AI Makes Everything Cheaper—So What Gets More Valuable?

By 2026, the core capabilities of generative ai—drafting text, generating images, writing code—are rapidly commoditizing. What was scarce and expensive just three years ago is now accessible through sub-cent API calls. When intelligence becomes abundant, and AI commoditizes previously scarce capabilities, abundance emerges in areas like programming, analytics, and content creation, fundamentally transforming value modeling and pricing strategies.

API costs have dropped and access is widespread, making advanced AI tools available to nearly everyone. AI generates outputs at near-zero marginal cost, systematically removing scarcity from information and cognitive labor.

As a result, the competition frontier moves to what remains hard to copy. As AI converts some scarce resources into abundant ones, it simultaneously makes other resources—like trust, proprietary data, and human judgment—critically scarce.

• Routine programming, analytical insights, and content creation are now plentiful, lowering their economic value.

Just as electricity and cloud computing shifted the basis of competition, AI shifts from automating existing processes to generating new content, software, and predictions at near-zero marginal cost.

Key Takeaways

• By early 2026, ai tools for drafting, summarizing, and generating content are effectively table stakes. As AI commoditizes core capabilities, abundance in intelligence dissolves traditional constraints, shifting the strategic focus to new scarcities like judgment and context. Most companies can access similar capabilities through public APIs, making raw ai models non-defensible on their own.
• When artificial intelligence becomes cheap and ubiquitous, the rare assets shift to judgment, context, proprietary feedback loops, and the ability to change workflows fast. These become the new strategic battlegrounds.
• Operators, founders, and leaders must stop competing on raw AI features and instead build moats around measurable outcomes, ecosystem position, and human-in-the-loop systems that compound over time.
• This article provides a concrete playbook: how to map where AI will erode your margins, identify where you can build durable advantage, and prepare your organization for the scarcity shift through 2030.
• The perspective here is practical—drawn from practitioners building products and organizations in the current AI wave, not speculative futurism about distant possibilities.

The Age of Abundant Intelligence: Why Scarcity Is Moving

In the early 2010s, AI was genuinely scarce. Building anything with machine learning meant assembling bespoke teams of PhDs, often poached from a handful of research labs. Google, Meta, and a few other FAANG companies could afford the talent and compute required for image recognition or natural language processing. Everyone else watched from the sidelines.

That world no longer exists. The shift happened remarkably fast. OpenAI released GPT-4 in March 2023, scaling to 1.76 trillion parameters. Anthropic’s Claude 3 family arrived in March 2024, matching or exceeding it on major benchmarks. Google’s Gemini 1.5 Pro could handle million-token contexts. Meta’s Llama 3, released in April 2024 with 405 billion parameters, let anyone deploy capable ai systems for free.

The adoption statistics tell the story: by mid-2025, ChatGPT reached 400 million weekly active users. Copilot found its way into 70% of Fortune 500 companies for code and productivity. API costs collapsed—GPT-4o input tokens dropped from $30 per million in 2023 to $2.50 by late 2024.

By early 2026, capabilities that once required specialized teams are accessible to anyone with a credit card:

• Drafting emails at 95% human-parity
• Summarizing 100-page reports in seconds
• Generating marketing copy
• Basic customer support resolving 60-70% of tier-1 queries
• Starter-level code with 80% acceptance rates in GitHub Copilot studies

This mirrors earlier technological shifts. Electricity was scarce before 1900, requiring expensive on-site generators. Grid standardization by the 1920s commoditized power, and scarcity moved to appliances and distribution networks. Cloud computing followed the same pattern—AWS commoditized servers after 2006, and global cloud spend hit $600 billion by 2025. The development of the computer revolutionized work processes and organizational efficiency, paving the way for digital transformation. Now, artificial intelligence (AI) is becoming a general-purpose technology integrated into every aspect of life, much like electricity. Value migrated to the SaaS layer, where companies like Salesforce built $35 billion ARR businesses.

The core claim is straightforward: once intelligence at the task level gets cheap and universal, the competition frontier moves to what remains hard to copy. Distribution, data, relationships, workflow depth, and governance become the new scarce resources.

Some constraints still exist. Nvidia’s H100 GPUs faced 2-3 year backlogs through 2025. Hyperscalers poured over $200 billion into data centers—Microsoft alone committed $80 billion in 2025. The EU AI Act and US Executive Order 14110 impose governance requirements on high-risk ai systems. But these are speed bumps, not roadblocks. The underlying trajectory is clear: artificial intelligence is becoming infrastructure, not advantage.

