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If You've Industrialized Bad Data, You Haven't Solved the Problem, You've Scaled It🎙️

You spent three decades on the operator side, leading global process control at Covestro, then overseeing 100+ discrete sites at Stanley Black & Decker, before joining APERIO. What’s a problem that was structurally invisible to you as a Fortune 500 SVP but became obvious from the startup seat? And conversely, what does the startup side consistently get wrong about how operators actually buy?

The thing that was structurally invisible to me as an SVP at Covestro or VP at Stanley Black & Decker wasn’t the technology; it was the cost of organizational inertia on every decision. When you’re inside a Fortune 500, you assume friction is normal. From the startup seat at Aperio, I suddenly saw it clearly: large operators aren’t slow because they’re unsophisticated. They’re slow because they’re accountable in ways startups never are. A bad call on a 16-site DCS rollout isn’t a pivot, it’s a safety event, a capital write-off, and a career moment all at once. One factor in my long-term success was claiming accountability for technical decisions. That relieved the workforce from the burden and let us move faster.

The flip side, startups consistently treat the technical champion as the buyer. They win the plant engineer’s enthusiasm, only to wonder why the deal stalls. The COO or SVP who controls the capital budget thinks in terms of risk reduction, not feature lists. Startups pitch OEE improvements and ROI calculators; operators are asking, “What happens to my site if this fails at 2 am and your three-person support team is asleep?” Getting that answer right, the risk story, the integration path, and the operational continuity commitment are what separate a pilot from a rollout. That’s where pilot purgatory lives.

OT owns the sensors, IT owns the cloud, and data science owns the models. Accountability is distributed, but someone still has to sign the contract. Who’s APERIO’s typical internal champion today, a plant-level OT lead, a corporate digital transformation VP, a CIO? And once they’re bought in, how do you align the other two seats at the table when each one measures success on a different KPI?

At Aperio, our most consistent internal champion was the end user, typically the OT data historian lead or unit engineer. Not the digital transformation VP, not the CIO. The person closest to the process, who understood the data and felt the pain of not having better tools. This tells you something important about where industrial AI adoption actually lives today: it’s being pulled from the shop floor up, not pushed from the boardroom down. My lifelong experience is that the person whose hands are closest to the process knows more about it than anyone else.

The alignment challenge across IT, OT, and data science remains unsolved industry-wide. OT cares about uptime. IT cares about security and governance. Data science cares about model performance. Those aren’t naturally compatible, and the contract signer is trying to satisfy all three simultaneously with a vendor who only speaks fluently to one of them. Layer on top of that a C-suite chasing the latest technology hype cycle, and you have budget and mandate flowing toward whatever got the keynote at Davos that year, not necessarily what the plant actually needs. When alignment works, it’s because someone with enough organizational authority makes it their problem to broker. When that person doesn’t exist, you get pilot purgatory from the inside. Extremely frustrating for everyone involved.

On a Tuesday afternoon at a plant running APERIO, what is the system actually doing differently from a traditional historian, and at which point in that chain does the error get caught: at the sensor, at the edge, inside the historian, or in the lake? What’s required from a change management perspective to ensure the error is captured, noted, and resolved before it reaches the model?

On a Tuesday afternoon at a plant running DataWise, the system is doing something a traditional historian wasn’t designed to do: actively evaluating OT data quality across the entire enterprise in near real-time. A historian records. DataWise watches.

A board operator has deep experience but finite attention. They’re tuned into a handful of critical tags. DataWise is evaluating anomalies enterprise-wide, from sensors to the cloud, including sensor failures, IT/OT network issues, and process deviations. The error gets flagged at the point it occurs, before it ever reaches the model.

That matters more than people realize. Models are only as good as the data they’re built on. DataWise flags bad data from the earliest stages of deployment, keeping the historian cleaner and ensuring bad data that does get through is identified and resolved rather than quietly corrupting model output over time.

The change management requirement is significant. The technology can flag the anomaly, but someone has to own the resolution. That means clear accountability for data quality, not just process performance, and building the habit of treating a DataWise flag as seriously as a process alarm. In most plants, that’s a cultural shift, not a technical one. And the honest reality is that many industrials simply don’t have the time or manpower to address every anomaly. The system can surface the problem. Only the organization can fix it.

The 80% AI failure rate is often cited, and “data quality” has become the convenient post-mortem. But pilots also fail because of the wrong scope, buyer, KPI, or business case. How much of what gets blamed on data is actually a buying and scoping problem dressed up as a data problem, and how do you know APERIO is solving the real bottleneck rather than the most visible one?

The 80% failure rate citation is real, but “data quality” has become a catch-all that lets everyone off the hook. Pilots fail for all the reasons you listed: wrong scope, wrong buyer, wrong KPI, and data quality is often the most visible symptom rather than the root cause.

That said, for industrials specifically, data quality is genuinely foundational. You cannot train a reliable predictive maintenance or process model on bad data. Industrials are sitting on millions of sensors, years of inconsistent tagging, and an IT infrastructure never designed with model training in mind. That enterprise data mess is real.

But most pilot failures I observed weren’t primarily data problems. They were buying and scoping problems wearing data’s clothes. The champion didn’t own maintenance or operations and couldn’t influence the work that needed to change when the model surfaced an issue. You’d have a model correctly flagging an asset anomaly, with no one authorized to act on it. Or what looked like a model failure was actually a bad sensor throwing the output sideways, and the organization had no process for telling the difference.

Scope is the other silent killer. If the pilot doesn’t connect to what leadership actually measures, it dies quietly regardless of technical performance. The data problem is real. The buying-and-scoping problem is just harder to admit.

Industrial foundation models trained on operational time-series data are starting to appear from the Siemens, GE, and Rockwell sides of the market. Does that future make a dedicated data trust layer less important, because models can learn around noise, or more importantly, because biased data at scale produces confidently wrong outputs across an entire fleet?

