“Three billion human lives ended on August 29. The survivors of the nuclear fire called the war Judgment Day” S. Connor

According to James Cameron’s Terminator 2, this Saturday, 29 August, is Judgment Day. That movie showed an AI (Skynet) becoming self-aware and putting humanity on the brink of extinction.

This is a theme that you see repeated in the Matrix, Battlestar Galactica and pretty much any other science fiction text tied to AI. It’s also what people are worried about with the current iterations of AI: are we going to be the harbingers of our own doom?

As the CEO of an AI company, I’m happy to report that this isn’t the direction we’re going in.

To explain why, let’s start by looking at what a robot actually is.

What do you think of when you hear the word “robot”?

Funnily enough, it’s probably age dependent, and can be anything from Rosie in the Jetsons to Johnny 5, the Iron Giant or C3PO. The ironic thing is that 95% of the time whatever robot you have in your head is humanoid. The other 5% it’s R2D2.

As depicted in the Sistine Chapel, man is created in God’s image, so it’s easy to see how our concept of a robot has consistently been created in man’s own image. It’s simply what we know.

The question then becomes why…

Looking at the most successful robot deployments to date, they are decidedly not human.

We have Tesla and Waymo cars, whose designers decided would be better modelled on KITT from Knight Rider than Johnny Cab from Total Recall. In both vehicles the companies realised that, rather than create a robot taxi driver, it would be best simply to have a driving wheel.

The other major success story is the Roomba robot vacuum. Rather than a humanoid pushing a vacuum, the designers went with an elongated hockey puck, 4-5 inches tall.

Looking ahead, we’re starting to see variations on that theme. There are a number of window cleaning robot demos and concepts that are a lot more spider than spiderman. You have Eric Schmidt backing a wheel-based robotic design by Genesis AI called Eno. You also have Launchpad Build AI, and we’re firmly in the camp of unhuman-style robots for manufacturing and assembly.

Why not humanoid? Well, take an action like screwing something in. It takes a number of steps:

1) Thread screw
2) Line up screw with hole
3) Pilot hole, with no cross threading
4) Torque to flat, without stripping the screw and without over or under-torquing
5) Inspection

When a human or humanoid does it there are a number of failure points. Take threading the screw. We have a client where the highest variability in their human cycle time is caused by dropped screws. Threading can take 2-25 seconds. Replace that with a blow-fed screw and top down screwdriver and it is now a 2-3 second operation, guaranteed.

Torquing is easier too. Screwdrivers can have torque sensing, but they still require stability. That doesn’t exist with humans or humanoids. By contrast, a fixed-position, non-humanoid robot has that today. All of this is a big deal: the difference between a demo and earning your way onto the factory floor is repeatability.

Say you drive a screw correctly 95% of the time. That sounds like a machine that works. But put 20 screws in one product and your first pass yield is 0.95 to the 20th, which is 36%. Two out of three units come off the line defective. For a company that’s game over.

Given that, would you rather trust a machine that has been designed and built especially for the purpose? No wasted movements, no extraneous appendages. Or do you want the humanoid with two legs and all kinds of joints between its feet and fingers, and that is looking to figure out screwing along with one hundred thousand other skills?

In fairness the humanoid is more aesthetic. It also fits in with our utopian ideals. We don’t want messy, and our dreams are definitely better served by a single machine that can solve any problem and do anything.

The issue with aiming for perfection is that the work along the way is asymmetrical. All of the world’s most expensive consultants will espouse the 80/20 Rule: the majority of the value coming from a small percentage of the work.

Manufacturing is different – here it’s a 90/10 rule. You’ll get 90% done and then need to spend 90% of the total time on the last 10%.

This is because things are messy. Tolerances are tight, variances and expectations hard to manage. When you want to be able to handle everything, there is an almost infinite number of possibilities you have to account for. It’s why, going back to the Terminator, you need to be part man, part machine.

At Launchpad Build AI, we’re working with a customer on a project that involves stacking a number of components. Even though our error rate is very low, we still had an internal debate about recovery: if a step goes wrong, what happens?

Initially, our engineering/development team wanted to go over every failure mode and simulate both mundane and elaborate recovery sequences. In the end we took a different approach: saying that, in the rare case of an issue, a human can pop out the work-in-progress and start over. Development time -> the 30 seconds it took to communicate.

For some that was obvious, to others a surprise. Pragmatically it means the customer gets their solution 90 days earlier and at a meaningfully lower cost than if we spent three months of engineering time to solve what to do in a black swan event.

