Good morning to all new and old readers! Here is your Saturday edition of Faster Than Normal, exploring the stories, ideas, and frameworks of the world’s most prolific people and companies—and how you can apply them to build businesses, wealth, and the most important asset of all: yourself. 

Today’s special edition is an excerpt from my essay The Last Invention, diving into the coming era of Artificial General Intelligence. We’ll explore ten high-leverage leadership plays, map realistic milestones over the next five years, and lay out risk-weighted strategies so you can start preparing now—before intelligence truly becomes abundant.

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Cheers,

Alex

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5 Years to AGI: The Playbook for Business Leaders

Many of the great fortunes in history have emerged at the intersection of technological change and financial foresight. The Medicis with banking innovations, the John D. Rockfeller with oil infrastructure, the early Microsoft and Apple investors who saw computing's future—each understood that wealth creation occurs when you position capital at civilization-scale inflection points.

AGI represents such an inflection, but with a disturbing asymmetry: while previous inflections unfolded over decades, AGI's wealth concentration potential could manifest in years, permanently dividing investors into those who prepared and those who hesitated.

What follows is a reimagining of investment philosophy for a world where algorithms may soon outthink market participants.

1. Raise the execution stakes

Recognise the new table stakes of execution and strategy. Beyond traditional sources of moat (which we've just discussed), the table stakes are shifting dramatically for businesses.

Whereas in the past:

• A superior product experience could be a durable source of advantage, that advantage will erode as AI enables rapid replication and improvement of most products

• Functional business skills (marketing, finance, etc.) were valuable, they will lose significance as as strategic oversight and human-centric aspects gain importance

Going forward:

• Relationships to power players, speed, unique distribution hypotheses, effective AI orchestration, and the hoarding of secrets will provide the advantages

2. Drive AI change

Most artificial intelligence initiatives fail not because of technical limitations but because they target superficial tool adoption rather than cultural change. Companies that just augment existing processes with AI tools without reimagining their core operating principles will find themselves outflanked by AI-native competitors.

The cardinal failures of AI adoption include:

• Over-indexing on tools while neglecting habit formation

• Ignoring the psychological resistance to change

• Failing to connect AI usage directly to individual incentives and team outcomes

Implement a comprehensive five-phase change strategy:

Phase 1: Strategic Framing Make AI adoption central to organizational identity by declaring a public "AI mission" tied directly to company vision and job security. Demonstrate leadership modeling with executives showcasing AI use cases weekly. Establish clear commitment deadlines for baseline fluency across the organization, and form a cross-functional futures team to monitor technological developments.

Phase 2: Incentives & Infrastructure Create visibility and rewards that reinforce AI-first behaviors through gamified dashboards tracking usage and time saved. Implement micro-bonuses ($250-1000) for high-impact use cases. Build an internal agent library where teams can share and modify AI workflows. Establish a skill progression ladder with certifications and recognition, and feature an "AI Hall of Fame" highlighting breakthrough applications.

Phase 3: Enablement & Onboarding Reduce adoption friction by developing role-specific AI playbooks, establishing buddy systems pairing early adopters with more reluctant team members, conducting focused one-week AI sprints on applied projects, and mapping workflows for potential AI enhancement.

Phase 4: Accountability & Enforcement Transform AI from optional to mandatory by incorporating usage metrics into performance reviews, requiring AI leverage cases for new headcount or resource requests, mandating AI components in all new proposals, and implementing a rigorous build-vs-buy evaluation framework.

Phase 5: Feedback & Learning Flywheel Build compounding returns through monthly AI retrospectives where teams share victories and failures, continuously evolve your agent library based on implementation learnings, and conduct regular surveys to identify adoption gaps and learning opportunities.

Your objective transcends mere tool adoption—it requires becoming an AI-native organization where intelligence augmentation permeates every aspect of operations and decision-making.

3. Launch an automation office

Automation functions will become one of the highest leverage points of future organizations. To neglect this is to significantly inhibit the future ability of the company to compete. Here is what I recommend for getting started:

Define a focused mission to identify high-leverage workflows for AI/RPA/LLM augmentation, with scope encompassing internal tooling, agent orchestration, API integration, and standard operating procedure automation.

Appoint a lean, cross-functional unit including:

• Head of Automation (product management/operations background)

• AI-savvy engineer with Python and major cloud platform expertise

• Business analyst skilled in workflow mapping and optimization

• Rotating subject matter experts from key departments

Create a streamlined intake system—either through forms or conversational interfaces—that captures task descriptions, time investments, systems involved, and desired outcomes. Establish a weekly sprint rhythm that prioritizes requests through impact-effort scoring, delivers 1-2 high-value automations weekly, and showcases successful implementations through company-wide demonstrations. Track impact rigorously through dashboards measuring hours saved, automation coverage percentage, and team-level adoption, linking these metrics directly to departmental objectives.