The “models can learn around noise” argument is mostly hype, and it’s a dangerous framing for industrials specifically. A foundation model trained on operational time-series data is still being trained on something. If that something includes years of bad sensor data, inconsistent tagging, and unresolved instrumentation failures, the model doesn’t learn around it. It learns from it. At fleet scale, biased data doesn’t produce uncertain outputs. It produces confidently wrong ones consistently across every asset the model touches.

But the more important point is entirely missed in the model conversation. In an industrial environment, a bad sensor can trigger a control action or a safety event. The stakes aren’t a wrong recommendation on a dashboard. They’re a process upset, an unplanned shutdown, or worse. Field instrumentation reliability and data trust aren’t model training problems; they’re operational integrity problems.

The Siemens, GE, and Rockwell foundation models are worth watching. But they don’t eliminate the need for a data trust layer; they make it more critical. You still need a human in the loop who can distinguish a genuine process anomaly from a failed transmitter. Foundation models don’t know the difference. They just scale whatever they’re given. If what you’re giving them is unreliable, you’ve industrialized the problem, not solved it.

To contact Jane, reach out to her on LinkedIn here. She’s always open to chatting and sharing valuable insights.

This newsletter is brought to you with support from our featured partners: AMT, IMTS, Jiga, Upkeep, and Industry 4.0 Club.

This newsletter is brought to you with support from our featured partners: AMT, IMTS, Jiga, Upkeep, and Industry 4.0 Club.

Never Let the Technology Precede the Problem🎙️

You’ve held leadership roles at IBM, AWS, and now Google Cloud, each during a different phase of industrial digitization. What’s the single biggest lesson each stop taught you that the previous one couldn’t?

It’s fascinating to reflect on how rapidly industrial digitization has evolved throughout my career. My experiences at these three companies each taught me a unique lesson about transformation, with one building directly on the next.

My time at IBM was a masterclass in the sheer scale and complexity of enterprise transformation. When I joined to lead technology strategy, the market was flooded with point solutions, early cloud, mobile, and social vendors, each pitching their specific technology as a hammer looking for a nail. At IBM, we focused instead on the concept of convergence benefits. This was about looking at the holistic problem and working backward from the desired business outcome. The big lesson there was that enterprise transformation requires understanding how people, processes, and technology must intricately weave together to achieve that final result.

If IBM taught me how enterprises think, AWS taught me how they can execute with agility. AWS radically challenged the traditional approach of bringing in big teams to tackle a project all at once. Instead, they pioneered the “two-pizza team” philosophy—the idea that an effective project team should be small enough to be fed by just two pizzas. By keeping teams tight, you minimize lines of communication and eliminate the bureaucratic overhead that slows down decision-making. This emphasis on speed, nimbleness, and constant experimentation for the customer is exactly what fueled AWS’s explosive growth. By focusing on highly targeted, small-scale pilots that could be scaled rapidly, we proved that you don’t have to sacrifice velocity to solve enterprise-grade problems.

Now at Google Cloud, the lesson has been all about intelligence and the true power of data. As a data and AI company, our core mission is to organize the world’s data and make it universally useful, a mission that translates incredibly well to the manufacturing industry, which sits on a goldmine of both IT and OT data. In that data resides the narrative of everything happening within a company, why it’s happening, and what you can do about it. Google has made me realize that the ultimate phase of digitization isn’t just about understanding the enterprise blueprint or moving fast. It’s about listening to what the data is telling you, uncovering actionable insights, and making the entire enterprise fundamentally smarter.

One thread that runs through all of those roles is the IT/OT divide, something you’ve called the “enemy of profits.” After years of the industry talking about convergence, why is that wall still standing, and what does Google Cloud’s Manufacturing Data Engine do differently to tear it down?

The IT/OT wall still persists due to the inherent complexity of manufacturing. Unlike retail, for example, which only has to manage buying and selling, manufacturing operates on three distinct imperatives: first, you have to invent a great product, then you have to make it faster, cheaper, and better than the competition, and finally, you have to navigate a complex supply chain and logistics to sell it to the right people. This involves a massive, fragmented web of people, processes, and machines. Historically, the industry managed this complexity by using traditional standards like ISA-95, which explicitly separated shop-floor OT from corporate IT systems. Because these systems speak entirely different languages, the divide became deeply structural.

While that divide is starting to blur, Industry 5.0 and AI agents are completely changing the game. To an AI agent, data is just data; it doesn’t care whether information originates on the shop floor or in the cloud. The real challenge is the heavy lifting required to connect those machines, extract their data, and harmonize it. True harmonization means building a digital thread that can instantly tell you which customer ordered a product, which plant made it, during which shift, and on which specific machine.

This is exactly where Google Cloud’s Manufacturing Data Engine and Manufacturing Connect come in. We can seamlessly connect to more than 250 OT data sources, such as PLCs, HMIs, and SCADA systems, and automatically integrate that machine data with your IT systems of record, such as CRM or PLM platforms. By bridging these two worlds into a unified data foundation, we break down historical walls and give companies the single thread they need to build a truly intelligent enterprise.

A key piece of that convergence story is the Cortex Framework, designed to bridge heavy ERP data and SAP environments with real-time analytics. For a mid-market manufacturer running on legacy systems, what’s the honest timeline and investment to get from where they are today to a functioning “digital thread”? How should leaders benchmark whether the framework is driving measurable ROI?

To understand the honest timeline and investment required, you first have to look at how long this process traditionally takes. In manufacturing, you are constantly dealing with a “museum of machines”–hundreds of protocols and assets that are 30 to 50 years old and were never designed to talk to each other. Traditionally, a company would spend six to eight months just on data extraction, stretching the total project timeline out to 14 months. It’s a slow, frustrating process because if you ask five different legacy systems for today’s production numbers, you will get five different answers. They all have their own metadata and versions of truth.