From Features to Systems: Where Value Collects When AI Is Everywhere

In an AI-saturated market, individual features are table stakes. A chatbot, a summarizer, an auto-code tool—these are no longer differentiators. As AI commoditizes features and capabilities, organizations must shift focus to the systems that orchestrate these capabilities into reliable outcomes.

The ai industry is experiencing a fundamental reorientation. Buyers care less about which model you use and more about what business problem you solve. Here are the specific shifts in scarcity playing out across the value chain:

From outputs to outcomes. “We use GPT-4” means nothing to a procurement team. “We cut your claims processing time from 14 days to 4” wins contracts. McKinsey’s 2025 survey found that 65% of executives valued AI for 20-40% workflow speedups, not model specifications. A 2025 Gartner report noted that 85% of AI projects fail to deliver ROI because they ship features instead of outcomes.

From one-off generation to continuous feedback. Any competitor can generate a first draft. What’s rare is a system that learns from every interaction. Intercom’s Fin AI agent, launched in 2025, resolves 50% more tickets than competitors because it learns from over 1 million human corrections annually. Systems with feedback loops improve 25-50% faster than static deployments.

From models to data and context. Hugging Face reports a 70% performance gap between public models and those fine-tuned on proprietary domain data. PathAI’s 100 million labeled pathology slides yield 15% diagnostic accuracy gains over general models. The training data you own—not the model you license—becomes your moat.

From apps to workflows. Deep embedding into CRM, ERP, EMR, and collaboration tools creates 5-10x stickiness. Salesforce Einstein, integrated directly into the CRM in 2024, makes switching costs prohibitive. Zendesk’s 2026 Answer Bot connects with 500+ applications and resolves 65% of cases autonomously. When you own the workflow, you own the customer.

From capability to trust. In high-stakes domains—law, medicine, finance—auditability matters more than raw capability. Many AI models operate as a black box, making it difficult to understand or explain their decisions, which is a challenge for trust, regulatory compliance, and transparency. Harvey.ai tracks 95% citation accuracy through court outcome feedback. Finance tools like Upstart reduced hallucination risks by 80% through governance layers. The ability to demonstrate reliable, accountable AI is genuinely scarce.

Consider legal drafting. Generative tools can produce first drafts in seconds—that capability is now commoditized. What’s rare is Casetext’s CoCounsel system (acquired by Thomson Reuters in 2023), which improves through partner reviews, tracks citation accuracy, and integrates with case management workflows. The model is abundant. The system is scarce.

In customer support, cheap chatbots achieve 70% resolution rates. Integrated systems connecting Zendesk, Salesforce, and internal knowledge bases push that to 90% while logging every interaction for continuous improvement. The chatbot is a commodity. The system is not.

Mapping Your Exposure: How to See Where AI Will Erode Your Moat

Before you can build new advantages, you need to see where your current ones are eroding. Here’s a practical frame for mapping your exposure across the value chain.

Break your product or organization into five layers:

Now classify each part of your business into three categories:

Collapsing: Generic AI can easily substitute. Think first drafts of emails, research summaries, generic documentation. If a public LLM can do it today, your margin here is disappearing.

Stable: AI helps but doesn’t eliminate differentiation. Domain-specific UI, compliance-tailored workflows, and specialized user experiences fall here. The technology assists, but your expertise and design still matter.

Compounding: AI makes you better the more you use it. Proprietary feedback loops, custom models trained on your data, and accumulated context create assets that grow more valuable over time.

Mini-Case: B2B SaaS Analytics Product in 2026

Consider a B2B SaaS analytics company mapping its exposure:

• Data layer (Compounding): Proprietary query logs from thousands of customers fine-tune their models 20% better than competitors using public data alone.
• Generation layer (Collapsing): Dashboard summaries and automated insights—now available through public APIs. No defensible advantage here.
• Workflow layer (Stable): Custom BI pipelines for financial services clients with compliance requirements. AI helps but doesn’t replace domain expertise.
• Decision layer (Compounding): Anomaly detection that learns from trader feedback. Every correction makes the system smarter.
• Integration layer (Compounding): Deep plugins with Tableau and Snowflake. Ecosystem position creates switching costs.