4. Scout the frontier

There is a famous saying that "you can't be what you can't see". Similarly, as it relates to implementing AI effectively, you cannot implement what you don't know about.

Dedicate resources to exploring cutting-edge developments through Google's 601 real-world generative AI use cases and NVIDIA's technical presentations. Regularly evaluate new agent platforms, no-code tools, and middleware that could accelerate implementation within your organization.

This cannot be delegated—it requires direct leadership engagement to gain maximum leverage from AI tools.

5. Decide build vs buy fast

The pace of AI development demands accelerated procurement. Establish systematic decision criteria based on:

• Deployment urgency (less than 30 days favors buying)

• Workflow uniqueness (standardized processes favor buying, proprietary workflows favor building)

• Internal capabilities (limited ML expertise favors buying, strong API orchestration skills favor building)

• Cost structures (subscription affordability versus development investment)

• Customization requirements (minimal control needs favor buying, deep integration requirements favor building)

• Compliance and data sensitivity (regulated industries with strict data controls generally favor building)

Implement process that begins with comprehensive tool inventory, conducts 48-hour proof-of-value testing comparing off-the-shelf and internal solutions, scores options on a 1-5 scale across key criteria, and shares implementation learnings through an internal knowledge registry.

6. Unlock unstructured data

Nearly 90% of enterprise information remains trapped in unstructured formats—emails, documents, messages, transcripts—inaccessible but potentially transformative when parsed by AI models. Transforming this data into something useable allows Companies to leverage LLM’s to the maximum degree. This will become table stakes in the near future.

Begin by mapping key data repositories across all communication channels and document stores. Implement centralized tools to compile and structure this information, segment it by relevant use cases, clean and annotate for improved utility, and integrate with retrieval-augmented generation pipelines that enable intelligent interaction with organizational knowledge.

7. Work the conference circuit

There's a huge information asymmetry between the technological frontier and the rest of the world. As a leader, your job is to cut this gap. You can do so by attending leading conferences.

8. Redesign org charts

AI is reshaping companies from the ground up. Hiring will quickly become the new technical debt:

Expect a two‑phase transition:

1. Near term (basic AI adoption). Leaner, tighter hierarchies and heavier managerial load at the top.

2. Later (advanced AI + abundant compute). Explosive firm growth, flatter networks, and large AI‑centred spans of control.

First, it automates junior roles—routine analysis, scheduling, and operational decisions now run on always-on AI teams. Managers oversee hybrid human-AI units, maintaining oversight while radically boosting productivity. The pyramid compresses into a tighter structure: fewer human roles, but more strategic ones.

Then AI augments leadership. Executives use AGI co-pilots for strategic planning, resource allocation, and cross-functional coordination—deciding faster while maintaining human judgment. Organizations evolve into lean, AI-powered teams, where humans focus on high-value decisions, relationships, and oversight.

The result? Smarter, flatter companies where humans and AI collaborate at every level—with people firmly in control.

9. Mandate AI usage

An increasing number of public company CEO's are publishing staff memos urging employees to take up AI usage. CEO of $100bn Shopify, Tobi Lutke, recently shared a memo outlining new internal company AI usage expectations.

As many business arenas become more competitive, I expect those that can effectively leverage the best of AI will get further and further ahead. It is no longer optional to use AI. Stagnation is almost certain, and stagnation is slow-motion failure. If you're not climbing, you're sliding.

10. Bet on durable moats

As AGI compresses software innovation cycles from years to months or even weeks, competitive advantage will shift decisively to assets that resist overnight replication—hard-to-replicate supply chains, capital-intensive infrastructure, proprietary data repositories, and deeply embedded workflow systems that competitors cannot easily reproduce.

Focus strategic investment on:

• Vertical integration in physical-world domains that remain difficult to virtualize

• Tangible infrastructure with high capital requirements that create implementation barriers

• Vertical software platforms that own the complete customer journey rather than point solutions

• Complex industry applications (healthcare, legal, finance) combined with unique distribution channels and proprietary data

• "Boring" but resilient sectors (waste management, HVAC, industrial maintenance) whose essential nature persists regardless of technological disruption

The leadership decisions you make in the coming months will likely determine your organization's relevance for decades to come.

This is not hyperbole but the logical consequence of AI's transformation of value creation. The window for positioning is open now but narrowing with each passing month.

For the full context and deeper insights into preparing for the AGI transition, read my complete essay, "The Last Invention."

That’s all for today, folks. As always, please give me your feedback. Which section is your favourite? What do you want to see more or less of? Other suggestions? Please let me know.

Have a wonderful rest of week, all.

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