The Moment Everything Changes

You know that feeling when you realize the game has fundamentally changed while you were still playing by the old rules? That’s what’s happening with artificial intelligence right now. Not in some distant future. Not in 2045 or 2050. We’re talking about artificial general intelligence (machines that can match and exceed human cognitive abilities) arriving between 2027 and 2032. That’s basically tomorrow in business planning terms.

Here’s what should keep you up at night (in a good way): OpenAI’s latest o3 model scored 87.5% on ARC-AGI benchmarks. For context, average human performance is 85%. Let that sink in for a moment. We’ve crossed the line from “AI as a tool” to “AI as a colleague”. Soon, AI may be the smartest entity in every room. Even the skeptics have compressed their (original) timelines: Geoffrey Hinton went from saying “30-50 years” to “5 to 20 years” in just two years. Demis Hassabis at DeepMind? He’s saying 3-5 years, and this is the guy who just won a Nobel Prize for solving protein folding.

But here’s the kicker: while everyone’s debating whether AGI is “really” coming, smart companies are already experiencing 10x, 20x, even 50x productivity gains in specific functions. GitHub Copilot users are writing code 55.8% faster. IBM is on track to save $4.5 billion by 2025 through AI automation. BCG consultants using AI are completing tasks 25% faster with 40% higher quality. This isn’t science fiction, it’s happening in real companies, with real P&L impact, right now.

The Path to AGI: Separating Hype from Engineering Reality

Let me paint you a picture of what’s actually happening in the AI labs right now. We’re not talking about incremental improvements anymore. OpenAI’s o3 model went from 5% to 87.5% on reasoning benchmarks in a single year. That’s not evolution; that’s a phase change. On PhD-level science questions, these models now score 70% accuracy compared to expert humans. On frontier mathematics problems that stump professional mathematicians, they’re solving 25% of them.

Think about what this means from first principles. For decades, we assumed intelligence was this mystical, irreducible human quality. Turns out, it’s more like an engineering problem with specific milestones. And we’re checking off those milestones frighteningly fast. The infrastructure tells the story: training compute is growing 4.1x annually and has been for 15 years straight. By 2028, we’ll have $10 billion training runs. By then, AI will consume 4% of all US electricity. You don’t invest those resources unless you’re building something transformative.

Dario Amodei at Anthropic (whose company just hit a $183 billion valuation, by the way) puts it best: “We’ll get there by 2026 or 2027.” Not “might.” Will. The convergence is clear: $1.5 Trillion in AI spending in 2025, massive capital deployment, breakthrough after breakthrough in reasoning capabilities, and infrastructure scaling at rates that would make Moore’s Law blush. The window is 2027-2032, with smart money betting on the earlier side.

First Principles of Cognitive Automation: What Work Remains Uniquely Human?

Here’s where things get really interesting. What happens when intelligence becomes as cheap and abundant as electricity? To understand this, we need to break down cognitive work from first principles.

MIT’s research with Stanford HAI and Berkeley identified something fascinating: AI absolutely demolishes humans at knowledge and comprehension tasks – we’re talking 99.5th percentile performance on working memory and information processing. But flip the script to perceptual reasoning about the real world? AI drops to 0.1-10th percentile. It’s like having a colleague with a photographic memory and lightning-fast calculation who somehow can’t understand why people cry at movies or why a sunset is beautiful.

The EPOCH framework from MIT identifies five areas where humans maintain what economists call “comparative advantage”: Empathy (emotional connection), Presence (physical collaboration), Opinion (values-based judgment), Creativity (novel problem-solving), and Hope (visionary leadership). Notice something? These aren’t temporary limitations waiting for the next model update. They’re fundamental differences in how humans and machines process reality.

But here’s the crucial insight most people miss: even when AI can do everything humans can do, economics ensures humans remain valuable. It’s the principle of comparative advantage. It’s the same reason lawyers don’t type their own documents even though they can type. Resource constraints create opportunity costs. AI might be better at everything, but it can’t do everything simultaneously. The “long tail” of rare, contextual tasks ensures human relevance. We’re not heading for mass unemployment; we’re heading for radical specialization.

The 10x Organization: Restructuring for AI-Amplified Productivity

Let me tell you what’s happening inside companies that get this right. They’re not getting 10% improvements or even 50% improvements. They’re getting 10x in specific functions, and it’s absolutely wild to watch.