Google Cloud Cortex Framework and our Industry 5.0 architecture fundamentally change this dynamic by acting as a translation layer. Cortex provides pre-built data models for complex environments like SAP, allowing you to connect legacy systems and extract data in days or weeks, rather than months. We sit on top of everything using a Unified Name space (UNS) with a pub/sub model, meaning all your legacy systems subscribe to a single source of truth rather than creating their own. By getting this data layer right, we compress the 14-month timeline into weeks. In fact, we’ve seen customers deploy more than 800 AI agents into production in under 10 months because their data foundation was finally seamless.

When it comes to benchmarking ROI, leaders shouldn’t view this as a multi-year gamble. Our global survey of over 500 manufacturing executives shows that generative AI is already delivering sizable returns, with 75% of leaders reporting immediate improvements in productivity. The most successful leaders are achieving these gains by following a “buy to integrate” philosophy. They don’t try to build everything from scratch, nor do they buy rigid, closed systems. They let Google Cloud handle the heavy data lifting, freeing them up to focus their time and budget on the specific use cases that drive real productivity and growth.

Even with the right data infrastructure in place, none of it matters without people to act on it. With 30% of manufacturing jobs going unfilled and 1.9 million projected vacancies by 2033, that’s a real problem. You’ve framed AI as a tool to capture retiring workers’ expertise and deliver it to new hires. How do you actually extract decades of tacit knowledge from a seasoned machinist and encode it in a way an AI agent can deliver reliably?

The manufacturing talent gap is undeniable, but the nature of the workforce is also undergoing a massive shift. The traditional image of a factory worker carrying wrenches is giving way to a new reality of “technocrats” who navigate the shop floor with tablets, configuring machines remotely and using computer vision to diagnose issues. Because of this shift, we can’t bridge the talent gap using old-school methods. If you ask a veteran worker to write down everything they’ve learned over 40 years, the project will fail miserably. As humans, we have decades of experience, which makes us excellent at catching mistakes but not necessarily at documenting them.

AI can play a profound role in capturing that hidden expertise by letting veteran workers validate information rather than author it from scratch. Instead of writing a massive manual, an expert can simply be recorded performing a complex repair. AI can instantly translate that footage into a step-by-step instruction guide or a synthetic training video. The veteran worker simply reviews and approves it, acting as the expert validator behind the AI.

With that verified data foundation in place, companies can deploy AR and VR tools to guide new hires through complex tasks in real time. The AI walks the technician through the process step by step, using automated validation to confirm each task is completed correctly before allowing them to proceed. Ultimately, this isn’t just about preserving legacy knowledge; it’s about dramatically compressing onboarding timelines and empowering companies to help generalist workers confidently perform highly specialized tasks.

You’ve spent two decades watching industrial tech waves: IoT, blockchain for supply chain, digital twins, and now agentic AI. Which of those earlier bets do you think the industry over-invested in, and what lesson from those experiences should manufacturers bring to agents?

Rather than a case of simple financial over-investment, it’s more useful to look at where the industry chased hype that ultimately under-delivered. From that list, blockchain and digital twins are the two clear examples where I believe the technology was applied to the wrong problems.

With blockchain, the technology was heavily hyped because people saw its success in crypto and started treating it like a hammer in search of a nail. They tried to force it into standard transactional supply chain problems without realizing it was built to solve specific multi-party trust issues, not to handle real-time, compute-heavy enterprise tracking. Similarly, early digital twins lacked a clear, unified definition. Many companies poured resources into building complex 3D CAD models of their shop floors just to have a visual representation on a screen, without tying those models to a specific operational problem or a measurable ROI.

The lesson for the era of agentic AI is clear: never let the technology precede the problem. Those earlier tech waves stalled because companies invested in the tool first and the use case second. With AI agents, the focus has to remain entirely on the business outcome. Leaders shouldn’t deploy an agent just because the capability exists; they need to identify a specific bottleneck and build the data foundation to solve that precise issue. When you work backward from the problem rather than chasing the tool’s hype, you ensure your technology delivers immediate value while protecting your transformation from reputational risk.

To contact Praveen, reach out to him on LinkedIn here. He’s always open to chatting and sharing valuable insights.

This newsletter is brought to you with support from our featured partners: AMT, IMTS, Jiga, Upkeep, and Industry 4.0 Club.

This newsletter is brought to you with support from our featured partners: AMT, IMTS, Jiga, Upkeep, and Industry 4.0 Club.

On Autodesk + MaintainX 🗞️🔬 📚

This past week, Autodesk announced an agreement to acquire MaintainX, a developer of maintenance and operations software, for approximately $3.6 billion. It is an impressive outcome for Chris, Nick, and the MaintainX team, a credit to what they have built over the past few years, and to the manufacturing software ecosystem more broadly.

The deal is funded with roughly $1.6 billion on hand and $2 billion of new debt. It is the largest acquisition in Autodesk’s history, with the previous high being the $875 million paid for PlanGrid in 2018. This is a major strategic land grab, and it signals Autodesk’s conviction in its expansion from design and make into operations.

Two things stand out. The first is around the deal structure. In a category where five-year revenue multiples for engineering and manufacturing technology sit around 11.9x and where even the five-year peak for broader SaaS tops out near 26.0x, an implied ~27x forward revenue multiple feels excessive. Furthermore, $2 billion of new debt is not free money. At today’s rates, the interest bill alone sets a bar that the cross-sell into Autodesk’s installed base has to clear before any of this counts as a good use of capital.

This raises questions about who was driving this liquidity event, especially given that the company raised a $150M round at $2.5B barely a year ago and is now flipping at $3.6B. It echoes the peak SaaS exuberance of 2020–21, an era of near-zero rates that, in my view, bears little resemblance to today’s environment. In fairness, a category leader growing by more than 50% is a strong asset, especially if there is a clear strategic fit. That, however, brings me to my second point.

On strategic fit, I am not sure I follow, at least as it pertains to the manufacturing sector. The logic of acquiring real-time telemetry (asset failures, work orders, maintenance patterns) is clear enough, particularly given that operations and maintenance command 70–80% of an asset’s total lifetime cost. What is less clear is how that operational data feeds back into Autodesk’s design and engineering functions or into the generative design AI that Autodesk sells. To close the design-to-manufacturing loop, an MES, QMS, or OEE platform would, in my view, have been the more natural fit. It is worth noting that on the construction and facilities side, the strategic fit is stronger, and I see greater alignment.

