Select Page

Automate Coverage Paused

I have been captured by the drama of the FIFA World Cup. Having the World Cup in my time zone, having been a soccer referee for more than 30 years, and unable to focus on automation, I’ve found myself sucked into Soccer/Football.

The games have been great. The players giving everything they have. The world’s top referees challenged to their limits.

Athletics at its finest.

(I am working on a long list of stories. Will get back to it this morning before today’s dose of dopamine.)

The Algorithm of Elon Musk

Given my interests about manufacturing, Lean, strategy, leadership—and dealing with strong bosses with mental health issues—I couldn’t resist reading a promo copy of a book covering all of those. And, recommending it. Go, get the book, study it with your team. It’s that good.

Set up with an appointment by Meta’s Sheryl Sandberg, Jon McNeill met Elon Musk who said to him, “I have a problem with Tesla. I can’t get Model X doors to align.” Two hours later, Jon McNeill and Elon Musk were tackling the problem head-on and soon, Jon would join Elon as President of Global Sales, Marketing, Delivery & Service at Tesla (2015-2018).

McNeill, now CEO of DVx Ventures, who sits on boards at GM and Lululemon, says most companies are about to discover the same bottleneck Tesla hit: not enough skilled workers to execute complex processes. He explores this in his new book, THE ALGORITHM: The Hypergrowth Formula That Transformed Tesla, Lululemon, General Motors, and SpaceX  (Portfolio; March 24, 2026). He details both the things he learned from Musk as well as the difficulties of working for someone with mental health issues.

The book is packed with stories showing (not telling) how leaders attack team problem solving with out-of-the-box thinking. 

Try this on for size. At Tesla, the body shop required scores of robots and a team of robotics engineers to keep them synchronized. It was expensive, complex, and fragile. Jon’s team questioned whether the entire body shop could be eliminated (almost three football fields of warehouse space). The answer: casting. By reducing the chassis from 300 welded parts to 3 cast parts, Tesla simultaneously cut manufacturing costs 50% AND made the process more resilient (fewer dependencies, fewer failure points) by questioning requirements.

Gathering my thoughts, what more perfect time to publish this review than immediately following the Initial Public Offering of SpaceX (which includes xAI and X-Twitter). The trillion-dollar valuation also propels Elon Musk into the stratosphere of finance making him humanity’s first trillionaire.

Musk takes big risks on audacious goals. Enough pay off big to make up for the failures.

  • The Algorithm? The five steps contain similarities to Lean and the Toyota Production System:
  •  Question requirements: Do we need this level of complexity? 
  • Delete steps: What can be eliminated before we relocate? 
  • Simplify: Can we redesign for fewer dependencies and lower skill requirements? 
  • Accelerate: What’s the constraint in a U.S. context? (Often: specialized labor) 
  • Automate last: Only after simplification should we consider technology (VERY IMPORTANT STEP TO NOT DO THIS FIRST!).

Here are a couple of relevant notes from the book:

Much of the genius in Musk’s companies comes from the legions of smart people empowered by the Algorithm. They’re chasing stretch goals with free license to question everything and innovate boldly.

The Algorithm features a set of best practices, yet one overarching idea infuses them all: Question the status quo.

AI and Programming: A Useful tool

I reflected recently on the changers in programming since my first experiences around 1977.

Everything back then was text based. You typed everything line-by-line. I started with BASIC and assembler. And also RPG on an IBM minicomputer. Went to C and C++ and then picked up Java in the early 90s.

Then I discovered integrated development environments (IDE), such as eclipse for Java. Then the IDE for C#. At that point, I was thinking, “this isn’t programming. There’s so much built in that you don’t even have to type.”

I try to forget the horrible experience of Ladder Diagram on a PLC.

(Oh, I should note that I was never a professional programmer. Fortunately, I had other roles.)

Lately, automation suppliers have been adding CoPilot to their programming interfaces.

