Cognitive Load, Hiring a Strong Founding Team, and How to Launch Your Startup Out of Stealth
carry the boats.
Good morning
In today's edition, among other things:
The Founder’s Invisible Tax: Cognitive Load
How to Hire a Strong Founding Team
How to Launch Your Startup Out of Stealth
Developer Tooling for Software in the AI Era
The AI Productivity Paradox
Onwards!
The Founder’s Invisible Tax: Cognitive Load
Every founder wakes up with a finite resource that can’t be raised from investors or outsourced to staff: mental bandwidth.
Cognitive load is the silent tax on leadership, compounding faster than burn rate.
The human brain can hold roughly four “chunks” of information at once. That’s fine for debugging a function, but lethal when you’re simultaneously:
Closing a funding round
Hiring a Founding Engineer
Deciding whether to pivot product strategy
Juggling daily-life logistics
Unlike code, leadership problems don’t come with a compiler yelling at you. They just accumulate in the background until you snap. The overload moment isn’t a dramatic scene.
Often it’s missing the obvious because your working memory is full.
Cognitive load comes in two forms: intrinsic (the actual difficulty of the task) and extraneous (the way the information is presented).
Confusion is caused by high cognitive load. Costs time, money, and health.
Founders can’t reduce the intrinsic difficulty of building a company. But they can ruthlessly eliminate extraneous load:
Shallow hierarchies
Over-abstracted playbooks
Tool sprawl
It’s tempting to think startups stall because of market shifts or talent gaps. More often, the bottleneck is cognitive overload of people.
Code isn’t the bottleneck anymore; relevant skill, knowledge transfer, debugging, and coordination are.
Social media, designed to consume “as much of your time and conscious attention as possible”, fragments focus outside of work too.
AI tools reduce certain loads but can create “cognitive debt” if founders outsource thinking without practicing their own judgment.
The hidden cost: delayed decisions, shallow reasoning, and slow iteration at exactly the moment speed matters most.
Not all load can be offloaded.
The founder’s role is irreducible: setting vision, making trade-offs, deciding when to quit or double down, build. These tasks can’t be fully automated. Delegating complexity doesn’t mean escaping judgment.
What helps?
Auditing your and teams attention like you audit cash. Track one week of tasks—how much is true leadership vs “coordination tax”?
Redesigning workflows to reduce extraneous load. Kill tools, collapse dashboards, simplify investor updates. Measure progress not by headcount, but by how many decisions you can make clearly per week that runs to output.
Building cognitive moats—trusted lieutenants, rituals, and systems that filter noise before it hits your brain. If you don’t, your company will drown not in competition, but in your own attention debt.
The most dangerous founder failure mode isn’t running out of money—it’s running out of clarity. Your cognitive load sets the ceiling on your company’s speed. Lower it, or the business won’t scale past. The same goes for your team.
How to Hire a Strong Founding Team
The first ten people you hire will do more to determine your company’s future than your first ten customers.
Your founding team is your culture, speed, and credibility compressed into human form .
Think about two timelines:
Who did you need yesterday?
Who will you wish you had 6 months from now?
Balancing both avoids the trap of being reactive. But there’s the quality problem.
Recruiting is sales with higher stakes. That means:
Sourcing strategy: don’t stop at your first-degree network. Use warm intros, niche communities, university clubs, even GitHub commits .
Structured process: top talent is lost to slow, sloppy funnels. Move fast, challenge hard, and front-load rigor. By onsite, you should already be 80% sure .
References as onboarding: not just a red-flag check. Great references help you learn how to manage someone from day one.
Here’s a16z on sourcing:
Most early-stage founders start with their immediate network: former colleagues, classmates, and trusted peers. That’s a solid first step. But if you stop there, you’ll risk hiring too slowly or from too narrow a pool. Think about sourcing as a multi-prong strategy, not a single channel:
First-degree network: People you’d work with again in a heartbeat. Reach out directly. Keep the pitch simple and personal.
Second-degree network: Warm intros are more likely to lead to better first conversations than cold outreach. Write forwardable blurbs and be as specific as possible with asks from close friends and investors. The more you do this, the easier it is for them to help you and the more help you get.
Internal recruiter: High leverage when you’re juggling multiple roles and overwhelmed with coordination. This also allows you to build processes and recognize patterns that have compounding benefits long-term. High-slope, young candidates can often be a better fit for startups than those with long tenures at established companies, where hiring is easier. The best startup recruiters are at companies with strong engineers, but no brand recognition—they must be good.
External recruiters: External recruiters vary in style (sourcing-only, contingent, or retained) and are often expensive. Choose carefully and get references from founders who have worked with them before. Once you’re hiring 5+ people a year it often makes more sense to hire someone in-house. External recruiters can still be very helpful for niche roles.
Creative channels:
X (yes, still useful)
Alumni from strong-but-struggling companies
Niche Discords or online communities
University clubs you’re connected to (Formula SAE, rocket clubs, etc.)