It’s a good time be building robots. Capabilities are increasing exponentially, while costs are coming down. Everyone is awake and attuned to the possibilities, looking to do something.

The next few years will decide what sort of robot fate has in store for us. At Launchpad Build AI we’re focused on the solution that is implementable immediately. I think it’s going to work.

After all, “There is no fate but what we make for ourselves.”

Last month, Launchpad Build AI hosted an exclusive summit in Edinburgh, Scotland, to discuss the future of AI and robotics.  We were privileged to have the event covered in a series of articles by leading publication, Newsweek.  In our final post about the summit, we look back at the event and showcase some of the insights it revealed.

If the earlier sessions were about bottlenecks, the later panels at Launchpad Build AI’s World of Tomorrow Summit turned to something bigger: who actually controls AI’s industrial future.

Newsweek’s Megan Carnegie captured the backdrop in her final report from the Summit. This was a summer when “SpaceX, OpenAI and Anthropic are all expected to hit public markets within the same window,” even as the same handful of firms supply most of the industry’s compute, cloud and capital.

That concentration worried some in the room more than others. Launchpad’s President, Yoav Zingher, was one.  As quoted by Newsweek, he warned that  “If the future is going to be a few big platforms, I think China will win that game…They have a controlled economy, collect as much data as they want, and direct capital into priority sectors. I don’t think that’s a good outcome.”

Not everyone shared his worries, however. Lavrock Ventures’ Steve Smoot argued the structure would simply reward whoever could isolate their advantages and build the most valuable proprietary workflows on top of it.

That debate fed into a second, more practical one: whether to build narrow and now, or bet on general-purpose robotics for later.

Our CEO Jon Quick made the case for the former. As reported by Newsweek, he favors highly specific, production-ready automation over “reinventing everything.” In Jon’s view, the priority is automating discrete, high-value tasks and delivering utility today.

Exponential’s Ricky Horwitz noted that, regrettably, humanoid robots draw capital largely because they’re easier for people to understand.

By the closing panels, the conversation had settled on what may be the real constraint: data. Our Vice President of Artificial Intelligence, Yannis Georgas, told the Summit he has yet to encounter a company – across manufacturing, construction or utilities – that has fully “got its industrial data right.”

As Newsweek warns, “Without the right skills to capture and structure data – and without the data itself – many firms risk being locked out of the gains AI promises to deliver.”

This is the challenge World of Tomorrow leaves us with – and the one Launchpad Build AI exists to solve.

You can read all the articles in the Newsweek series about Launchpad Build AI’s Summit here:

https://www.newsweek.com/world-of-tomorrow-ai-factory-floor-12106904

https://www.newsweek.com/world-of-tomorrow-scotland-ai-robotics-12116700

https://www.newsweek.com/ai-race-models-manufacturing-world-of-tomorrow-12122020

https://www.newsweek.com/ai-industrial-power-struggles-world-of-tomorrow-12146543

Last month, Launchpad Build AI hosted an exclusive summit in Edinburgh, Scotland, to discuss the future of AI and robotics.  We were privileged to have the event covered in a series of articles by leading publication, Newsweek.  In this post, the third of four, we look back at the event and showcase some of the insights it revealed.

Once the pipers packed up and Launchpad Build AI’s World of Tomorrow summit moved from Edinburgh Castle into the working panel sessions, the mood shifted from ceremony to hard questions.

Held inside The Caves (a converted stables on Niddry Street in the heart of the Old Town) the panels kept circling one tension: as Newsweek’s Megan Carnegie put it in the third of her articles about the Summit “software is racing ahead, while factories and supply chains still move too slowly.”

Chorus.ai founder Roy Raanani named the bottleneck directly, telling the Summit that AI is in such demand, that “the bottleneck to growth is in the upstream supply chain given the time needed to build factories and components.” 

In a market that rewards speed, he argued that long-term supply relationships with infrastructure providers have become one of the few real advantages available to startups working in the physical world.

Simulation emerged as a recurring answer to that speed problem. Our CEO Jon Quick noted that digital twins let teams “tweak, simulate and refine in a much tighter loop” before ever touching physical hardware, though panelists were candid that the models behind these simulations remain imperfect and still under-trusted.

