How AI is reshaping work and economy: Scott Vincent's vision

Bias isn’t just a human flaw — it’s now a digital one. When AI learns from biased data, the consequences ripple into real life: ❌ Wrongfully denied opportunities ❌ Reinforced stereotypes ❌ Amplified inequalities Bias in AI isn’t invisible — it’s everywhere we don’t look closely enough. If we want AI to drive progress, we can’t treat bias as a side effect. We must treat it as a design flaw. The future of AI will be written not just in code, but in values. 👉 How do you think we can best fight bias in AI — stricter regulation, better data, or more human oversight?

AI’s Clock Speed is Accelerating This chart from METR tells a profound story: The length of tasks AI can complete at a 50% success rate is doubling every 7 months. What started with GPT-2 answering questions in seconds has scaled to today’s frontier models (GPT-4, GPT-4o, Sonnet 3.7) handling tasks like training classifiers or building robust image models—things that once took humans hours. The implications are staggering: Productivity Compression → Work that took months to execute may soon take minutes. Capability Compounding → Each model builds on the last, accelerating discovery and application. Strategic Urgency → Enterprises and governments don’t just need AI roadmaps—they need adaptive AI operating systems that evolve with this pace. But speed also sharpens risks. As models race forward, governance, safety, and resilience must scale at the same rate—or faster. This is the defining paradox of our age: AI is compounding like Moore’s Law on steroids, but our institutions move at human speed. The question isn’t whether AI can keep doubling—it's whether leadership, governance, and society can keep up. 👉 What do you think? Are we ready for this pace, or is the gap widening too fast?

AI’s Clock Speed is Accelerating This chart from METR tells a profound story: The length of tasks AI can complete at a 50% success rate is doubling every 7 months. What started with GPT-2 answering questions in seconds has scaled to today’s frontier models (GPT-4, GPT-4o, Sonnet 3.7) handling tasks like training classifiers or building robust image models—things that once took humans hours. The implications are staggering: Productivity Compression → Work that took months to execute may soon take minutes. Capability Compounding → Each model builds on the last, accelerating discovery and application. Strategic Urgency → Enterprises and governments don’t just need AI roadmaps—they need adaptive AI operating systems that evolve with this pace. But speed also sharpens risks. As models race forward, governance, safety, and resilience must scale at the same rate—or faster. This is the defining paradox of our age: AI is compounding like Moore’s Law on steroids, but our institutions move at human speed. The question isn’t whether AI can keep doubling—it's whether leadership, governance, and society can keep up. 👉 What do you think? Are we ready for this pace, or is the gap widening too fast?

AI’s Clock Speed is Accelerating This chart from METR tells a profound story: The length of tasks AI can complete at a 50% success rate is doubling every 7 months. What started with GPT-2 answering questions in seconds has scaled to today’s frontier models (GPT-4, GPT-4o, Sonnet 3.7) handling tasks like training classifiers or building robust image models—things that once took humans hours. The implications are staggering: Productivity Compression → Work that took months to execute may soon take minutes. Capability Compounding → Each model builds on the last, accelerating discovery and application. Strategic Urgency → Enterprises and governments don’t just need AI roadmaps—they need adaptive AI operating systems that evolve with this pace. But speed also sharpens risks. As models race forward, governance, safety, and resilience must scale at the same rate—or faster. This is the defining paradox of our age: AI is compounding like Moore’s Law on steroids, but our institutions move at human speed. The question isn’t whether AI can keep doubling—it's whether leadership, governance, and society can keep up. 👉 What do you think? Are we ready for this pace, or is the gap widening too fast?

“Without a deliberate focus on capabilities like analytical reasoning and creativity, as well as culture and behaviours, AI projects will never deliver up to their potential.”

🤖 AI-Native Deals: When Algorithms Become Actors In 2025, AI supports diligence. By 2030, AI will not just support — it will act. 👉 Where AI shifts from tool to actor in M&A: • Diligence at Scale – Algorithms reviewing entire datasets beyond human reach. • Integration Orchestration – AI mapping cultural, operational, and legal pathways in real time. • Negotiation Dynamics – Smart contracts and algorithmic decision-making reshaping bargaining power. 📌 The thesis: AI-native deals will test the very definition of agency. When algorithms design outcomes, leaders must decide: who is accountable? 🎯 The outcome: By 2030, the frontier of M&A will not be speed or size, but the governance of intelligence itself. Those who lead in AI accountability will lead in deal-making. #MergersAndAcquisitions #Future #AI #CorporateStrategy #Transformation

I have a lot of conversations with friends about AI. Most of them tilt technical: models, tools, experiments. We’re all AI-optimists. But we’re even more human-bullish 💪. 👉 I ended up writing some thoughts down here -> https://lnkd.in/gTHnnxYh The debates don’t end in conclusions (though the premises are always fun). But they leave me thinking about relevance and trust. This isn’t new, I’ve been having versions of this same conversation with different people for a while. Maybe that’s why my friends like to poke me with it: “Hey Andres, you think AI is amazing… so what about us?” Inspired by Swyx’s three strikes rule (https://lnkd.in/g6Wy3JQW), I finally wrote it down: why curation, signal, and trust are becoming the real currencies of relevance, and why AI doesn’t erase the need for humans, it makes the human edge sharper. Any thoughts, pushbacks, or contrarian takes?

When intelligence costs $20 a month, what’s really scarce, and therefore more valuable than intelligence itself? Phenomenal article from my brother-in-law Andres Torres; Head of #AI Strategy at Norwegian Cruise Line Holdings Ltd. In this new reality, the currencies that rise above knowledge are curation, signal, and trust. - Curation reframes abundance through a unique lens. -Signal makes that perspective visible. - Trust compounds when humans put skin in the game. AI floods the world with answers. Humans turn them into meaning. That’s the edge that can’t be automated. Read below 👇🏼

The rise of 'AI slop', has created unexpected economic opportunities, demonstrating the evolving relationship between human creativity and machine automation. Read our take on how organisations can harness AI effectively whilst maintaining the human touch to combat AI slop https://lnkd.in/eefG3GD9

The AI paradox: The easier it becomes to build anything, the more critical it becomes to know what's worth building We're drunk on capabilities, but the hangover is coming. AI hasn't eliminated the need for strategic judgment - it's amplified it. Now we can fail faster, automate the wrong things brilliantly, and optimize metrics that don't matter at unprecedented scale.The technical barrier has collapsed. The strategic barrier - knowing WHEN, WHAT, WHY, and HOW to deploy these tools - is now more important then ever. Organizations will soon divide into two camps: those who used AI to amplify good judgment, and those who automated bad judgment at scale. The difference? Whether they asked "why" before "how." (Which camp is your organization in? Be honest.) The winners won't have the best AI. They'll have the best questions. Reach out if you want to discuss AI strategy - or if we humans still have purpose and relevance going forward. *Built with Claude Opus 4.1 + human judgment (practicing what I preach) #AI #AIStrategy #AITransfornation #Innovation