GitHub contributors
Conference speakers and podcast guests
If you’re willing to dig, there are plenty of strong candidates out there.
Founding hires join because:
They believe in the founder’s inevitability.
They feel valued—both in equity and scope.
They see the long game: growth, networks, reputation .
Some are future founders in training. Others want more ownership than big tech allows. A few are scarred but wiser from past failures. All share one trait: they want their work to matter.
The most common reason early hires fail isn’t “culture fit” but misaligned expectations. An engineer hoping for a leadership path while outside execs get parachuted in. One wrong early hire can stall momentum and poison morale.
Recruiting isn’t something you do after figuring things out; recruiting is how you figure things out. It’s not a side task. It’s the company. Founders who treat it that way are the ones who build lasting companies.
The AI Productivity Paradox
Two interesting papers came out recently: MIT NANDA’s The GenAI Divide and Brynjolfsson et al.’s “Canaries in the Coal Mine?” Both worth reading, but I really enjoyed this summary by Sequoia:
For AI founders, these two papers offer a clear set of instructions for building enterprise AI application companies that can not only survive, but thrive in this new environment. The key is to move beyond the hype and focus on solving real-world business problems in a way that is both technologically innovative and organizationally intelligent. Here are some key takeaways:
Focus on Learning, Not Just Generating: The "learning gap" identified in the MIT report is the single biggest opportunity for AI startups. Founders who can build systems that learn from user feedback, adapt to changing workflows, and improve over time will have a massive competitive advantage. This means moving beyond simple "prompt-in, text-out" models and building true "agentic" systems that can act as persistent, collaborative partners for their users.
Build for the Workflow, Not Just the User: The failure of many enterprise AI tools can be traced to a lack of deep integration with existing workflows. Founders need to obsess over the details of how their customers actually work and design their products to fit seamlessly into those processes. This may mean sacrificing some of the "wow" factor of a flashy demo in favor of a more practical, but ultimately more valuable, solution.
Embrace the "Shadow AI" Economy: The fact that so many employees are using their own personal AI tools is not a threat, but an opportunity. It is a massive, real-time focus group that can provide invaluable insights into what users actually want and need. Founders should study this "shadow AI" economy closely and use what they learn to inform their product development.
Target the Back Office: While sales and marketing are often the most visible and well-funded departments, the MIT report suggests that the highest ROI for AI may actually be in the back office, in functions like finance, procurement, and operations. These are often the most process-driven and data-intensive parts of a business, and they are ripe for disruption.
Think Like a BPO, Not a SaaS Company: The most successful AI buyers, according to the MIT report, are treating their AI vendors not as software providers, but as business process outsourcing (BPO) partners. They are demanding deep customization, a focus on business outcomes, and a true partnership approach. Founders who can deliver on these expectations will be well-positioned to win in the enterprise market.
Developer Tooling for Software in the AI Era
You used to start with a blank file when starting to build. Now you start with a prompt, an agent, and a versioning protocol. The interface is changing. So is the work.
Software is being assembled, reasoned, and evaluated by systems that look more like autonomous organizations than text editors or IDE’s.
The “IDE” in 2025 is unrecognizable to a 2015 engineer. Yes, you still write code. But most of your job is now framing inputs, tuning flows, and inspecting outputs.
Copilot drove 40% of GitHub’s $2B in revenue in 2024. That’s not assistive tooling — that’s co-ownership of the stack.
Modal, Fireworks, and Replicate are removing DevOps entirely — the Heroku of inference is here.
LangChain, LlamaIndex, and DSPy are doing to prompt chaining what React did to front-end: abstracting plumbing so developers focus on logic.
Here’s Bessemer on Software 3.0:
This isn't just an incremental progression of developer tools. Software engineering is being radically reimagined from the ground up, with AI not just disrupting traditional workflows, but creating entirely new categories of developer platforms.
Here’s the market map:
Context is the new compute. If the 2010s were about testing in production, the 2020s are about reasoning in production — and it’s chaotic. Non-determinism means outputs vary run-to-run.
Is this prompt producing accurate, safe, and useful results?
Do hallucinations degrade silently, or does the system self-correct?
Does your LLM respond to edge cases with recoverable behavior?
The winning infra stacks are building for one job: make AI’s judgment legible and safe at scale.
The hype says agents will replace dev teams. Not yet, but they’ll replace the way dev teams operate. Most engineers are not ready mentally.
What works today?
Stateless tasks — one-shot SQL generators, refactorers, doc explainers. Fast, cheap, accurate.
Autonomous agents for QA, test case generation, and bug detection. Superhuman response time, 24/7.
Hybrid stacks — agent writes code, human reviews logic, system does the deploy.
The bottleneck is reliability.
Even if you magically achieve 99% per-step reliability (which no one has), you still only get 82% success over 20 steps.
Long-chain orchestration collapses under compounding error rates. That’s why production agents look more like highly structured workflows than free-roaming copilots. The interface is agentic and the output is deterministic.