Why this reflection on migration? I’ve been reading mass media and social media angst about the end of programmers with things like Vibe Coding and Claude Code.

Programming automation has been a constant for decades. They all served to make programmers better and faster and better able to tackle tougher problems.

Even with AI, someone must have the ideas of what needs to be developed, do the thinking about approaching the problem, and make the decisions for the best application.

We’re only going to see better applications solving harder problems. Those who lose their jobs will be those who cannot adapt.

New people? They will just think it’s the only way.

Click on the Follow button at the bottom of the page to subscribe to a weekly email update of posts. Click on the mail icon to subscribe to additional email thoughts.

Bridging the Design-to-Manufacturing Gap with AI-Driven Generative Engineering

I appreciate press releases about AI that include definite use cases rather than just the usual vague “we’ve got AI.” InfinitForm is a company new to me. It uses the popular Co-Pilot form of AI for its Generative Engineering Platform.

InfinitForm launched its Generative Engineering Platform, the next stage in the evolution of Design for Manufacturing (DFM). The Generative Engineering Platform is powered by the InfinitForm AI Co-Pilot to automate DFM analysis while optimizing for manufacturing processes, freeing engineers to focus on innovation rather than design iterations and reducing design cycles by 60-80%. 

Speaking from past harsh experience as a manager of product development, anything reducing engineering and design time getting us into manufacturing more quickly is a win.

The Generative Engineering Platform is a software-as-a-service (SaaS) platform that integrates with computer-aided design (CAD) workflows and uses artificial intelligence (AI) to optimize design for manufacturability. The Platform fosters a manufacturing-first approach that extends generative design beyond additive-only optimization, providing engineers and designers with automated analysis and intelligence tools to bridge the gap between design and production. 

Much of the PLM, CAD, and similar technologies on the cutting edge have moved into a variety of cloud-enabled applications. This fits the trend.

The Generative Engineering Platform automates design while optimizing for manufacturing processes, including CNC (computer numeric control) machining, die casting, injection molding, extrusion, additive, and hybrid manufacturing. Automated analysis accounts for multiple manufacturability variables, including wall thickness, draft angles, tool accessibility, tolerance stack-up, assembly complexity, and tooling feasibility. The Platform also analyzes the cost of manufacture and provides first-pass yield predictions. 

The InfinitForm AI Co-Pilot amplifies rather than replaces engineering expertise to accelerate decision-making, freeing design engineers to focus on innovation rather than manufacturability trade-offs. Using AI, the Generative Engineering Platform enables design engineers to explore more concepts with confidence that the results will be manufacturable.

The Platform also reduces the time required for handoffs to manufacturing engineers from weeks to days. Manufacturing engineers gain early visibility into design decisions that could affect manufacturing, eliminating surprises and reducing time-to-production. Using AI to ensure manufacturability also delivers a higher first-pass manufacturing yield.

The Generative Engineering Platform also features a Privacy-First Architecture to protect intellectual property. Customer designs are never used to train Platform algorithms, so proprietary data is always protected.

Click on the Follow button at the bottom of the page to subscribe to a weekly email update of posts. Click on the mail icon to subscribe to additional email thoughts.

Mentor In A Pocket

Articles about a worker shortage due to Boomer retirements have been a staple of trade magazine editorial ever since I became an editor in 1998. Some twenty-seven years later, those articles and news releases keep coming.

The concomitant problem is how to bring new people in. Apprenticeship programs went out the window with World War II. Businesses and manufacturers began expecting the education system to supply appropriately skilled workers. This was not going to happen despite education systems becoming increasingly industrialized. They taught basic math. Taught kids how to sit still and follow directions. Taught them to show up every day at the required location.

We need more.

It’s taken me some thought to place this new product from Derek Crager, Founder & CEO of Practical AI.

There is irony here, in that Crager touts himself as developer of an award-winning training program at Amazon—yes, the place that thinks it can replace its workers with robots. But, we will go beyond that thought for now.

I’ve not read the book, but he has also released a new book, Human First AI.