Take software development. GitHub Copilot delivers 55.8% faster code completion in studies, but I know CTOs reporting their best developers are now 5-10x more productive. How? They’re not just using AI to write code faster; they’ve completely reimagined the development process. One developer with AI can now do what used to take a team. Y Combinator’s latest batch has startups where 95% of code is AI-written. These companies reach $10 million in revenue with fewer than 10 people.

IBM isn’t messing around either. They’re projecting $4.5 billion in productivity gains in 2025. They’ve automated 94% of HR inquiries. Customer service? 70% resolved without humans. But here’s the key: they didn’t just buy some AI and call it a day. They followed what BCG calls the 10-20-70 rule: 10% on AI licenses, 20% on technical integration, and 70% on people and process transformation. That’s the difference between companies getting incremental gains and those getting transformational results.

The consulting world shows what’s possible when knowledge work gets the AI treatment. BCG ran a study with 758 consultants using AI. Results? 25.1% faster completion, 40% higher quality. But here’s the kicker: below-average performers improved 43% while top performers only improved 17%. AI doesn’t just boost productivity; it democratizes excellence. The skill gap is compressing. Average becomes exceptional. Exceptional needs to redefine itself.

Competitive Dynamics: Winner-Take-All Effects of Early AGI Adoption

Now, let’s talk about why this creates winner-take-all dynamics that should either terrify or exhilarate you, depending on where your company stands.

The economics are brutal. Training a frontier model costs hundreds of millions. Running it? Near-zero marginal cost. This creates extreme economies of scale. OpenAI went from zero to $5 billion in revenue faster than any company in history. They hit 100 million users in 2 months. Every user makes their model better, which attracts more users, which makes the model better still. It’s a flywheel spinning at incomprehensible speed.

Look at the numbers: Digital leaders are pulling away from laggards at an accelerating rate. The performance gap has widened 60% since 2016. In insurance, digital leaders show 6x higher shareholder returns. In banking, they deliver 8% annual returns versus 5% for laggards. This isn’t a temporary advantage that competition will erode. It’s compounding. Every day you wait, the gap gets wider.

But here’s what’s really wild: being first with AI isn’t enough anymore. DeepSeek just showed you can build GPT-4 level models on a shoestring budget. The moat isn’t having better AI; it’s using AI better. It’s about execution velocity. It’s about workflow integration so deep that switching costs become prohibitive. It’s about building network effects where your AI gets smarter with every customer interaction while your competitor is still running pilots.

McKinsey’s data is sobering: Only 48% of AI projects reach production. But companies that achieve deep integration see 2-6x higher returns than those treating AI as a bolt-on. The window to establish these advantages? We’re talking quarters, not years. By 2027, the leaders and laggards will be set. Which side will you be on?

New Value Creation: Business Models Enabled by Near-Zero Intelligence Costs

Here’s where things get really exciting. Intelligence costs have cratered from $30 to under $5 per million tokens in two years. DeepSeek represents a 17x cost reduction over GPT-4. When intelligence becomes essentially free, entirely new business models emerge.

Look at what’s already happening. AI Copilots like GitHub and Harvey AI charge $30-50 monthly per seat—double or triple traditional SaaS pricing—while delivering 50%+ productivity gains. Customers happily pay because the ROI is immediate and massive. AI Agents like Decagon price at $0.99 per customer service resolution, 90% cheaper than human agents. AI-Enabled Services like EvenUp automate legal document preparation at prices that destroy traditional paralegal economics while maintaining 50%+ margins.

Y Combinator’s latest batch is growing 10% weekly (their fastest cohort ever). A quarter of these startups have 95% of their code written by AI. They’re reaching $10 million revenue with teams under 10 people. This isn’t just capital efficiency; it’s a complete redefinition of what a company is. When intelligence is free, the constraints that shaped business for centuries simply evaporate.

McKinsey projects $2.6-4.4 trillion in annual value from generative AI. But I think they’re being conservative. When you can have a thousand AI employees for the cost of one human, when every customer interaction is perfectly personalized, when R&D cycles compress from years to weeks – the value creation potential is beyond what our current models can calculate.

Strategic Framework: AI Transformation Readiness Matrix

So how do you actually do this? How do you transform your organization for the intelligence abundance era? Let me break down what actually works, based on hundreds of transformations.

First, recognize where you are. Gartner’s research shows only 9% of companies reach true AI maturity. Most are stuck in what I call “pilot purgatory” – endless experiments with no production impact. The companies that break through follow a clear pattern.

McKinsey’s Executive AI Playbook identifies ten failure modes, but they all boil down to one thing: treating AI as a technology problem instead of a transformation challenge. The winners follow BCG’s Deploy-Reshape-Invent progression. Start with off-the-shelf tools for quick wins (10-15% gains). Then redesign workflows around AI capabilities (50-100% gains). Finally, invent entirely new business models (10x or more).