For all that, this is good news for the manufacturing software ecosystem, especially after years in which venture and private capital have poured into the sector. Companies like Tractian, Squint, and Augury should only benefit, as all three just watched their category get repriced. As Autodesk further expands into the operations category, it will be interesting to see who moves next.

Some Interesting Reads:

This came out a few weeks back, but McKinsey calculates that building the capacity to produce products and their upstream inputs in the US could cost on the order of $2 trillion. The analysis is fantastic, providing insight into the ramp-up needs, a capacity and utilization sensitivity analysis on import volumes, and job and labor requirements. [McKinsey]

• An awesome breakdown of the functional taxonomy of World Models from Fei Fei Lei [X]

• Honeywell’s Aerospace spinoff plans to generate at least $6.5 billion in earnings and $4 billion in cash flow by 2030 [Honeywell]

• A breakdown of the impact the manufacturing and robotics community has had on Columbus, Ohio [NYTimes]

• BCG with its vision for the Factory of the Future [BCG]

• A great analysis from the FT, echoed by Axios and Fortune this week. Manufacturing investment is sputtering, as corporate commitments to build factories fail to translate into construction. Furthermore, an interesting quote from Katie Farmer, CEO of BNSF, states that she has “certainly seen a revival” in “certain commodity areas”, including steel, but that “the flip side of that is that in the other industrial segments, we’ve really seen a plateau”. [FT]

• GE Aerospace invests another $1B in the US [GE]

• A high-level analysis on the state of process manufacturing [IoT Analytics]

• An interesting assessment of the structural advantages of the modern robotics systems integrator [Avi Zurlo]

• Energize with another Industrial AI Market Map [Energize Capital]

Sponsored by Jiga

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Products & Research:

Protos is an AI-native co-engineering workspace built by Aris Machina (Sweden) for teams designing physical systems from first principles, batteries, pharma, humanoids, advanced materials, optics, and much more. It collapses the coordination tax across data, simulation, and domain knowledge, becoming the R&D operating system for deep tech. Check out more here at the Protos Homepage [Aris Machina]

• Siemens announced the Intelligence Center X, combining Mendix with Siemens’ Graph and AI Studio to help customers build applications and deploy agents with operational context. [Siemens]

• A pretty awesome breakdown of WestMag, one of the coolest newish startups building the American Actuation Supply Chain by both and . [WestMag]

  Core Memory

  This Is Why America Can’t Have Robots And Other Nice Things

  At a hardware happy hour in the Mission — I dragged our social media editor Armaan along, half-expecting to be the only woman there — I met a different kind of tech bro. You know the usual kind: youn…

  Read more

  18 days ago · 119 likes · 6 comments · Kylie Robison
  Not Boring by Packy McCormick

  America Spins on Westmag

  Welcome to the 730 newly Not Boring people who have joined us since our last essay! Join 268,518 smart, curious folks by subscribing here…

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  18 days ago · 129 likes · 2 comments · Packy McCormick

• NVIDIA announced Cosmos3, an open Omni-model for Physical AI Reasoning and Action [Hugging Face]

• Generalist announces $400 million in new funding [Generalist]

• CoLab announces Operator to analyze, search, generate, and automate tasks across your engineering data [CoLab]

• Intrinsic launches its own Vision Model and 3D-Object Perception Transformer [Intrinsic]

• AWS launches Kiro, an AI-powered IDE that accelerates iteration across provisioning, policy training, and simulation validation with full contextual awareness of the Physical AI lifecycle. [AWS]

Financing & Transactions 💵

Agile Robots plans to raise $800M from Softbank and others
Robotics / Physical AI • Funding • Europe

Generalist raised $400M from Radical Ventures, 8VC, USV, and Norwest
Robotics / Physical AI • Funding • USA

Mecka raised $60M from Framework Ventures
Robotics / Physical AI • Funding • USA

Endra raised $50M from a16z and Notion Capital
Industrial AI and MEP • Funding • Europe

Layup Parts raised $42M from MarlinSpike
Composites • Series A • USA

Invisix raised €20M from Hitachi Ventures
Semiconductors • Seed • Europe

Planned Downtime 🧑‍🔧

A Great Lecture with Base Co-Founder Justin Lopas

The 20 Greatest Soccer Players of All Time

Breaking the Bottleneck is brought to you with support from our partners.

• AMT & IMTS - The Association for Manufacturing Technology represents and promotes U.S.-based manufacturing technology and hosts the largest manufacturing technology trade show in North America, IMTS.

• Jiga - Is a platform that connects hardware teams with vetted manufacturers for custom parts. Instead of black-box instant quotes, you see exactly who’s making your parts and can talk to them directly.

• Upkeep - CMMS Software helping maintenance teams manage work orders, assets, and preventive tasks efficiently from any device.

• Industry 4.0 Club - Leading cross-functional group accelerating the adoption of Industry 4.0, delivering better experiences to consumers, better profits to manufacturers, and better jobs to factory workers.

• Ironloop - Ironloop is a modern OT/ICS software platform that delivers configuration-level intelligence, giving manufacturers ground-truth security visibility and compliance confidence across air-gapped networks.

• T51 - T51 is an AI-native planning, scheduling, and optimization platform that integrates securely with your existing ERP, MES, and data stack.

This newsletter is brought to you with support from our featured partners: AMT, IMTS, Jiga, Upkeep, and Industry 4.0 Club.

AI Is Accelerating Design Faster Than We Can Validate It

You spent years at PARC and Carbon doing deep research on accessibility constraints for multi-axis machining and lattice design before founding C-Infinity. What was the specific moment or failure mode you kept seeing in the field that convinced you the problem had to be solved from scratch, rather than fixed inside an existing platform?