Crager says the real cost is downtime, rework, and attrition. He continues, It isn’t just a staffing problem—it’s an OEE problem. Every knowledge gap shows up in the metrics leaders actually manage: MTTR, FPY, scrap, rework, and yes, attrition. Ask any maintenance manager: the fastest way to lose a promising hire is to strand them without support on a tough job at 2 a.m. We send people to training, hand them SOPs, and hope they remember when it counts. But memory fails—especially under pressure.

His solution? Just-in-time guidance—the right step at the exact moment of need, while hands are on the task. When a technician can ask and do in the same breath, training becomes throughput. That’s the difference between teaching a concept and multiplying your best expert across every line and shift.

He called on his experience at Amazon to develop something called Pocket Mentor: A Phone Call to Your Best Expert. This is a hands-free, eyes-free mentor your team reaches by phone, anytime, on the floor or in the field. No app. No Wi-Fi. No passwords. Just tap & say, “Talk me through it” — and we will.

Here’s how it works:

  • Capture once. We sit with your best people and harvest SOPs, changeovers, fault trees, “what-if” branches, and tribal tricks—the real decision trees pros use when the line’s on fire.
  • Validate and govern. Content is approved by your SMEs and version-controlled with human-in-the-loop QA. Your source knowledge stays in a secure, governed box; people approve changes before they go live.
  • Guide in the flow of work. A tech calls in, we ask two clarifying questions (model, symptom), then deliver step-by-step voice guidance they can follow while working—hands-free — eyes-free.
  • Optional enterprise integrations. We can use your digital-twin/IoT signals today (enterprise integration) to pre-fill context—e.g., “Given Code 47 and 200 service hours, here’s the fastest fix; want me to talk you through it?”

He cites this pattern of stats.

  • Up to 80% faster onboarding—because new hires can “tap & say” from day one instead of waiting for a veteran.
  • ~30% reduction in downtime/rework—because the right step shows up at the right time, not after the post-mortem.
  • ~53% lower early attrition—because nobody wants to feel alone on the line; support drives retention.
  • 30× impact vs. traditional training—because we replace recall with real-time execution.
  • 0 extra staffing to scale coaching—your best employee effectively becomes 20 or 50 virtual coaches, every shift.

Most project managers agree that you should start with a specific pilot, prove the system, then scale it out. Crager offers a few suggestions.

  • Pick a chronic stopper. The two or three faults that always cause headaches (and overtime).
  • Harvest the fix. Sit down with your A-team and capture the real-world fix path—model variants, hard-won “gotchas,” and the restart checklist nobody remembers at 3 a.m.
  • Go live by phone. Give your night shift a number to call. Let them say, “Talk me through it.”
  • Measure MTTR for 30 days. Compare to your baseline. Then expand to changeovers, start-of-shift checks, and training-intensive stations.

Click on the Follow button at the bottom of the page to subscribe to a weekly email update of posts. Click on the mail icon to subscribe to additional email thoughts.

Time Management Thoughts

From Seth Godin (but I have used as many as possible over time).

Here are some proven ways to save hours of wasted time. You’re probably doing many of them, but they’re still treated as options by many. In rough order of importance:

  • Don’t invite someone to a meeting if an email or 1:1 conversation will do the job just as well.
  • Don’t fly if you can show up virtually and get the job done.
  • Instead of asking a group of people when a good time to meet might be, use a doodle.
  • Send a calendar invite when you book a time.
  • When you get stuck, first ask Claude, then ask a human.
  • Show up on time. Leave when the work is done.
  • Default to using shared docs (like Google docs) for any collaborative work.
  • For repeated tasks, make a checklist. Update it and share it as you go.
  • Respect synchronized time. If you can put it in a video instead of saying it live, please do.

Click on the Follow button at the bottom of the page to subscribe to a weekly email update of posts. Click on the mail icon to subscribe to additional email thoughts.

Follow this blog

Get a weekly email of all new posts.