What I look for in robotics founders

I have met with dozens if not hundreds of robotics startups who want to build for industrial operations.

This is what I look for in the founding teams:

• Factory native founders who understand real operations.
  People who have lived uptime, OEE, and production pressure, not just built robots in labs.
  
  
  
• Founders who are deployment-obsessed, focused on install time, integration with PLC/MES systems, safety, and reliability.
  Not demos or model benchmarks.
  
  
  
• Founders think in workflows, not robots.
  They define the manual process, redesign it for automation, and treat the robot as just one component in a broader system.
  
  
  
• Founders who can clearly articulate ROI.
  Cost per hour, payback period, uptime assumptions, and why this beats human labor or existing automation.
  
  
  
• Founders with a strong bias for simplicity.
  Deliberately reducing complexity, constraining environments, and standardizing tasks to make systems reliable and scalable.
  
  
  
• Hybrid teams combining software/data capability, industrial experience, and hands-on deployment experience.
  Not just pure roboticists.
  
  
  
• Founders who think in fleets and learning loops, understanding that value compounds through data, iteration, and scaling across many deployments.
  
  

👉 Bottom line: 

• Founders building industrial-grade systems that deploy, work, and scale.
  Not impressive robots.

Factory to robotics startup: Forget the humanoid; build the System.

A recent article in the Wall Street Journal about Agility humanoids at Schaeffler ("When Humanoid Robots Come to a Small-Town Factory") and a Techcrunch interview with Rivian's R J Scaringe ("... we're doing robots all wrong") provided interesting insights into the state of the so called humanoids in factory environments today.   

Industrial environments are the ideal testing grounds for robotics because they are predictable and well-bounded. Early deployments are already happening at entry points such as material handling and logistics where dexterous manipulation is not required.

For these use cases, the economics of robotic labor are closing in on human labor costs.

What works today is simple, constrained, and supervised. What is not working is trying to solve everything at once and over-engineered, complex systems aren't scaling. The bottleneck isn't the AI, it's everything around it. Safety in human-shared environments, integration efforts, and day-to-day reliability are gating adoption. 

The hardest problem, dexterous manipulation, remains largely unsolved, and the most capable-looking robots are often the least deployable ones.

The solution is a better system and not a better robot. The winners own the workflows, the deployment infrastructure, and the manipulation layer that ties hardware and software to real operations. Semi-humanoid and non-humanoid form factors that fit cleanly into existing industrial environments will outperform impressive general purpose machines that can't clear the integration bar. As uptime improves and costs continue to decline, what looks today like augmentation will segue into substitution.

The transition will be won by industrial-grade systems built for operations, not by the most compelling demo. 

The Industrialization of Low Earth Orbit: Beyond Collision Avoidance

When SpaceX unveiled its Stargaze Space Situational Awareness (SSA) system last month, the positioning was largely as a collision avoidance feature. However, the architectural implications point to a more profound shift from passive tracking to active Space Traffic Management (STM).

Credit: SpaceX

As mega-constellations scale toward tens of thousands of assets, the "manual" era of satellite operations has reached its functional limit. The industry is now moving toward automated governance, characterized by two critical requirements:

👉 Autonomy at Scale: With hundreds of thousands of collision avoidance maneuvers already occurring annually, operators can no longer rely on human-in-the-loop systems. The future belongs to automated conjunction analysis and predictive orbit modeling.

👉 Data Aggregation over silos: Reliable threat identification and maneuver detection require a unified "single source of truth." Orbital intelligence can no longer exist in proprietary silos if we are to mitigate systemic risk.

Stargaze is the first evidence of this new reality at scale: a move toward a sophisticated, automated architecture for the orbital commons.

How a board should govern innovation it cannot predict (6/6)

Supervisory boards are designed to control risk, ensure accountability, and protect long term value. That logic works well for established businesses whose performance can be forecast and audited, the typical public company or private equity owned shop. 

Transformation initiatives are different. Their outcomes cannot be predicted, only managed probabilistically. 

When both activities are governed identically, boards unintentionally force management to either hide uncertainty or avoid it. Core governance expects reliable plans, budget adherence, and variance explanations. Venture activity produces evolving plans, changing strategy, and learning instead of predictability. So management adapts and presents exploration as execution which leads to late surprises, sudden write-offs, loss of trust. 

A board should never approve innovation projects. They cannot evaluate uncertain outcomes. Instead, they can evaluate whether uncertainty is being reduced responsibly and should ensure capital exposure is bounded, learning velocity is high, and escalation rules are clear. 

A board should only oversee three things:

1. Portfolio exposure
   How much capital is at risk simultaneously
   
   
2. Funding discipline
   Continuation only after evidence
   
   
3. Separation of logic
   Exploration metrics that are not mixed with operational KPIs
   
   

Board should never ever select ideas, approve technical directions, demand detailed forecasts, integrate ventures prematurely. These actions destroy optionality. 

But when governed correctly, downside is limited, upside remains uncapped, and management credibility increases. Transformation becomes auditable without becoming a foregone conclusion. 

No industrial company fails to transform because they lack ideas, talent, or technology. 

They fail because they apply operational governance to exploratory activities. 

Once the board governs uncertainty instead of trying to eliminate it, transformation stops being a gamble and becomes a managed asset class. 