That gap between capability and trust ran through the day’s toughest conversations. Orrick, Herrington & Sutcliffe partner Alex Sobolev flagged the liability problem at the center of AI’s move into physical systems: “When AI goes wrong, there’s huge uncertainty around who is responsible, especially when causation is difficult to establish in a black-box system with no clear explanation for how a decision was reached.”

Speakers repeatedly returned to autonomous vehicles as the cautionary tale for what happens when promise outpaces proof.

According to our own Vice President of Artificial Intelligence, Yannis Georgas, Europe is firmly in the driving seat when it comes to regulation.  Newsweek summarized his point: “AI models controlling physical machinery are categorized as ‘High-Risk’…meaning systems shouldn’t be pushed into production without robust simulation and strong data foundations to verify safety first.”

A lot remains to be worked out for AI’s potential in the manufacturing industry to be fully unleashed, but we believe the discussions we hosted in Edinburgh marked a meaningful milestone along the way.

You can read all the articles in the Newsweek series about Launchpad Build AI’s Summit here:

https://www.newsweek.com/world-of-tomorrow-ai-factory-floor-12106904

https://www.newsweek.com/world-of-tomorrow-scotland-ai-robotics-12116700

https://www.newsweek.com/ai-race-models-manufacturing-world-of-tomorrow-12122020

https://www.newsweek.com/ai-industrial-power-struggles-world-of-tomorrow-12146543

In June, Launchpad Build AI hosted an exclusive summit in Edinburgh, Scotland, to discuss the future of AI and robotics.  We were privileged to have the event covered in a series of articles by leading publication, Newsweek.  In this post, the second of four, we look back at the event and showcase some of the insights it revealed.

Launchpad Build AI’s World of Tomorrow Summit began the way few tech gatherings do: atop an extinct volcano, inside one of Europe’s oldest fortifications. Guests were piped in from Crown Square for dinner in the Great Hall of Edinburgh Castle, and as Newsweek’s Megan Carnegie observed in the second of her four articles, “instead of kings, clans and statecraft, the discussion centered on AI, robotics and advanced manufacturing.”

The evening was built to make a point. Newsweek described it as “a reminder that Scotland wants to shape the next industrial era, not watch it unfold.” The country’s pitch rests on more than just scenery: it offers “a smaller, well-coordinated ecosystem where AI and robotics companies can work closely with government, universities, investors and industry.”

Dinner, however, was a chance to lean fully into Scotland’s impressive heritage. Newsweek reported a feast “prepared by acclaimed Scottish chef and restaurateur Tom Kitchin, a haggis ceremony (complete with a reading of Robbie Burns’ Address to a Haggis,) and a harp performance.” This was Scottish tradition front and center for an audience focusing on the technology of the future.

As the Tartan Army was charming World Cup audiences around the world, the message inside the castle was clear: Scotland wants the same kind of global attention for its role in the next wave of industrial technology.

Guests included figures such as Mike Splinter of TSMC’s board, Alistair McFarland of Leonardo, Chorus.ai founder Roy Raanani and Scottish Enterprise chief executive Adrian Gillespie.

For Launchpad Build AI, hosting the dinner in Edinburgh was a natural choice. Our CEO Jon Quick told Newsweek it’s a lot to do with the similarity between North American and Scottish cultures: “I’ve found the Scots to be such earnest, high-quality people. They’re the kind of people you want to do business with, and we’ve got big plans for the relationship here.”

The evening also carried real substance behind the ceremony. Scotland’s innovation minister, Ben Macpherson, pointed to the country’s research pedigree, noting that Europe’s first AI research group was set up at the University of Edinburgh in 1963. 

As he told the assembled guests, “That makes Edinburgh the second university in the world after Stanford to offer AI as a research subject, and we’re looking to build on that history.”

It’s also a history we at Launchpad Build AI are proud to be building on, one castle dinner and one summit at a time.

You can read all the articles in the Newsweek series about Launchpad Build AI’s Summit here:

https://www.newsweek.com/world-of-tomorrow-ai-factory-floor-12106904

https://www.newsweek.com/world-of-tomorrow-scotland-ai-robotics-12116700

https://www.newsweek.com/ai-race-models-manufacturing-world-of-tomorrow-12122020

https://www.newsweek.com/ai-industrial-power-struggles-world-of-tomorrow-12146543

Last month, Launchpad Build AI hosted an exclusive summit in Edinburgh, Scotland, to discuss the future of AI and robotics.  We were privileged to have the event covered in a series of articles by leading publication, Newsweek.  In this post, the first of four, we look back at the event and showcase some of the insights it revealed.