For fifteen years, I watched the same failure mode repeat from the outside. At PARC and Carbon, I was often sitting at the boundary between advanced process capability and the engineering organizations trying to use it. I watched Fortune 500 manufacturers, aerospace primes, medical device companies, and consumer hardware teams hit the same wall: a design could be complete in CAD and still be nowhere close to production-ready.

The specific domains changed from multi-axis machining, lattice design, metal additive manufacturing, and assembly, but the underlying bottleneck was the same. Function-oriented product data had to be transformed into manufacturing-ready process intelligence, and that transformation was still done almost entirely by expert humans. CAD models are authored by design engineers with the implicit assumption that the next consumer is another human: a manufacturing engineer, process planner, quality engineer, or supplier. That works when timelines are forgiving. It does not scale when companies are under pressure to accelerate time to market and respond quickly to change.

That is the missing computing layer we started C-Infinity to build. Existing platforms were not designed for it. CAD defines the product, PLM manages the product record, MES executes the production workflow, but the reasoning layer that turns product definition into a feasible manufacturing plan has largely been missing. If AI is going to meaningfully accelerate time to market, it cannot assume engineering data was created for automation. It has to work with human-oriented engineering data while reasoning over geometry, physics, motion, constraints, and manufacturing process logic.

We started with assembly because nearly every advanced product is assembled, and assembly planning exposes the core challenge clearly: translating product structure into process structure. It is a combinatorial, geometry-rich, physics-constrained problem embedded in a complex enterprise workflow. Solving it requires a purpose-built foundation.

AutoAssembler treats the EBOM and MBOM as dual representations of the same configuration space rather than two separate handoffs. In practice, where does that model break down first is it change management across variants, geometry validation, or organizational resistance to collapsing two workflows into one?

The honest answer is variants under continuous change, but it is worth being precise about what “dual representation” actually covers.

The EBOM and MBOM share a part structure, and at that layer they can be treated as projections of a common configuration space. AutoAssembler uses that fact to reason from the EBOM to the assembly sequence. But a full MBOM is more than its part structure. It also carries process content: tools, fixtures, consumables, work centers, sequence, and process logic that have no direct analog in the EBOM.

So the projection holds for parts. It does not hold for the MBOM as a whole.

The model starts to strain first under variant proliferation. A single product family can generate thousands of MBOM permutations driven by configurability (regions, customer options, supplier substitutions, etc). Each variant inherits much of the assembly logic of its siblings but diverges in specific places. The hard part is tracking which planning decisions are shared across the family and which are variant-specific.

The second pressure is continuous change. Every ECO forces a recomputation: what changed in the EBOM, which parts of the existing MBOM and assembly plan remain valid, what has to be re-planned, and what can be reused. When a change touches a part that participates in multiple variants, the two pressures compound. You are re-validating the projection across a family, not a single product.

Geometry validation is a real failure mode, but it is usually more well-defined, e.g. this fastener cannot be reached, this sub-assembly cannot be sequenced as drawn. Those problems become much harder when variants and ECOs are also in motion, because the system has to know which configuration and which version of the plan it is validating.

Organizational resistance is real, but in our experience it tends to recede when the model holds up under variants and change. When it does not, no amount of organizational alignment fixes the underlying problem.

So the model breaks down first where the projection is most stressed: high-mix products under continuous engineering change. That is why EBOM-to-MBOM translation stops being a data-management problem and becomes a manufacturing-reasoning problem.

When you’re selling into an OEM with an entrenched PLM stack, who do you actually close first: the manufacturing engineer, the manager, or the VP of Engineering running the program? What is the initial product wedge?

The wedge is the ECO (Engineering Change Order) process.

That is where the pain is immediate, measurable, and already tied to the customer’s existing PLM workflow. OEMs process thousands of engineering changes, and every change creates downstream manufacturing questions: what changed, which process steps are still valid, what has to be re-planned, what work instructions need to be updated, and what risks have been introduced?

The manufacturing engineer is usually the primary user because they feel that pain directly. They are the ones working through the backlog: interpreting design changes, assessing assembly impact, reusing what they can from the existing plan, and manually updating instructions.

AutoAssembler helps them do that work faster and more systematically. We identify what changed, determine which planning logic can be reused, generate the missing portions of the assembly plan, and produce updated work instructions.

That makes ECO a strong initial wedge because it is not an abstract AI use case. It is a concrete operational bottleneck with direct impact on manufacturing readiness, engineering throughput, and program execution.

Over time, the goal is to shift this intelligence left. The same manufacturing reasoning that helps process planners evaluate ECOs should also become available to design engineers before changes are pushed downstream. That allows them to measure the manufacturing impact of engineering changes earlier, when the cost of iteration is lower, and the design is still flexible.

The economic buyer is usually higher up: a VP of Engineering, VP of Manufacturing, or digital transformation leader. They care about throughput, change velocity, program delays, and the cost of engineering rework.

How much of AutoAssembler’s architecture today is model-based versus statistical, and where do you expect that balance to shift as the product matures? To ensure seamless adoption, what’s your philosophy on integration depth versus breadth, and which platform are you targeting next?

AutoAssembler uses a neuro-symbolic architecture: model-based and deterministic at the core, with machine learning applied where it creates leverage.

Manufacturing planning is not a pure prediction problem. A system cannot simply infer a process plan statistically and treat it as correct. It has to reason explicitly over geometry, motion, contacts, constraints, accessibility, sequencing, and feasibility. That requires a deterministic planning foundation.

Machine learning becomes powerful around that foundation. It can learn customer-specific preferences, recurring planning patterns, reuse decisions, and organization-specific process logic. Over time, the system becomes more informed by each customer’s environment while preserving deterministic reasoning where correctness matters.

That architecture also shapes our deployment model. We deploy inside the customer’s VPC and do not cross-train across customers. Each customer’s system is tuned to its own products, processes, and know-how — and as the system learns over time, that institutional knowledge compounds inside their environment rather than leaking into a shared model. The customer’s manufacturing know-how stays with them, and the system gets more valuable to them with every deployment, ECO, and planning decision.