Why industrial startups don't scale like software, and why that matters for corporate strategy (5/6)

Over the last decade, industrial companies have tried to adopt startup methods like agile teams, MVPs, incubator and venture units with the expectation that small teams could rapidly build scalable businesses, just like in software. Yet most industrial digital initiatives plateaued in the same place where successful pilots never became large businesses. The explanation is usually organizational. But the pattern is too consistent to be cultural. 

In SaaS, the sequence was to build product, find users, scale distribution and optimize economics. In industrial systems the sequence is to prove technical reliability in reality, integrate into operations, earn organization trust, and scale commercially. Applying the former to the latter leads to the stereotypical "pilot purgatory', and the product market fit only comes later. 

The Lean Startup thinking has popularized the concept of a minimum viable product. And in fact, the advent of LLMs, vibe coding and agents have made building software MVPs easier than ever. But an industrial MVP still requires safety acceptance, workflow integration, downtime risk, and operator trust. Learning cycles are constrained by operations, not coding speed and the experimentation bandwidth massively narrows. As opposed to software which scales with distribution, industrial solutions scale with deployment capacity from integrators, commissioning, change management, training, and liability acceptance.
Therefore growth is stepwise, not exponential.

Because risk reduces differently in industrial systems, stage gates must evaluate different evidence. Early stages must mitigate technical and operational risk, later stages market and commercial risk. 

Industrial transformation will not be driven by apps on top of factories. It will instead be driven by systems that cross the boundary between software and operations. And the highest value companies in the next industrial cycle will control operational feedback loops, not dashboards. 

Next article (6/6)

Board governance for industrial transformation - managing two business models inside one company.

How to run a corporate venture portfolio without becoming a venture capital firm (4/6)

Operational companies run on annual planning cycles. 

Venture building runs on learning cycles. 

Trying to manage the second with the first results in one of two predictable outcomes. Either the initiatives are frozen by planning, or they run outside governance entirely. 

The solution does not lie in creating a new department, but in implementing a different decision cadence that funds increasingly expensive questions

1. Technical feasibility
   
   
2. Real customer usage (-> viability)
   
   
3. Repeatable deployment (-> product market fit)
   
   
4. Scalable economics

Funding should not be a commitment to success belief, but a purchase of evidence. Capital should only be provided when uncertainty decreases. 

Most companies have steering committees, project reviews, and budget committees to evaluation progress against plan. In contrast, a venture forum evaluates risk versus evidence and is characterized as follows

• Frequent meetings (6-10 weeks)
  
  
• Very small group 
  
  
• Authority to stop initiatives
  
  
• No project ownership
  
  
• Compares initiatives against each other
  
  

This forum manages allocations, not execution. It is making kill decisions based on transparent stop conditions: 

• No repeatable customer usage
  
  
• No technical feasibility
  
  
• No economic faith
  

Because the conditions are agreed beforehand, stopping is not political. And the organization can celebrate disciplined termination more than heroic persistence. 

This requires a clear delineation of roles and responsibilities. Leadership controls the funding continuation, the strategic direction, and the portfolio size. The teams control the solution, the iterations and the learning speed. Corporate burden is reduced and outcomes are improved. 

The role of the executive changes from approving projects to continuously reallocating capital. 


All of this may sound like software venture building, but industrial initiatives behave differently:

• hardware dependencies
  
  
• pilot purgatory
  
  
• deployment friction
  
  
• long validation cycles
  
  

and so the operating model must adapt. 


Next article (5/6)

Why industrial ventures are structurally different from software - and why most corporate playbooks fail here 

Innovation culture doesn't exist - governance and incentives do (3/6)

When industrial innovation initiatives stall, the diagnosis is almost always cultural: risk aversion, silo thinking, lack of entrepreneurial mindset. 

This diagnosis is not wrong, and companies respond predictably by launching training programs, incubators, intrapreneurships, innovation days et cetera et cetera et cetera. And yet the same patterns reappear where promising initiatives expand too early, weak ones linger, and transformational ones rarely scale.

If behavior does not change after repeated cultural interventions, the cause is probably structural rather than psychological. Managers optimize for what is rewarded - approval, budget adherence, and avoiding visible failure - and thus produce optimistic forecasts, broad roadmaps and incremental proof. Clearly this is rational behavior, and the organization punishes the alternative. 

This governance creates a doom loop: An upfront business case plan is created with massive amounts of detail and the team commitments to such plan. But then the evidence contradicts the plan and the team is forced to defend rather than adapt. The funding continues to protect prior decisions, and terminations happen late and are expensive. It is the ultimate irony that nobody chose this insane path, but the governance enforced it. 

There is a better way. Instead of asking people to think like entrepreneurs, the corporation should adopt a system with three mechanisms which reward and entrepreneurial behavior:  

1. Mile stone funding focused on progress
   
   
2. Predefined kill criteria that make stopping a successful outcome
   
   
3. An independent decision forum to separate continuation decisions from project sponsors
   

An organization will always behave rationally within the rules it is given.
The leaderships job is not selecting ideas, it is designing decision environments.

Most companies understand they need a portfolio logic, but few understand how to operationalize it without losing control. 


Next article (4/6)

How to actually run a corporate venture portfolio