This mapping reveals 40% margin erosion in the generation layer but 2x uplift potential in compounding layers. According to Deloitte’s 2025 AI maturity report, companies that redirect investment from collapsing to compounding layers see substantially higher returns on their business investment in AI.

The point isn’t to predict exactly where value will flow. It’s to build the habit of asking: which parts of my business are becoming abundant, and which remain genuinely scarce?

What Actually Becomes Scarce: The New Moats in an AI-Saturated World

Once you understand where value is collapsing, the strategic question becomes: what’s still scarce, and how do I position there?

Here are the new constraints that matter:

Proprietary, high-fidelity data. Labeled, cleaned, domain-rich data points that public models can’t access. Waymo’s 20 billion autonomous miles make their systems 40% safer than industry averages. PathAI’s labeled pathology slides, UiPath’s 10 billion task logs—these assets compound because they’re tied to real outcomes, not synthetic training.

Distribution and embedding. Owning the channel or being deeply integrated into daily tools. Copilot is the default AI for 1 billion Office users. That distribution advantage is nearly impossible to replicate. Being embedded in Microsoft 365 or Salesforce creates an installed base that pure capability cannot overcome.

Workflow ownership. Controlling critical workflows where switching costs are high. Workday owns HR close processes with 90% adoption stickiness. When you’re embedded in monthly close, claims adjudication, or product roadmap decisions, customers don’t leave because of a slightly better model elsewhere.

Judgment and governance. Structured human oversight, ethical review, and risk management capacity. JPMorgan Chase’s 2025 AI risk committee rejects 30% of model deployments. As regulators and boards demand accountability, the ability to govern AI at scale becomes a genuine competitive advantage.

Reputation and trust. Brands with consistently low hallucination rates, strong security, and clear accountability. Anthropic’s Constitutional AI approach achieves 25% lower hallucination rates. In high-stakes domains, customers pay premiums for AI they can trust.

Adaptability and speed of change. Organizations that redesign processes, train staff, and ship new workflows monthly rather than yearly. The ability to work faster with AI—and change how you work—becomes a meta-capability that compounds.

Ecosystem position. Being the hub around which complementary tools, consultants, and integrators cluster. LangChain’s 100,000+ plugin network creates a gravity that makes it the default choice for AI orchestration.

What’s Weakening

Some traditional moats are eroding:

• UI polish alone: Design can be replicated quickly with Figma-AI plugins and modern tooling
• Generic predictive models: Open-source alternatives match most proprietary models
• One-time consulting playbooks: McKinsey’s Lilli and similar tools democratize strategy frameworks

The path forward is focus. Experts like Andrew Ng advise choosing 1-2 scarcity categories you can realistically own over the next five years—not trying to cover all seven. A startup with limited resources should target narrow domains where it can accumulate data 5x faster than larger competitors, not try to out-distribute Microsoft.

The Operator Playbook: Shifting From Collapsing to Compounding Layers

Many teams in 2024-2026 added AI features but left their core business model and workflows untouched. They doubled down on what’s commoditizing while competitors built compounding assets. Here’s how to avoid that trap.

Step 1: Diagnose Your Collapsing Layers

Run internal workshops to inventory where you rely on tasks that a public LLM can now do. Map every process where generative ai performs at human-parity. Quantify revenue and margin exposure—BCG’s 2025 research suggests 30-50% of tasks in knowledge work are automatable.

The goal isn’t to panic. It’s to see clearly where your current moat is eroding so you can redirect resources.

Step 2: Choose Your Target Scarce Layers

Decide if your best path is proprietary data, workflow ownership, ecosystem position, or governance. This depends on your market and existing assets. A healthcare company might lean into proprietary clinical data. A services firm might focus on workflow ownership in specific verticals.

Don’t try to do everything. Pick 1-2 scarcity categories you can realistically dominate.

Step 3: Redesign Workflows, Not Just Screens

Map current processes end-to-end. Insert AI where it removes bottlenecks. Decide explicitly where humans must stay in the loop for critical thinking and judgment. Forrester’s research shows that end-to-end workflow redesign cuts bottlenecks by 35% compared to simply adding AI features to existing processes.

The mistake most companies make is bolting AI onto existing workflows. The opportunity is redesigning workflows around AI’s capabilities while preserving human oversight where it matters.

