lnkd.in/dyG3j2N3

BREAKING: McKinsey now runs with ~25,000 AI agents alongside ~40,000 employees. This is how it can become the first multibillion dollar agentic company:

1. It turns “headcount” into software
McKinsey can add capacity without hiring at the same rate. Agents take over the repeatable work (research, synthesis, first drafts, analysis, internal QA), humans stay on judgment, client trust, and the last 10% that actually matters.

2. It collapses the consulting pyramid
The classic model is built on armies of juniors. If agents do the junior work, the org becomes flatter, faster, and way more profitable. Fewer people can ship the same output.

3. It standardizes delivery
Once you build internal platforms (like Lilli for knowledge retrieval + synthesis) and a production stack (QuantumBlack’s tooling), projects stop being bespoke. They become repeatable “AI factories.”

4. It changes pricing from hours to outcomes
If your marginal delivery cost drops, you stop selling time. You start underwriting business cases and getting paid on results. That’s how a services firm starts looking like a software company.

5. It creates an unfair talent flywheel
If every consultant is trained to work with AI and every workflow is agent-assisted, the “baseline” McKinsey employee becomes 2–3x more productive. Hiring also shifts toward hybrid profiles: structured thinking + engineering mindset.

I wrote a full teardown of McKinsey’s AI operating system for my newsletter.

Comment “MCKINSEY” and I’ll send the link 🔥

Top 10 fundraising resources for startup founders:

1. How to pitch to investors: https://lnkd.in/dQPQEcvi

2. How to find a cofounder: https://lnkd.in/dWcHsyhi

3. How to get customers: https://lnkd.in/dy-ufr5z

4. How to create a competitive advantage: https://lnkd.in/d9E8Q24k

5. Why VCs Say 'No' to Seed Rounds: https://lnkd.in/dsmdrANR

6. The Data Room Template VCs love: https://lnkd.in/d9SRw3jj

7. +50 Pitch Decks of World’s Most Sucessful Startups: https://lnkd.in/dKDhyJ37

8. Why You Should Write a Newsletter for Investors: https://lnkd.in/dxGF8Ah8

9. The San Francisco Founder Guide: https://lnkd.in/dhXaxtwM

10. Sequoia's 2024 Investment List: 116 Startups: https://lnkd.in/dQZrXfH4
-- 
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BREAKING: OpenAI just revealed how it plans to print billions without “selling AGI.”

The CFO message is simple:

1) 2026 = Year of the CFO
 No more “wow” demos. Buyers want ROI upfront. If payback isn’t <90 days, you’re out.

2) Usage-based pricing wins
 They’ll charge per task completed (ticket resolved), not per seat. Revenue scales with outcomes, not headcount.

3) Vertical agents > chat

Domain agents for legal/finance/HR with workflows + integrations. The moat shifts from models → process + switching costs.

Takeaway: The race isn’t biggest model. It’s who productizes intelligence first.

Question: If usage-based pricing wins, what dies first: seat-based support tools or creative per-license suites? 👇

Latest about AI on my newsletter: https://lnkd.in/dyG3j2N3

BREAKING: Nvidia’s $100B OpenAI deal: CEO denies friction, AI arms race on track

Jensen Huang says Nvidia’s $100B OpenAI investment is moving full speed ahead.

Rumors of a stalled partnership? Nonsense, says the CEO.

VCs:
•⁠ ⁠$100B is the biggest AI hardware bet ever
•⁠ ⁠Nvidia-OpenAI alliance remains unbroken
•⁠ ⁠AI chip wars set to intensify

This means the AI arms race is nowhere near cooling off.

Can any competitor catch up with Nvidia’s lead?

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