On integrations, we are API-first. We support all major CAD platforms today, and we design our endpoints to connect with critical workflow triggers: ECOs, design releases, variant updates, and manufacturing-readiness checks.

That focuses our integration strategy. We prioritize workflow depth over connector count. We integrate where decisions happen and where automation can remove friction from the workflow. The goal is for AutoAssembler to operate as an event-driven manufacturing reasoning layer across the customer’s enterprise stack.

In practice, many customers operate mixed CAD and PLM environments, which makes interoperability essential. Our tightest integrations today are within the PTC ecosystem. We’re deliberate about not pre-announcing the next platform; we let customers pull, and commitments drive that sequencing rather than picking targets in advance. The goal is to meet customers where they already work while giving them a reasoning layer that connects product definition, manufacturing planning, and production readiness.

Most people in the CAD/PLM world frame the opportunity as “AI will accelerate design.” Your thesis seems to be the opposite, AI is already accelerating design faster than humans can validate it, and the real bottleneck is now verification. Who in the industry do you think is most wrong about where the value actually gets created?

The design-acceleration thesis is not wrong. It is incomplete.

As design generation and physics simulation get cheaper, validation across the stack becomes the scarce resource. AI will help engineers generate more concepts, explore larger design spaces, and run simulations faster — that part is real. But the harder question is no longer whether AI can generate more candidate designs. It is which of those designs can actually be manufactured, assembled, changed, inspected, sourced, and scaled in the real world.

Manufacturing has always had this asymmetry: process capabilities often advance faster than our ability to design for them. The manufacturing stack can do more than the surrounding engineering and planning workflows can reliably support. AI-assisted design accelerates one side of that asymmetry without addressing the other.

So the larger opportunity is not pushing more designs downstream into manufacturing. It is pushing manufacturing intelligence upstream into design, so engineers can make targeted decisions that minimize feedback loops before production. The systems that create the most value will not simply generate more options. They will help engineers generate options that already understand manufacturing feasibility, assembly logic, change impact, and production readiness.

The next bottleneck is not imagining more designs. It is proving which ones can become real products.

To contact Sai, reach out to him on LinkedIn here. He’s always open to chatting and sharing valuable insights.

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Diagon: How a Simple Spreadsheet Became the World's Most Comprehensive Equipment Marketplace 🎙️

You were one of the first formal buyers Tesla hired specifically to source factory machinery. What did “professionalizing” that role actually look like during Model 3 production hell? And how has that experience, coupled with your work at Astra and Bright Machines, driven the early vision for Diagon?

I actually started this role in 2013, during the Model S ramp and the design and build of the Model X production lines, which is a really interesting place to begin the story.

When I joined, my job was to intake requirements for every equipment project across the company. The first thing I needed was an organized system, a single dashboard where every project’s requirements were documented. From there, I began identifying qualified suppliers, which meant building spreadsheets and Confluence pages cataloging every equipment manufacturer that sold the machines and systems we needed. Then came standardizing how we negotiated contracts and got spending under terms. When I started, very few projects were under any kind of general T&Cs. By the time I left, nearly all of them were.

Diagon started as a spreadsheet! That spreadsheet of manufacturers and machines became the company's single source of truth. Something special happened along the way. Engineers were constantly coming to me for supplier recommendations, and I could quickly answer questions about which companies made which machines, what they typically cost, what the lead times looked like, and what the specs were. The same dataset eventually tracked warranties, financing options, and other supplier details. Engineers, the finance team, and even the CFO used it to build forecasts and cost models for future factory projects.

That idea has stuck with me. At Diagon, we’re building the largest open dataset of manufacturing equipment ever attempted, and we’re layering AI on top of it to create the most powerful equipment sourcing tools available anywhere.

Albus is designed to give a single SME engineer the procurement sophistication of a Tesla or Boeing sourcing team. But those teams carry decades of tribal knowledge. How does an AI agent replicate supplier judgment, not just supplier search?

We’ve built a fully autonomous equipment sourcing agent named Albus, and a few things give him real judgment, not just search.

First, he has access to our proprietary dataset of equipment and suppliers, so he simply knows more about the industry than the average equipment buyer. Second, because he’s autonomous and connected to platforms like LinkedIn, Practical Machinist, Reddit, and other forums, Albus learns from many experts and SMEs at once. A typical SME only has the experience they’ve personally lived through. Albus can synthesize across thousands of practitioners to make far better-educated predictions about the right machines for a given application. Third, he has access to a deep pool of used and pre-owned equipment, which helps him surface genuinely great financial deals for customers.

The “One Big Beautiful Bill” offers 100% immediate write-offs on equipment purchased after January 2025 and 35% tax credits for semiconductor gear. Are manufacturers actually pulling purchases forward in response, or is the industry too slow to capitalize?

My honest read is that companies are treating this as an accounting benefit. It helps clean up financial statements and improves how their position shows on paper. But at the end of the day, actual financing dollars are what drive purchase decisions. So the write-offs have only helped the industry moderately. The real movers are venture capital, private equity, and debt financing. As that financing has become more available, that's been the major driver of purchasing activity I'm seeing.

You’ve said the real bottleneck in industrial software is culture, not technology, and that Diagon focuses on building software “frontline workers actually want.” What does the onboarding conversation sound like with a skeptical plant manager who’s been buying machines the same way for 20 years? And once they’ve made a purchase, how do you keep them engaged on the platform?

Everybody hates new tools. Nobody uses Diagon because they want an equipment sourcing platform. They use Diagon because they need a CNC machine or a laser cutter. The platform is a means to an end. It’s one of the fastest ways to go from a concept of what you want to source to a list of viable machine options and live listings.