Step 4: Build Feedback Loops Into Everything

Ensure every AI-assisted interaction—support tickets, code reviews, sales calls—feeds corrections and outcomes back into your systems. This is how you create compounding assets. Systems that learn from every interaction improve 25-50% faster than static deployments.

Your data advantage isn’t what you start with. It’s what you accumulate through thousands of interactions, each one making your system slightly smarter.

Step 5: Align Pricing With Outcomes

Move away from AI add-on fees toward pricing tied to measurable value: time saved, risk reduced, revenue uplift. Zendesk’s per-resolved-case model delivers 2x margins compared to seat-based pricing. ServiceNow’s shift to outcome-based pricing drove 40% ARR growth in 2026.

When you price outcomes, you align incentives with customers and differentiate from competitors who still sell AI by the seat.

Step 6: Train for AI Literacy and Judgment

Invest in upskilling across roles so staff know when to trust, adjust, or override AI outputs. McKinsey’s research shows AI literacy training lifts productivity by 25%. This isn’t about teaching everyone to code—it’s about developing organizational judgment around AI capabilities and limitations.

Example: Marketing Agency Transformation

A mid-sized agency stopped selling AI content packages (collapsing) and moved to pipeline influence based on multi-touch attribution (compounding). They integrated with HubSpot, tracked which content actually influenced closed deals, and priced on pipeline contribution. Result: 28% lift in measurable pipeline impact and pricing power competitors couldn’t match.

Example: Software Vendor Shift

A support software vendor moved from AI seat licenses to per-resolved-case pricing. They invested heavily in feedback loops that learned from every resolution. Their system improved faster than competitors, resolution rates climbed, and customers paid more because the value was clear. 2026 ARR increased 40%.

Human Judgment, Trust, and Governance: The Ultimate Bottlenecks

As generative capabilities spread, new constraints emerge that have nothing to do with model capability. The bottlenecks shift to questions like: can we trust this at scale in law, medicine, finance, or public services? Can we govern it responsibly?

Risk and Responsibility

In 2026, regulators and courts still hold organizations—not models—liable. The EU AI Act imposes fines up to 7% of revenue for non-compliance. Risk frameworks, legal review capacity, and compliance infrastructure are scarce resources that most organizations haven’t built. Human labor in governance roles is becoming more valuable, not less.

Calibration of Trust

Teams must learn where AI is reliable versus where it should only assist. Drafting and summarizing? 95% reliable. Medical diagnosis or credit decisions? Below 70% in many cases. Organizations need structured processes for deciding when to trust AI outputs and when human judgment must override.

This calibration is itself a skill. It requires understanding both AI capabilities and domain-specific risks—a combination that takes time to develop.

Cultural Acceptance

Unions, professional bodies, and employees increasingly insist on involvement in AI deployment decisions. Google faced union demands for AI veto rights in 2025. The labor market is negotiating new terms for how AI enters the workplace. Organizations that ignore cultural acceptance constraints face resistance that slows adoption.

Ethical Constraints

Fairness, bias mitigation, and explainability are not solved by default. Amazon scrapped its AI hiring tool in 2018 after discovering gender bias—similar audits are now mandatory in many jurisdictions. The political system is catching up, imposing requirements that purely technical competitors struggle to meet.

Measurement and Transparency

Emerging dashboards track 20+ KPIs for AI systems: hallucination rates, bias metrics, labor impact, performance benchmarks. These governance tools create accountability but require investment to implement. Organizations with mature measurement capabilities can demonstrate trustworthiness that others cannot.

Governance Practices to Institutionalize

• Regular red-teaming of AI systems by internal and external reviewers
• Incident reporting processes for AI failures with clear escalation
• Model cards documenting capabilities, limitations, and appropriate use
• Human-in-the-loop checkpoints for high-stakes decisions
• Regular bias audits with documented remediation

AI oversight roles grew 300% between 2023 and 2025 according to LinkedIn data. Model risk committees are now standard at major banks. JPMorgan Chase’s AI risk committee reviews every deployment, rejecting 30% of proposed use cases.

Organizations that treat governance, ethics, and trust-building as strategic capabilities—not compliance overhead—will own the scarce layer of credible AI in their markets. This sense of responsibility becomes a genuine moat in the world of abundant capability.

