AI is Already Increasing Our Cognitive Load. That’s Only Going to Increase — Dramatically.

So I love our SaaStr AI.  It’s already enabled us to:

• Dramatically improve our content
• Review 200+ pitches for SaaStr Fund
• Review 700+ content submissions for SaaStr Annual
• Review 150+ sessions for SaaStr Annual

And so much more.  It’s great.

But because we can now do so much more with AI … we also have to process all that output.  It’s so, so much more to process than just 9 months ago.

While AI and emerging agent technology are creating unprecedented efficiency gains, they’re simultaneously introducing a new class of cognitive burden that few are discussing.

The 3X Efficiency Paradox

I’m easily 3X more productive than 12 months ago, leveraging various AI tools across my workflow. Content creation, data analysis, customer communications, pitch deck review—all truly transformed.

But there’s a shadow side to this productivity explosion.

Every AI-powered workflow creates additional decision points and outputs requiring human judgment. Each time Claude, GPT, or another system generates content or analysis for me, I need to:

1. Evaluate its accuracy
2. Adjust the framing
3. Integrate it with other workstreams
4. Apply business context the AI doesn’t possess

This work—what I call “AI output management”—isn’t captured in our efficiency metrics. Yet it’s growing exponentially.

The Hidden Burden of Integrating Multiple AIs and So Many Outputs

When I generate five different strategic approaches using an AI, I still need to synthesize them, discard the impractical ones, and integrate the survivors with organizational realities. The AI doesn’t do this for me—it multiplies the options I must process.

This isn’t just about filtering content. It’s about:

• Maintaining strategic coherence across AI-generated work
• Ensuring consistent voice and positioning
• Validating factual accuracy without becoming a full-time fact-checker
• Translating between AI outputs and human teammates

The Coming Agent Revolution: Cognitive Load Multiplied

What we’re experiencing now is just the beginning. True AI agents—systems that can independently take actions toward goals—will dramatically amplify both productivity and management overhead.

Consider a CRO 12 months from now:

She’ll likely be managing say 20 human team members and … say 5 AI agents. Those 5 agents might be handling work equivalent to another 20 humans—prospecting, meeting scheduling, pipeline analysis, competitive intelligence, and deal coaching.

But those agents will require direction, monitoring, and integration that looks nothing like managing humans. They’ll produce rivers of output, request clarification at machine speeds, and require guardrails that must be continuously adjusted as they learn.

The New Executive Skill: AI + Human Management.  And That’s a Lot >More< to Manage.

The winners in this new paradigm won’t just be those who adopt AI fastest—they’ll be those who master the meta-layer of orchestration.

I don’t love this term “orchestration”, it’s a bit too nerdy and technical.  But it’s a skill we are all going to have to learn, very very soon.

Successful executives will need to:

• Develop robust filtering systems for AI-generated outputs
• Master delegation patterns specific to AI agents’ capabilities
• Create synthesis workflows that efficiently combine human and machine outputs
• Design organizational structures where humans and AIs complement rather than overwhelm each other

This isn’t theoretical. I’m already seeing the early versions of this challenge in my own work and among founders I advise.

There’s No Going Back

Despite these challenges, there’s no reversing course. The productivity gains are too substantial, the competitive advantage too significant.

The question isn’t whether to embrace these tools—it’s how to evolve our own cognitive approaches to thrive alongside them.

Companies that develop organizational designs and executive practices optimized for AI orchestration will outperform those treating AI as merely “faster humans.”

Where Do We Go From Here?

As I look ahead, I see several critical adaptations required:

1. Leadership teams need explicit discussion about AI output management as a distinct workstream
2. We need new metrics for cognitive load, not just output volume
3. Executive coaching needs to incorporate AI orchestration skills
4. Team structures need reimagining around human-AI collaboration patterns

The next generation of elite operators won’t just be technical—they’ll be masters of their own attention and cognitive capacity in an environment of overwhelming AI-produced possibilities.

Companies that recognize and address this hidden overhead now will have a substantial advantage when true agentic systems arrive in the marketplace.

The most valuable skill of 2026 might not be programming AI—it might be effectively managing your attention while directing a team of both humans and machines.