The frameworks converge on critical success factors: Start with high-impact, low-risk pilots in customer service or document processing. Get early wins to build momentum. Establish governance before you scale—ethics, security, quality control. Invest massively in workforce development, focusing on those uniquely human capabilities we discussed. Measure everything with concrete KPIs tied to business outcomes, not vanity metrics.

But here’s the real secret: velocity matters more than perfection. The companies winning aren’t the ones with the best AI strategy. They’re the ones moving fastest, learning fastest, adapting fastest. Every quarter you spend planning is a quarter your competitor spends implementing.

Investment Thesis: Betting on Intelligence Abundance

The capital markets have made their bet. OpenAI’s $300 billion valuation on $5 billion revenue (a 60x multiple!) only makes sense if you believe AGI changes everything. Anthropic went from $4.1 billion to $183 billion valuation in 18 months. These aren’t bubble valuations; they’re strategic positions for controlling the infrastructure of intelligence.

Venture capital invested $100.4 billion in AI companies in 2024, with AI capturing 46.4% of all US venture funding. Sequoia predicts “five finalists” will dominate foundation models while 20+ application companies reach billion-dollar scale. Sovereign wealth funds are all-in: Abu Dhabi’s $100 billion MGX fund is betting their entire post-oil economy on AI dominance.

The capital deployment extends beyond software. Hyperscalers will spend $405 billion in 2025 on AI infrastructure, double this year. Data centers will increase power consumption 160%. This isn’t speculation; it’s the engineering requirement for AGI-scale systems. The companies controlling this infrastructure will control the means of intelligence production.

For executives, the investment implications are clear: if you’re not allocating significant capital to AI transformation, you’re basically planning for irrelevance. This isn’t about having an AI strategy anymore. It’s about recognizing that AI IS the strategy.

Executive Playbook: Leading Through the Cognitive Revolution

Let me be brutally honest about what you need to do, starting tomorrow morning.

First, conduct an intelligence audit. Map every cognitive task in your organization. Which could be automated today? Which need human oversight? Which will always require human judgment? Most executives skip this step and pay for it later.

Second, pick your battles wisely. Start where AI impact is highest and risk is lowest. Customer service, document processing, data analysis… these are your beachheads. Get wins, build confidence, then expand. But move fast. We’re talking weeks, not quarters.

Third, rebuild your organization architecture. The hierarchical structures designed for human coordination become obsolete when AI agents can coordinate instantly. Flatten structures, accelerate decision-making, push intelligence to the edge. Think networks, not pyramids.

Fourth, invest in your people like never before. The 70% rule isn’t optional. Your competitive advantage won’t be your AI because everyone will have that. It’ll be how well your humans and AI work together. Train for creativity, judgment, leadership – the things AI can’t do.

Fifth, measure what matters. Not how many AI projects you have, but how much productivity you’ve gained. Not how much you’ve spent on AI, but how much ROI you’ve captured. Real metrics tied to real business outcomes.

Finally, and most importantly: act with urgency. The window for establishing competitive advantage is closing rapidly. By 2027, when AGI arrives, the leaders and laggards will be determined. The 10x productivity gains available today will be table stakes tomorrow. The business models you could pioneer now will be commoditized then.

The Time to Act is Now

We’re standing at the edge of the productivity singularity. The next 2-5 years will compress a century of productivity gains into a moment. Companies achieving 10-50x improvements today will define entire industries tomorrow. The frameworks exist. The technology works. The capital is available.

But here’s what separates the companies that will thrive from those that will struggle: the willingness to act on incomplete information, to transform while others debate, to rebuild fundamental assumptions about how business works. The executives who grasp this aren’t just adopting AI—they’re rebuilding their organizations from first principles for an intelligence-abundant world.

The question isn’t whether AGI will transform your industry. It will. The question isn’t whether productivity gains of 10-100x are possible. They are. The only question that matters is this: Will you be the disruptor or the disrupted?

In 2027, when AGI arrives and intelligence becomes as abundant as electricity, every company will scramble to transform. But by then, the race will already be over. The winners will be those who started today, who moved fast and broke things, who rebuilt their organizations around intelligence abundance while others were still debating whether it was real.

The future isn’t something that happens to you. It’s something you create. And with AGI on the horizon, you have the chance to create a future of unprecedented abundance, productivity, and possibility.

But only if you act. Only if you transform. Only if you start now.

Dejan Dan Keri