The Long Middle of the Founder’s Journey: Board Alignment, Trade-Offs, and Realism

Nobody talks about the long middle of the founder's journey. The years after conviction, but before clarity. It is the time where most startup stories are decided and characters are shaped.

I have watched version of this play out with an industrial SaaS founder I am privileged to advise. From the onset everything looked great. The company solved a hard problem uniquely, had real customers, recurring revenue, credible investors and a friendly board.

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The friction showed up as the company scaled: the expectation an industrial company should grow liek a SaaS unicorn, timelines not matching reality, a bulging capital structure. Not every good business can be stretched into someone else's success story. Founder and board constantly negotiated about control vs. trust, founder ambition vs. realism, speed vs. durability. Board meetings started optimizing for a clean narrative instead of hard trade-offs. Fundraising logic started dictating operating decisions. 

The founder was working harder and controlling less and the runway no longer was just financial, it became psychological.

Industrial software startups very rarely IPO. They are acquired by large industrial companies if they manage to become really large. If they are on the smaller side they get bought by private equity platforms that price EBITDA over growth.

Once those choices become explicit, everything come into focus: product calls get easier, conversations with acquirers become deliberate instead of reactive, and an exit stops feeling like failure and starts looking like completion. 

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This particularly ending did not produce a 10x outcome for the investors. 

It did make a founder who understood the system he was in, the trade-offs it forced, and what it cost to pretend otherwise.

It did produce a very handsome outcome for the founder who is already thinking about his next venture.

VCs are paid to hunt for 10X outliers. Founders are building for life-changing wealth.

Venture capitalists are structurally incentivized to hunt for rare outlier startups with greater than 10x outcomes that can return the entire fund.

Most early exits do not produce these >10x returns, but exits with lower multiples can be life changing for founders.

This leads to a major structural misalignment because first-time founders frequently lack the market timing and M&A experience necessary to recognize the optimal window for an exit.

Every Stage Has a 50% Conversion Rate

The path for startup founders with VC funding seems obvious: keep raising, always. The rationale: later-stage exits are worth dramatically more since exit values climb steeply at every stage, and dilution doesn't climb nearly as fast.

But that thinking ignores the odds of getting there. Roughly half of all companies don't make it to the next round, and the odds get longer at every stage and only a small fraction of seed-stage companies ever reach the later rounds at all. Ilya Strebulaev of Stanford GSB recently published his findings on nearly 53,000 companies that raised their first round in 2014, tracking their fate through 2023: only 4% had an IPO exit, and 20% exited via M&A.

Founders Should Ask Themselves This Every Six Months

So the math doesn't give a clean "always keep raising" answer. It comes down to one question a founder needs to ask at every stage:

Are my odds of reaching the next round better than the average company sitting where I am right now, or am I just hoping?

If yes, raising again beats cashing out, assuming the value climbs faster than the dilution and the risk. The dilution is the easy part to model. The risk is much harder to assess.

At later stages, top line growth is a simple proxy for risk. Rory O'Driscoll of Scale Venture Partners documented this in his seminal analysis termed the 'SaaS Mendoza Line of growth': once private companies fell below the expected growth rate, only 35% saw even one year of growth re-acceleration, and fewer than 10% managed two or more years of it. In other words, once you fall below the growth rate expected by your next round's investors, your odds are very slim.

If There is Growth Momentum: You Live to  Raise Again … And Die Another Day

The key metric is the momentum of that growth, which shows whether the growth rate is accelerating or decelerating.

If the growth rate is rising, and rising faster each quarter, it's worth to keep on pushing.

Yet even then, founders face risks they do not control: market windows opening and closing in times and ways no one can predict, competitor gaining market share, key people leaving.

SaaStr's Jason Lemkin put it bluntly in a 20VC podcast: 'When you start to get into nosebleed territory it has to be worth 10X to go for it. Building something generational - you kind of know as a founder when you're on that path.'

He added that 3X over a three-to-four-year horizon isn't worth it: "There is way too much risk for not enough money."

If you can't see a path to 10X, you should seriously consider selling.

If The Growth Rate Stalls: Head for the Exit

The easy case, and the one rarely talked about, is the M&A exit.

If growth is still rising but the increments are shrinking, the founder and investors should push for an orderly exit.

If growth is already falling, the window closed a few quarters ago — and they're usually the last to know.