AI has spent the last few years proving what it can do with words, images and code. The next test is harder: can it hold up on a factory floor, where mistakes aren’t just embarrassing – they’re expensive, physical and sometimes dangerous?

That question sat at the heart of World of Tomorrow, the gathering Launchpad Build AI hosted in Edinburgh last month. As Newsweek’s Megan Carnegie put it in the first of her articles about the event, “the bigger story is where AI is heading next: into robotics, advanced manufacturing and the physical systems that make, move, and assemble things in the real world.”

Newsweek framed the stakes plainly: “A chatbot can make things up; a robot arm cannot.”

That distinction – between software failure and physical failure – is exactly why we felt this conversation needed a room of its own, and why we chose to hold it away from the usual tech-conference circuit.

The guest list reflected the scope of the challenge. The roughly 50-person gathering brought together chip, compute and simulation companies including Nvidia, TSMC and Nebius; industrial, defense and technology leaders from organizations like Lockheed Martin UK, BAE Systems Air, Leonardo, Ericsson Ventures and Booz Allen Hamilton; investors and advisers from J.P. Morgan, Lavrock Ventures, Squadra Ventures, Bain and Orrick; and figures from the Scottish government and its economic-development bodies.

The pressure behind the moment is real. Newsweek noted that, in the US, “the workforce still has 1.7 million fewer people than it did in February 2020…even as manufacturers continue to report hiring strain.”  And in the UK, “British firms are expected to invest roughly $650 billion in [reindustrialization] by 2028, while supply chains remain fragile and vulnerable to shocks.”

Our own CEO, Jon Quick, summed up the disconnect this creates: “We all want reindustrialization, But the crossover in conversation around AI and manufacturing is hard to decipher—there’s no pragmatic middle ground of where to go and what to do.”

That’s the gap World of Tomorrow was built to close: not another showcase of what models can say, but a working session on what it will take to get AI safely and usefully onto the factory floor.

Over the following days, leaders from industry, government, defense and investment sat down in Edinburgh to find a way to do just that.

You can read all the articles in the Newsweek series about Launchpad Build AI’s Summit here:

https://www.newsweek.com/world-of-tomorrow-ai-factory-floor-12106904

https://www.newsweek.com/world-of-tomorrow-scotland-ai-robotics-12116700

https://www.newsweek.com/ai-race-models-manufacturing-world-of-tomorrow-12122020

https://www.newsweek.com/ai-industrial-power-struggles-world-of-tomorrow-12146543

This weekend is Super Bowl Sunday in the U.S., and it got me thinking about the parallels between football and startup life.

In both, quarterbacks and founders get most of the credit. And in both, that misses the point. Without the full team, there is zero chance of success. In football, you need linemen to have time to throw, receivers to get open and catch the ball, and a defense that keeps the other team off the board. You can even win when everyone else does all the work (see: Manning, Eli).

Startups work the same way. Engineering, R&D, sales, finance — if there’s a gap in any one of them, the whole thing breaks down.

The attitude matters too. Tom Brady has talked about treating every practice like it was the Super Bowl, so when game day came, it felt like just another day. Startup life is similar. When you start, you aren’t at flashy stadiums. We are talking coffee shops and office/warehouses whose best attribute is the price. Travel is discount flights at weird hours instead of business class or PJs. While we aren’t Apple or Google… yet… we need to act like we are. We need to behave like we are going to walk in and present to Tim Cook, or that any customer we are going to call is going to pick up the phone. The early days are like spring practices… quiet… alone.

John Wooden said, “How you practice is how you play.” I’ve always liked that quote, but what resonates more over time is the idea behind it: character is built when nobody is watching. The real measure of a team — or a person — isn’t what happens under the lights. It’s the quiet work. The repetition. Try something… fail… try again… fail… try again… it’s good not great… keep going… millions more reps to go. The days that feel small and invisible but add up to something real.

That’s very much where we are at Launchpad Build AI. We’re in the grind. Late nights. Trying things, many of which don’t work the first time. Learning as we go. There’s nothing glamorous about that phase, but it’s the phase where identity gets built. Every setback forces you to get a little sharper. Every small win builds trust. We’re building the character we’ll need later, even if most of that work is happening far away from any spotlight.

And while football sparked the thought, it’s a big sports weekend all around. Six Nations rugby kicks off — go Scotland — and the Olympics are starting (O Canada!). Whatever you’re going to watch, remember that behind every second you see is what it took to get there.