For skeptical plant managers, what’s worked best is just doing a demo, especially a live sourcing session. If they were recently in the market for, say, a waterjet cutter, walking them through that flow is the best pitch we have. Diagon is free for buyers, so there’s really no risk in trying it. They get the accuracy of going directly to manufacturer websites, combined with the flexibility of an AI platform like Claude. Albus (Diagon’s CAPEX Sourcing Agent) invites them to collaborate on the Diagon platform and evaluate the options that best suit their needs.

Five years out, does Diagon look more like an industrial marketplace, a procurement operating system, or a financing platform, or is the thesis that those three things are inseparable? How will this vision change the workflows and operating model for your customers?

Honestly, none of the above. Five years out, I think Diagon becomes an agent-friendly equipment information API, and the mode of interaction looks much more like that of a trusted advisor or even a colleague. Customers will move seamlessly from identifying the machines they’re looking for (which feels more like a procurement operating system), to finding what’s actually available on the market (the marketplace piece), to lining up financing and shipping. It will be one of the most powerful tools any manufacturer can have on the floor.

Today, we’ve built Diagon to fit within the existing process: engineers define specs, a procurement leader identifies vendors, an equipment buyer reaches out for quotes, and then compares, negotiates, and selects. In the future, I think this looks much more like iterative design. Engineers will be far better informed upfront on the machine options available, which means smarter decisions on equipment specs and even product design, and ultimately less capex spend. Equipment buyers will come to see Diagon as the place where the work currently happening in inboxes and on phone calls actually gets executed.

To contact Will, reach out to him on LinkedIn here. He’s always open to chatting and sharing valuable insights.

This newsletter is brought to you with support from our featured partners: AMT, IMTS, Jiga, Upkeep, and Industry 4.0 Club.

This newsletter is brought to you with support from our featured partners: AMT, IMTS, Jiga, Upkeep, and Industry 4.0 Club.

A Design and Simulation Market Map 🗞️🔬 📚

Enjoyed building this with Michael Finocchiaro at Threadmoat. This is the start of a new series featuring market maps of engineering and manufacturing startups shaping the landscape today. For more analysis of this ecosystem and more on each of these startups, check out ThreadMoat and visit Fino’s profile! As always, let us know if we’re missing anyone!

Some Interesting Reads:

Keychain surveyed 1,000 U.S.-based CPG manufacturers, with 52% planning to add new production lines and 25% planning to open new facilities. In terms of AI adoption, 12.3% have already deployed it, 18.4% are piloting it, and 22.1% are planning to implement it.

• Cerebras, which went public this week, warned that the US Chip manufacturing rebuild could take up to 15 years [WSJ]

• An interesting breakdown and analysis from Joris Feels on the AI CAD tools best suited for 3D Printing. [3DPrint]

• Fanuc announced a partnership with Google, unsurprisingly shares surged [Bloomberg]

• Atlas shared the Q1 2026 State of Clean Energy Manufacturing report. Surprised by the significant drop in battery investment given the energy bottleneck, though unsurprised by the cancellation of 48,000 announced manufacturing jobs. [Atlas Public Policy]

• KPMG released its industrial manufacturing tech report. 48% plan to significantly increase cybersecurity investment, and 70% are taking a centralized approach, with IT leading AI implementation. I’m personally skeptical of this approach, AI’s value should be democratized to business owners. [KPMG]

  

• Emerging approach? Stellantis and Ford announce partnerships with Chinese carmakers to survive in a European market squeezed by high costs and weak demand [FT]

• China’s BYD leads in silicon-carbon battery development, a technology that the US invented, and companies like Sila Nanotechnologies have been working on for years. A great breakdown of another technology developed by the West, and productionized by China. [The Information]

• An AWS solution breakdown for capturing multi-modal embeddings from manufacturing-specific data, such as engineering diagrams, CAD drawings, inspection photographs, thermal analysis plots, and fatigue curves. [AWS]

• a16z shared that 20% of manufacturing firms are using agents [a16z]

• BCG’s CEO Guide to Physical AI [BCG]

Products & Research:

Figure’s livestream drew 1.5 million views on X over its first eight hours. While exciting, I’d be curious to see how this performed relative to the first generation of warehouse automation, and it looks like the Agility CEO agrees. Also interesting that the robot didn’t optimize for the barcode being up on a lot of the parcels. [Figure]

• Config launches building the data layer for robotics foundation models [TechCrunch]

• Alcon case study, with HighByte serving as the foundation for predictive maintenance and defect-monitoring use cases. [HighByte]

• Siemens and Xometry announced a $50 million partnership, including the integration of Supplyframe, which provides design-to-source information for electronic components, and Thomas’s sourcing data. [Engineering.com]

• Mind Robotics raises another $400M for Manufacturing robots [Robot Report]

• Microsoft updated MatterSim, a multi-task foundation model for in silico materials characterization [Microsoft]

• Chef Robotics now automates secondary packaging and kitting automation for CPG manufacturers and customers. [Chef Robotics]

• Perceptron released MK1, a purpose-built system for video understanding and embodied reasoning

  

Finance & Transactions 💵

Anduril raised another $5B from Thrive Capital and Andreessen Horowitz
Aerospace & Defense • Series H • USA

Mind Robotics raised $400M from Kleiner Perkins, Meritech, and Redpoint
Robotics / Physical AI • Funding • USA

RangerAI raised $8.4M from Bonfire Ventures and 25madison
Industrial AI • Series B • Canada

Planned Downtime 🧑‍🔧

A24 and Bourdain, Sign Me Up

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• AMT & IMTS - The Association for Manufacturing Technology represents and promotes U.S.-based manufacturing technology and hosts the largest manufacturing technology trade show in North America, IMTS.

• Jiga - Is a platform that connects hardware teams with vetted manufacturers for custom parts. Instead of black-box instant quotes, you see exactly who’s making your parts and can talk to them directly.

• Upkeep - CMMS Software helping maintenance teams manage work orders, assets, and preventive tasks efficiently from any device.

• Industry 4.0 Club - Leading cross-functional group accelerating the adoption of Industry 4.0, delivering better experiences to consumers, better profits to manufacturers, and better jobs to factory workers.