Fixing the SAFE Gap: How Founders Can Rebuild Governance

Most seed-stage companies never had a board.

At pre-seed, SAFEs account for roughly 90% of U.S. deals. At seed, where a priced round is a real alternative, roughly 64% of rounds still get raised on SAFEs and another 10% on convertible notes. Only about 27% of rounds are raised as priced equity, the one structure that seats an investor-director and creates a fiduciary board.

The SAFE instrument was built to close rounds in days instead of months, argue less about valuation, and keep the cap table simple. But the few weeks of speed come with a cost, and that cost is the loss of governance.

SAFE holders are contract counterparties, not stockholders. They have no fiduciary duties and no board seats. The result: companies now raise tens of millions of dollars and operate for three or more years without a board ever being constituted.

When there is no priced round, no one has the right and the standing to ask the hard questions. There is no structured review of what's working and what isn't. There is no forum where outside perspectives are exchanged without management in the room. There is no mechanism to replace a CEO or key executives who stop performing.

A company with no outside check during that period of highest uncertainty has no check on the founder's blind spots: The VP hire nobody vetted before the offer went out, the churn number nobody tracked consistently, the work the founder was still doing two quarters after it should have been delegated.

The probability to graduate from Series Seed to Series A has within two years after the Serie Seed has plummeted since SAFEs have become the dominant fundraising instrument, caused by a mix of macroeconomic shifts and cap table mechanics. Multi SAFE stacking and inflated valuation caps have all led to increased VC selectivity with higher expectations. 

The inflation of SAFEs has removed the automatic mechanism for governance. Until a priced round seats a real board there is a void for someone else to purposefully fill that gap. It is where founders should leverage informal allies to replicate boardroom oversight without the administrative burden.

Enter the experienced angel advisor.

Experienced angel don't need a board seat to help close that gap. They can provide real oversight, with no formal structure and no extra dilution attached to it. They substitute organizational power with personal power. 

Experienced angels will help establish clear operating rules.

• They set the ground rules early. They agree, in the first real advisor conversation, on what gets reviewed and how often.

• They introduce a cadence. A structured check-in every month or every other does the job a CEO evaluation and an executive session would otherwise do.
  
  

• They will track the same signals a Series A board would before the company is forced to: Non-financial North Star metrics tied to customer value, revenue, cash burn and runway.

• They review the first VP hires before the offer goes out. One bad key hire costs a company more than any single missed sales quarter.

• They help pick the next round's investors, not just the price. Capital comes with governance and named partners.

Founders should get experienced angel advisors on board at the pre-seed stage and agree on their explicit roles. Then and only then the angel advisors will deliver value all the way to the first priced round, and sometimes even beyond.

This post was inspired by The Cure for Bad Boards Isn't No Boards by Pascal Levensohn, Private Company Director, July 27, 2026.

Disposable UIs and Bedrock Foundations: The new architecture of B2B SaaS.

The hot take in B2B SW right now: You don't need enterprise software, you just let agents access the database and let them do the work. 

But several signals that are pointing to something different and more nuanced.

Jason Lemkin's team runs SaaStr on 3 humans and 20+ AI agents. His conclusion isn't that CRM is dead. It is the opposite: 20 agents are writing directly to a database and produce 20 different definitions of a qualified lead. No shared forecasting logic, no audit trail. It turns out the agents need the shared system of record as much as the humans do.
Salesforce's answer is to go headless where CRM acts as an API/MPC substrate, with Slack, voice, and custom UIs as interchangeable surfaces on top. 

Andre Wenz of SAP Signavio pushes the argument one layer up. He observes that today's application screens of pipelines, stage views, and approval forms aren't the process. Instead, they are cloud era artifacts, born when software was too expensive to rebuild per task. With AI, the interface itself is generated per objective and is disposable. The surface is fluid but the foundation is orderly.The canonical records, permissions, policies and controls are the bedrock underneath. 

And then there is Celonis. They are already living the 'foundation, not the app' thesis on top of  any single ERP stack. Their Process Intelligence Graph mines the actual processes across SAP, Oracle, Salesforce and other B2B apps and feed that context to agents via MCP. 

Put together, here is my read on where this goes: 

Generated, fluid, disposable UIs will become the norm; fixed screens will be limited to very few use cases and demos. 

The system of record and governance layers become more valuable because ungoverned agents are more dangerous than ungoverned humans. But rebuilding a system of record is hard and will take to time get deployed and distributed. 