And it’s getting more interesting:
New essential parts of the stack include:
Memory & Context Management: Tools like Mem0, Zep, Subconscious, and foundational model labs themselves are racing to address the limitations of stateless LLMs. Where developers once built custom vector databases and retrieval systems, "memory as a service" tools now provide plug-and-play memory layers that maintain conversation context, user preferences, and long-term learning—critical for any AI application that needs to feel intelligent beyond a single interaction.
AI-Native Frameworks: Just as React transformed UI development, frameworks like LangChain, LlamaIndex, DSPy, and Crew are abstracting the complexity of prompt chaining, tool use, and multi-step reasoning. Developers no longer need to hand-roll retry logic, token management, or agent orchestration—they can focus on business logic while these frameworks handle the plumbing.
Runtime & Deployment Infrastructure: Modal, fal, Replicate, and Fireworks are to AI what Vercel was to Next.js—removing the GPU procurement headache and cold-start problems that plague AI applications. They eliminate the DevOps bottleneck with a simple function call.
Agents handle complexity. Humans handle judgment. Software handles reliability.
In Software 3.0, natural language is the programming interface, and models execute on prompts, not functions.
That rewires everything about value capture:
Distribution: Devs don’t Google anymore — they prompt. Your brand equity depends on how often a model “remembers” you.
Enter: GEO (Generative Engine Optimization) instead of SEO.
Metric: Reference rate and attribution depth, not click-through.
Moats: Access to code isn’t scarce. Taste is. Workflows are. What’s scarce is trust, context, and decision-routing.
Margins: Code is cheap. Understanding, maintaining, and deploying it is not.
Debugging, testing, and integration become the new engineering costs.
Tooling that evaluates models — not just outputs — becomes a defensible wedge.
This shift mirrors the broader collapse of the software value stack. Software is still the leverage. But the profit will live elsewhere.
How to Launch Your Startup Out of Stealth
If you’re not embarrassed by the first version of your product, you’ve launched too late.
Famous quote from Reid Hoffman that is both well known but also not often followed (myself included). But it’s dead right.
The riskiest place for a startup isn’t on stage, but it’s in the shadows. Stay in stealth too long and your team loses urgency, your product drifts, and you’re just rehearsing in an empty theater. A launch forges your product, your positioning, and your purpose.
Recently, we had a successful launch of Shotgun - a lot of things were not ready, could be better, etc.
By picking a date—even arbitrarily—you move from theoretical planning to a concrete execution path. The calendar becomes your first growth hack.
A date beats perfection: without one, stealth becomes inertia.
Founders often wait for “feature completeness,” but that’s a mirage. Claire Butler recalls Figma launching without its multiplayer core, a decision that sounded reckless at the time but was crucial for momentum.
Before launch, Butler argues every founder must draft three deceptively simple deliverables:
Website: Even a single homepage sharpens positioning, brand feel, and the customer call-to-action. Is it a waitlist, a demo request, or instant sign-up? That decision shapes everything downstream.
Announcement Post: A founder-written narrative clarifies the “why now,” long-term vision, and corporate story arc. It’s as valuable for your Series A pitch as it is for launch day.
Social Post: The one-sentence gut check. If you cringe at posting it, your message isn’t ready yet.
Writing is a forcing function. The artifacts may never be published in their first form, but the act of drafting them hardens strategy into language.
Gone are the days when a single TechCrunch exclusive made your company. You need to go where your users already are. That might be Discord, Reddit, or LinkedIn—not a vanity blog no one reads. What works:
Personal accounts over brand handles (founders are more trusted than logos).
Email every user, beta tester, friend, and ex-roommate.
Pre-seed amplification: ask allies to repost and boost, so you never launch to crickets.
In early-stage GTM, your personal network is your first distribution channel.
A launch won’t save a broken product. It won’t manufacture PMF. And metrics are fuzzy at this stage—vibes and momentum often matter more than dashboards.
The real work begins the day after launch: engaging conversations, iterating fast, compounding learning loops.
The launch is less about the world discovering you and more about you discovering your product and team. The deadline sharpens decisions, the artifacts sharpen narrative, and the distribution strategy sharpens focus.
Interesting Analysis and Trends
AI, Agents & Industry Applications
AI’s Labor Impact and How to Not Lose LINK
AI Ads LINK
Voice AI Market Map LINK
Replatforming of CRM LINK
AI Pricing Innovation LINK
How Hotels Can Thrive in an Agentic Future LINK
The New Industrial Intelligence Stack LINK
The DNA of AI-Forward Organizations LINK
Open Source Business Models: Notes LINK
Startups, Growth & Product
Lessons Learned from Staring at Thousands of Startups LINK
Marketing Is… LINK
What We’ve Learned About Building LINK
How to Find the Perfect Name LINK
Clouded Judgement: The AI Innovators LINK
AI Economics & Business Models
Tech & Engineering
Macro, Policy & Commentary
Meditations
Konstantin Kisin:
You'll never meet a hater who is doing better than you.
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Thank you for your time,
Bartek