Preparing People and Organizations for the Scarcity Shift

The shift in scarcity changes talent requirements and organizational design. AI skills now filter 80% of hires according to Indeed’s 2025 data. In fact, 66% of leaders state they would not hire someone without AI skills, and leaders are increasingly prioritizing AI skills in hiring as AI becomes more integrated into work processes. The use of generative AI has nearly doubled in the last six months, with 75% of global knowledge workers now using AI at work; 90% of them report that it helps them save time and focus on their most important tasks.

AI is also helping employees manage overwhelming workloads, with 92% of power users stating it makes their workload more manageable and boosts their creativity. However, this transformation is not without challenges: a study found that workers aged 22 to 25 have experienced about a 13 percent decline in employment since late 2022, highlighting the immediate impact of AI on younger workers. Additionally, 71% of respondents to a poll expressed concern that artificial intelligence will permanently displace too many jobs, and industry leaders predict

AI could drive unemployment up 10 to 20 percent in the next one to five years, potentially wiping out half of all entry-level white-collar jobs. The Bureau of Labor Statistics projects only 3.1% employment growth over the next decade, a significant decrease from the 13% growth in the previous decade, indicating a slowdown in job creation amid AI advancements. As a result, skills such as warmth, empathy, and the ability to read a room are becoming more sought after than cognitive skills in the workplace. But the skills that matter are evolving beyond technical proficiency.

Organizations that use AI strategically are seeing greater productivity and creativity gains, underscoring the importance of not just adopting AI, but integrating it thoughtfully into business strategies and workflows.

High-Value Skills in an AI-Saturated World

Problem framing and decomposition: Turning ambiguous goals into structured tasks AI can help with. The ability to break down a complex job into components—some AI handles, some humans own—is increasingly valuable.

Cross-functional integration: Connecting engineering, operations, compliance, and customer-facing teams around AI-enabled workflows. AI doesn’t stay in one department. Neither should your people.

Data stewardship: Owning data quality, labeling, governance, and lineage. The quality of your data determines the quality of your AI. People who understand this create compounding value.

Judgment under uncertainty: Making calls when AI confidence is high but stakes are higher. Knowing when to override the model—and having the authority to do so—prevents costly mistakes.

Change management: Guiding teams through workflow redesign, retraining, and role evolution. The human element of AI transformation is often harder than the technical element.

Training Shifts

Move from tool-specific training (“how to use Tool X”) to capability-specific training (“how to design prompts, build workflows, and review AI outputs effectively”). The specific tools will change. The underlying capabilities endure.

Organizational Models

Centers of excellence centralize AI expertise and develop best practices that flow across business units. Google DeepMind pioneered this model for research; it works for applied AI as well.

AI champions embedded in business units translate strategy into practice. Salesforce uses this model, placing AI-literate people throughout the organization to accelerate adoption.

Incentive structures that reward experimentation create cultures of learning. Some organizations offer experiment bounties—recognition or money for AI initiatives that generate learning, whether or not they succeed.

Two Vignettes

Notion invested heavily in AI literacy across their organization from 2024-2026. They didn’t just buy licenses—they trained every employee on effective prompt design and workflow integration. Revenue grew 5x as the organization used ai coherently across functions.

Contrast this with companies that bought ai tools but didn’t invest in organizational change. They achieved licensing compliance but not capability development. Their productivity remained flat while competitors pulled ahead.

The difference isn’t technology. It’s organizational commitment to building the judgment and skills that make technology effective.

Looking Ahead: Strategy in a World of Moving Scarcity

As AI keeps improving and spreading through 2026 and beyond, specific capabilities will continue to commoditize. But scarcity keeps reappearing at higher levels of abstraction: systems, relationships, institutions, trust.

This is not a one-time disruption. It’s a permanent feature of the technological economy. Linear extrapolation of current trends misses the dynamic: as one layer becomes abundant, new bottlenecks emerge elsewhere.

Enduring Principles

Ask the three-year question. For every capability you’re investing in, ask: if this is free in three years, what will still matter? That’s where you should be building.

Invest disproportionately in compounding layers. Data, feedback loops, workflow depth, and trust grow more valuable over time. Features don’t.

Price and measure what customers actually care about. Time, risk, money. Not your internal view of AI innovation. Your research into customer needs should drive metrics, not your excitement about technology.

Treat human judgment, ethics, and governance as design problems. Not afterthoughts. Not compliance checkboxes. These are core capabilities that differentiate you as AI makes the rest abundant.