The real battleground is the governance layer. 

SpaceX wants 100,000 satellites in VLEO, making it the default orbit for the space industry

On July 6 2026, SpaceX asked the FCC for authority to fly Gen3 Starlink 100,000 satellites spread across two Very Low Earth Orbit (VLEO) shells at roughly 325 km and 475 km. This is a more than sixfold increase over the 15,000 satellites the FCC has approved for Starlink so far and in orbits far lower than Starlink's original constellation. 

While Starlink built its original business at 550 km and higher, Starlink already runs more than 650 satellites at 340 to 370 km to support direct to cell service. It moved lower when a real business opportunity emerged in the form of direct to device connectivity, and Gen3 is the next step and a full commitment to VLEO as the default orbit for the next generation of the constellation.

Below 450 km, residual atmosphere drags an unpowered satellite out of the sky within weeks. For sustained operations below, significant material science and physics problems need to be solved. Two key solutions are in the early stages of validation:

The first is propulsion. Air breathing electric propulsion (ABEP) ingests the residual atmosphere itself, mostly atomic oxygen, as reaction mass converts the abrasive oxygen into propellant. ESA's DISCOVERER program and NATO backed work at Kreios Space are pushing this from research into flight hardware. Once ABEP is flight qualified, a VLEO satellite trades a lifespan measured in weeks and months for one primarily limited only by the fuel it carries. 

The second is autonomy. A constellation spread across two tightly stacked shells at 325 km and 475 km, with satellites moving between altitudes and inclinations as SpaceX's own filing describes, cannot run on ground computed maneuvers alone. Spacecraft in VLEO should hold station, avoid collisions, and execute rendezvous and proximity operations with minimal supervision from the ground. Pair that with ABEP and a satellite gains the ability to not just survive at 325 km, but to actively move, between shells, between inclinations, even between orbits, in response to demand.

Propulsion solves how long a satellite can stay in VLEO. Autonomy solves how it moves once it gets there. Together they turn a fixed shell into a fluid, maneuverable layer of orbit, exactly what SpaceX's own request for flexible altitude and inclination assignment assumes will exist.

For investors, the signal is clear. The world’s largest satellite operator just announced to make VLEO its primary orbit at a scale that dwarfs every LEO constellation flown or proposed before it. That is a strong vote that the drag problem gets solved in time to matter, and it will pull capital toward the propulsion and autonomy companies that make it possible.

For founders building in VLEO, the filing is validation and warning at the same time. Validation, because the largest player in the industry just confirmed where the puck is going. Warning, because it will  be hard to compete with an even partially built 100,000 satellite constellation for orbital slots, spectrum, and coordination. 

Everyone still talks about LEO as the orbit that matters. As of July 6, VLEO is the New New Thing.

Robotics Is Not About Robots

Co-authored by Oana Olteanu of Motive Force

 

Henry Ford’s Rouge, source

A robot running at 95% accuracy sounds impressive. In a factory running fifty cycles an hour, that means fifty failures every shift. Each one requires a human to intervene, log the incident, reset the system, and decide what happens next. The robot is working. The deployment isn't.

This is the gap that ends most robotics companies. Not the hardware. Not the vision model. Not the manipulation benchmark. The system around the robot.

We have been spending time with founders and engineers working in physical AI, and the same failure mode keeps surfacing. The robotics conversation is almost entirely about the machine: better models, better form factors, better demos. The deployment conversation barely exists. And almost nothing in the stack was built for what comes after the demo.

Physical AI is not a robot problem. It's a skills, workflows, and learning system problem.

The hierarchy nobody draws

At its core, a robot is just an embodiment: joints, sensors, actuators. The real abstraction sits above the machine.

Physical work can be structured as a hierarchy: scenarios decompose into skills, skills into tasks, tasks into trajectories. This defines how complex industrial processes become machine-executable actions. It is also, not coincidentally, how enterprise software has always modeled work. The systems we worked on at SAP were built on exactly this kind of decomposition. What's different now is that AI can learn the levels of this hierarchy, not just execute against a fixed specification.

The primary challenge is therefore not the robot. It's the toolchain that lets robots learn, operate, and improve: simulation environments, workflow models, perception and planning stacks, data pipelines, the software that converts human demonstrations into reusable skills. This is the infrastructure that lets automation move beyond isolated machines toward scalable systems.