PXN Ventures forms part of PXN Group, which has £700m AUM across over 120 portfolio companies after launching in November 2025 following the merger of Par Equity and Praetura Ventures. The VC champions high growth businesses based in the North of the UK that are actively transforming their sectors. 

Launchpad Build AI’s investment syndicate includes a mix of specialist VC funds like Lavrock Ventures and Squadra Ventures as well as strategic investors such as Ericsson Ventures, CX2 and Lockheed Martin Ventures.  The company also has financial backing and ongoing support from major Scottish organizations: the Scottish National Investment Bank and Scottish Enterprise.

Automation is mission-critical for manufacturers that wish to be globally competitive. It is also a prerequisite to reindustrialize and reshore.  Current geopolitical uncertainties are increasing supply chain risk, adding extra imperative to increase domestic production.  Launchpad Build AI’s technology tackles these issues head on, pushing the boundaries of autonomous manufacturing systems to make factories faster, safer and more flexible than ever.

Claire Cramm, Investment Manager at PXN Ventures, said: “We’ve admired the Launchpad Build AI team and their work ever since they set up in Edinburgh in 2024.  The company’s vision of how AI will reimagine the manufacturing industry is compelling, and we’re excited to be a part of the story.”

Jon Quick, CEO of Launchpad Build AI, said, “PXN Ventures understands where industrial automation is actually heading: not just the technology, but the operational reality companies face on the factory floor. Their support strengthens our ability to help customers deploy robotics in a way that works for people, scales economically, and delivers real outcomes. We’re excited to welcome them to the Launchpad Build AI family and look forward to working closely together.”

About Launchpad Build AI

Launchpad Build AI, founded in 2020, was created to reimagine manufacturing and revitalize local economies.  The company addresses labour shortages and improves competitiveness by combining proprietary AI-first technology with advanced robotics to help firms manage the optimal integration of humans and robots.  It estimates its systems halve the time and cost of delivering automation solutions.  With offices in Los Angeles, CA, and Edinburgh, UK, Launchpad Build AI’s technology is already deployed in factories across the US and Europe.

About PXN Ventures

PXN Ventures is the venture capital arm of PXN Group, formed as a result of two of the North’s best fund managers (Praetura Ventures and Par Equity) coming together to create an investor that the region deserves. With offices in Manchester, Edinburgh, Leeds and London, it delivers world-class investment programmes at scale to help build the Northern economy, supporting founders across a diverse range of sectors, including deep tech, life sciences and software. PXN Ventures is committed to backing early-stage founders across the North of the UK, with a long and growing list of northern success stories in our portfolio that have received early backing from PXN, including Modern Milkman, Phlo, AccessPay, Street Group and Advanced Electric Machines. As well as its EIS, VCT and institutional funds, including the GMC Life Sciences Fund By PXN Ventures, PXN is also the North West of England fund manager for the Northern Powerhouse Investment Fund II, which is managed on behalf of the British Business Bank.

For more information, please contact:
Dan Bradley, Communications Director, Launchpad Build AI
dbradley@launchpad.build +44 (0)7816 829166

Dearest Reader,

The new season of Bridgerton came out today, and in its honor, please allow me to share a brief tale from the modern industrial age.

Every startup talks about culture. Most of the time, that conversation centers on excellence or hard work. Far fewer talk openly about failure.

Ironically, among elite athletes, there’s a saying: failure doesn’t break culture – it reveals it.

Tying this back to Bridgerton, I think the same is true in relationships. It’s easy to be happy and romantic at luxurious balls or while sauntering down the promenade. Let’s see how it holds up eight years and three kids later – at 2am – when one of them is projectile vomiting. (I love you, Jenn.)

This week, I sent feedback on some website copy asking for carriage returns.

For those Gen Z or younger, a quick explainer: before keyboards and screens, we used typewriters. Pressing “enter” physically returned the carriage – the metal assembly holding the letters – to the start of the page and advanced the paper to the next line.

Our VP of Marketing immediately sprang into action: questioning my sanity and attempting to organize horse and buggies for 50 people moving between two event sites.

And no, she is not Gen Z or a Millennial. She is my age…which is to say, outside both cutoffs.

Once the misunderstanding was cleared up, we laughed.

She laughed hardest.

And honestly? I loved the response.

Not the misunderstanding – but the instinct behind it. Immediate action. No defensiveness. No hesitation. And the confidence to own it, fix it, and move on. That combination says more about culture and character than any values slide ever could.