Building the Habit

Run an annual scarcity review of your product and organization. Ask:

• Where is AI now eroding value that we’re still investing in?
• What new bottlenecks are forming that we could own?
• Which compounding assets have we built over the past year?
• What should we stop doing because it’s becoming abundant?

This review should inform resource allocation, hiring priorities, and product strategy. Make it a regular discipline, not a one-time exercise.

The future belongs to organizations that don’t chase the flashiest AI demos but systematically redirect effort from collapsing layers into scarce layers. When everyone can generate, the winners are those who build systems that learn, integrate, and earn trust.

The ai disruption is real. But the path through it isn’t about technology alone. It’s about understanding where scarcity is shifting—and positioning your organization to exist at the new frontier of value.

To stay competitive through 2030 and beyond, start your scarcity mapping today. Identify one collapsing layer you’ll de-invest from and one compounding layer you’ll build. That clarity—not another AI feature—is what matters most.

How do I know if my current AI features are already commoditized?

If multiple vendors can ship similar capabilities using public APIs in weeks, you’re on a collapsing layer. A quick test: if a competitor could match your flagship AI feature in under 90 days with off-the-shelf ai models and public data, treat that feature as non-defensible. Gartner reports 70% AI parity across most generation tasks by 2026.

Focus your defensibility efforts on elements that would take years—not months—to replicate: proprietary datasets, deeply embedded workflows, regulatory approvals, or ecosystem position. The feature might get you in the door, but it won’t keep competitors out.

Where should a small startup focus when it can’t compete on data volume with big tech?

Small firms can build moats through narrow, high-value domains with dense context. Think specific industries (construction permitting, specialty insurance), specific workflows (medical coding, regulatory filings), or specific geographies where incumbents haven’t invested.

Specialize in a painful workflow and build deep integration, UX, and trust there. Collect high-quality labeled data over time. Rippling built an $11 billion valuation by focusing intensely on HR workflows rather than competing broadly. South Korea and other countries offer opportunities where global tech giants have less presence.

Agility itself is scarce. Your ability to adopt new models quickly and iterate on workflows faster than large incumbents is a genuine advantage. Use it before they catch up.

What if my business is mostly services and not software—does the scarcity shift still apply?

Absolutely. Generic tasks in consulting, marketing, legal, and other professional services will be commoditized by AI-assisted practitioners. Research, drafting, basic analysis—these become abundant. Mass unemployment in these tasks isn’t guaranteed, but margin compression is.

Move up to outcome-based engagements, proprietary frameworks, and ongoing advisory relationships. Stop billing hours for what AI can do. Bill for judgment, relationships, and measurable results.

Build internal tools, playbooks, and data assets from client work that compound over time. That knowledge moat—accumulated context from hundreds of engagements—is something generic AI cannot easily match. The efficiency gains go to those who reinvest them in harder problems.

How should I think about regulation and compliance as a source of scarcity?

In regulated sectors—finance, healthcare, public sector, employment—the ability to meet emerging AI rules and provide auditable, explainable ai systems is itself a scarce capability. While others scramble to retrofit compliance, early investors in governance infrastructure have a head start.

Invest early in model documentation, audit trails, bias testing, and human-in-the-loop checkpoints. These are hard for latecomers to add after the fact. Collaborate proactively with regulators, industry bodies, and unions. That trust creates informal advantages—like being consulted on emerging rules—that purely technical competitors cannot copy.

Wall Street firms and major healthcare providers are already building these capabilities. If you’re not, you’re creating risk for your future market position.

Will there be another scarcity reset if AGI or much stronger models arrive later in the decade?

More capable models would likely commoditize additional tasks and domains, triggering another shift in where value concentrates. Live performances didn’t disappear when recorded music became abundant—they became more valuable. Similar dynamics will likely apply to highly technical human work.

But the discipline outlined here—mapping collapsing layers, identifying new bottlenecks, and investing in systems, data, governance, and relationships—remains valid under stronger AI. The specific scarcities will shift. The strategic habit of asking “what’s becoming abundant and what remains scarce” stays relevant.

Treat this as an ongoing capability, not a one-time response to the 2023-2026 generative AI wave. The profound questions about where humans add value will keep evolving. So should your strategy. The co founder who builds this thinking into their company’s DNA creates an organization that adapts—no matter what capabilities ai makes abundant next.

Your Friend,

Wade