Hardware is a data acquisition device

Here is the reframe that matters for thinking about where value accumulates.

Robots increasingly function as data acquisition devices. Each deployment generates operational telemetry and demonstrations that feed back into learning systems. Over time, those feedback loops produce skill libraries, workflow abstractions, and training datasets that let automation capabilities transfer across tasks, sites, and embodiments.

This is why many of the most interesting robotics founders are not primarily designing new machines. They are building learning systems that combine simulation, real-world telemetry, and human motion data to continuously improve performance across fleets. The compounding advantage is in the accumulation of operational data and the ability to convert it into reusable automation capabilities.

One idea we keep coming back to from several conversations: the intelligence doesn't have to live inside the robot. One architecture inverts the assumption entirely: the brains are in the environment, and the robot is just a tool the physical model uses to act on the world. That inversion changes the cost structure, the upgrade path, and the distribution model. It also makes the robot itself much cheaper, which is the actual bottleneck for adoption in most markets.

The deployment gap is real, and it's not about the model

The pattern is consistent: great demos, almost no real deployments. And when companies do deploy, they hit a different set of problems than they expected.

Integration with existing operations. Financing and asset lifecycle. Service networks. Change management. Human workflow alignment. These are not engineering problems. They are adoption problems, and almost nothing in the robotics stack was built to solve them.

The deployment stack is where most robotics companies die: not on the benchmark, but on the installation. Nobody can plug the robot into the existing ticketing system. Nobody has figured out how to triage a failure at 2 am. The operator doesn't know when to trust it and when to override. And the vendor who sold the hardware has no infrastructure to help.

This is the tank problem. Germany built the most complex and complicated tanks in the second world war but lacked scale everywhere. The Soviet Army built the most tanks of all, but they were designed as disposable items. The United States built simpler tanks at scale with a massive supply chain and service network.

Robotics is still in the beautiful tank era. The market winners will build the whole logistics network.

Berlin, summer 2024. Research.

The agent runtime is the missing layer

There is something happening in the software stack that will define which robotics companies matter in the next few years, and it is barely being discussed.

The architecture of a capable physical AI system is, at its core, two things: the model and the agent runtime. The models are getting better and everyone is paying attention to that. The agent runtimes are almost invisible in the conversation, and yet the performance of the whole system is insanely dependent on them.

An agent runtime for physical AI has to do things that software has never had to do before. Operate continuously, not just on demand. Manage asynchronous input from the physical world. Reason about state across long time horizons. Coordinate between multiple agents pursuing different objectives in the same physical space. Log decisions in a way that's legible to humans who need to understand what happened and why.

This is not a solved problem. What most people are building today are either model wrappers or task-specific automation. Neither is infrastructure for continuous physical operation. Neither was designed for a world where the agent is running, the robot is acting, and a human somewhere needs to understand and trust all of it.

The companies building the agent runtime layer for physical AI don't have a clean category name yet. That's usually the right sign.

Where value actually accrues

Selling a robot is a capex transaction. Scaling automation requires owning the workflow layer: the point where robotic skills integrate with customer operations, business processes, and human workflows.

That layer determines adoption. It determines utilization. It determines whether a company accumulates the operational data that makes the next deployment better than the last one. And it determines whether, when a customer's processes change, the automation changes with them or breaks.

The companies that define this market won't necessarily be those that build the most sophisticated machines. They will be those that develop the most effective learning and workflow systems around machines. The robot is the vessel. The system is the product.

What we’re looking for

The category map writes itself. Simulation and training infrastructure. Workflow modeling tools. Agent runtimes for continuous physical operation. Incident management and operational orchestration. Context layers for physical assets. End-to-end deployment platforms that treat automation as a service, not a product.

None of these require building the robot. All of them are more defensible than the robot.

We’re looking for founders who see physical AI as a systems problem, not a hardware problem. Who understand workflows and operations, not just models and sensors. Who think in hierarchies: what is the scenario, what are the skills, what are the tasks, what are the trajectories, and how does learning flow back up through all of them.

If you're building in this layer, or working in physical environments where you've felt the deployment gap firsthand, we’d genuinely like to talk. 

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About Motive Force: We back technical founders at the idea stage, before the category has a name. If that's you, reach out.

About Christian Dahlen: angel investor with investments in robotics and industrial software and a Ph. D. in engineering. Select investments include Wandelbots, deltia.ai, and SDA.

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.