The instinct to act first, take responsibility, and keep going is hard to teach – and incredibly valuable.

The reality is this: great companies aren’t perfect. They’re not defined by getting it right on the first pass. They’re defined by how fast they recover.

Coca-Cola is a world-class company. Coke II was a world-class disaster.

Meta built one of the most consequential platforms in history. Its first major VR push burned billions before the strategy was reset.

Success isn’t about avoiding mistakes. It’s about recognizing them quickly, adapting fast, and making sure you don’t repeat them.

Startups are a grind. The days are long, the stakes are real, and sometimes the only sane response to the chaos is to laugh – briefly – then get back to work.

Mistakes happen.

Own them.

Fix them.

Learn.

Move on.

Forward motion beats perfection. Every time.

I remain, as ever,

Your most humble servant,

— Jon

I’m going to start with a joke.

A retired engineer gets a call from his old manager: “That machine you always looked after has broken down. Can you come back and fix it?”

The engineer says, “Sure – but it’ll cost you $6,000.”

The manager agrees. The engineer goes down to the factory, looks at the machine, picks up a hammer, and gives one nut a light tap. The machine starts working immediately. He asks for his $6,000.

The manager isn’t thrilled. “That took 15 seconds. I want an itemized bill.”

So the engineer writes:

• 15 seconds of labor: $20.
• Knowing exactly where and how to hit the machine: $5,980.

This joke comes to mind when I think about the wave of AI projects launched over the past year and the way the industry is shaping up heading into 2026.

It’s been a breakout period for AI, not just in the number of companies being formed, but in how seriously the industry is now being taken.

We’ve seen new ventures launched by some of the most established names in technology (Bezos), sustained investment from Middle Eastern sovereign wealth funds (treating AI as long-term infrastructure rather than a speculative bet), and major venture firms like A16Z raising large, dedicated funds ($7B+) to back the next generation of AI-driven companies.

A lot of this AI energy is focused on innovation in the manufacturing industry. I’ve spoken publicly about the need for the U.S. and other Western economies to reindustrialize, so I’m pleased to see broader agreement on the what. Where I start to get concerned is the how.

Too many companies talk about “innovation” as though they can walk straight into a factory and do things better. It’s like thinking you can walk off the street for the Super Bowl and say, “Don’t worry Drake Maye, I’ve got it from here.”

They can’t — because manufacturing is hard.

This industry is about making complicated, physical objects at scale. The people who do this work are exceptionally skilled. You don’t wake up one day and decide you’re going to manufacture things well. And if you don’t respect the people who already know how to do it, you’re going to fail.

That’s the serious point behind the joke at the beginning. Manufacturing has human judgment at its core: knowledge built through experience, iteration, and failure over time.

In many ways, manufacturing looks less like pure science and more like an art form. It’s a lot like cooking.

Anyone can follow a recipe: “Crack two eggs. Add milk.” But then the recipe says, “Beat until silky.” What does silky actually mean? You don’t learn that from instructions, you learn it from doing.

Manufacturing works the same way. It’s not just about building systems; it’s about building the ability to observe, respond, and adjust: knowing when to intervene, and when not to.

Some of that is done through robots and software, and some of it is done by humans. The next phase of automation isn’t about stronger or faster machines, it’s about embedding judgment into the software that guides them.

Think of a musician warming up. You almost never see it live, and if you told someone to show up and watch a band do that, they’d think you were crazy.

Now think of Freddie Mercury warming up in front of 70,000 people at Wembley during Live Aid. That moment was legendary, not because it was rehearsed, but because it was responsive, instinctive, and completely in sync with the crowd.

That’s the difference between repetition and intelligence.

I believe a better way to approach innovation starts with two simple questions:

• What are you actually trying to do?
• And do you really need to do it?

That brings me to feedback loops.

Put a tray of muffins in the oven and they won’t all bake the same way. It depends on the shelf, the heat distribution, how full each cup is, and a dozen other variables.

Experienced cooks know their ovens, just like our engineer knew his machine.

Manufacturing works the same way. It’s not just about building systems. It’s about building the ability to observe, respond, and adjust: knowing when to intervene, and when not to.

The future of manufacturing isn’t fully automated factories run end-to-end by software. It’s a combination of baseline activities that can, and should, be automated, and a critical human layer that ties everything together.

Getting that balance right – between robots and people, automation and experience – is the real recipe for success.