• Ironloop - Ironloop is a modern OT/ICS software platform that delivers configuration-level intelligence, giving manufacturers ground-truth security visibility and compliance confidence across air-gapped networks.

• T51 - T51 is an AI-native planning, scheduling, and optimization platform that integrates securely with your existing ERP, MES, and data stack.

This newsletter is brought to you with support from our featured partners: AMT, IMTS, Jiga, Upkeep, and Industry 4.0 Club.

Scaling AI Agents Is Less About Building More Agents and More About Designing the System Around Them

You went from programming systems on factory floors to P&L accountability at Honeywell, then led global business units at Rockwell. Now you’re at a hyperscaler selling into the same customers you used to serve. How does sitting on the Microsoft side change the way you think about what manufacturers actually need versus what they’ll buy?

What changed for me isn’t the definition of “need,” but the scale of what’s possible when you can connect data, AI, and workflows across the entire value chain. On factory floors, “need” is still the same: safer operations, better quality, more throughput, and lower cost. If a solution can’t move one of those needles, it’s noise. Now sitting on the Microsoft side, I’m even more passionate about practicality.

Manufacturers don’t buy AI to check a box; they invest in outcomes they can measure in months, not years. And it has to show up in the way people actually work: the maintenance workflow, the scheduler’s daily decisions, the quality engineer’s root-cause loop—not another dashboard. What excites me now is being able to bring that together on a single platform, connecting data, AI, and workflows across the entire value chain in a way that wasn’t possible in fragmented systems.

Microsoft has deployed over 25 AI agents inside its own logistics operations, with a target of more than 100 by year-end. What has that internal experiment taught you about scaling agents that you wouldn’t have learned from customer deployments alone?

The biggest lesson is that scaling agents is less about building more agents and more about designing the system around them. In real operations, speed matters, but the ability to coordinate actions across workflows matters more.

Internally, you see quickly where agents deliver value today: monitoring inventory signals, tracking supplier commitments, flagging issues early, and triggering actions, not just insights. That shift from recommendation to execution is critical. Especially in supply chains where conditions are constantly changing, supplier delays, demand shifts, and logistics constraints, agents help teams respond in real time, not just react after the fact.

You also learn the harder part: how agents work together, how they integrate into existing processes, and where to lean on human intervention. Scaling isn’t just about autonomy; it’s about orchestration. If people don’t have clarity on “what should happen next and why,” you won’t build trust or adoption at scale. That is where a unified platform becomes critical, so agents are not isolated, but operating on shared data, aligned to the same workflows, and able to drive coordinated action across planning, supply, and production.

You’ve set a six-month goal for delivering measurable AI impact. What is the most common reason you see industrial AI projects fail to hit that mark, and at what point should a leader kill a stalled pilot rather than pivot it?

The most common failure indicator is trying to do too much at once with no clear measurement tied to throughput, quality, safety, or working capital. AI doesn’t fail because the model isn’t clever; it fails because it’s not embedded into the daily operating rhythm. What we are really talking about is a shift in operating model, from periodic, human-driven decisions to continuous, AI-assisted decision loops embedded directly into those workflows.

If it lives only in a dashboard, it won’t move the needle. It has to show up inside maintenance, scheduling, or quality workflows where decisions get made every day. As for “kill vs. pivot”: I’d give any pilot a clear checkpoint at 6–8 weeks. If you can’t get reliable data, real user engagement, and a leading indicator that the outcome is achievable, you don’t have a pilot, you have an experiment. Pivot if the outcome is right, but the workflow, data source, or scope is wrong. Move on if you can’t tie it to a real operational constraint and an owner who will run it after launch.

You frame AI as a “workforce multiplier,” not a replacement. But sentiment around AI is extremely negative right now, especially here in the United States, and frontline workers hear “autonomous operations” and immediately think layoffs. How do you address that cultural resistance in practice and get them to buy in, particularly when they’re the ones providing the critical context that agents on the factory floor need to function?

You earn buy-in by starting with respect for the people doing the work. I’ve stood next to technicians and operators who don’t want another screen, they want clarity. So we position AI as reducing friction: fewer fire drills, fewer surprises, faster root cause, and more time spent on high-skill work that only humans can do. Practically, that means involving frontline experts early, designing the system to explain “what to do next and why,” and putting clear guardrails in place so people understand where autonomy ends and human judgment begins. Often, the hardest part isn’t the technology, it’s change management. If leaders want trust, they need to pair AI investment with upskilling and a consistent vision, and they need to measure success in operational terms that matter to the team. That only works if the tools show up where people already work, in their daily workflows, not as separate systems they have to learn.

You talk about connecting every stage from design and simulation to production feedback, so manufacturers can test decisions before disruptions cascade. The “digital thread” is a vision the industry has been chasing for a decade. What is actually different now? Is it the AI layer, the data infrastructure, or something else that makes this achievable today when it wasn’t three years ago?

What we are describing is industrial intelligence, the ability to continuously connect data, AI, and workflows across the value chain and act on it in real time. What’s different now is the convergence of three things. First, the data foundation is finally catching up; more plants can bring together production, quality, maintenance, and supply signals in a usable way. Second, AI has advanced from “reporting what happened” to understanding patterns and cause-and-effect across silos, so you can see what is happening, not just what should be happening. Third, planning is moving closer to execution. Instead of static forecasts, teams can run frequent what-if scenarios using live production data, supplier updates, and demand shifts, so decisions get tested before disruptions cascade. So yes, the AI layer matters, but only because it turns the digital thread from a vision into something you can operationalize day to day. It is the combination of data platform, AI, and the applications people use every day that makes this practical, not just theoretical.

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This newsletter is brought to you with support from our partners: Jiga, Upkeep, Industry 4.0 Club, Ironloop, and T51.

Content I Enjoyed Last Week 🗞️🔬 📚

Some Interesting Reads:

Capgemini surveyed 1,678 senior executives across 15 industries for its new 140-page Physical AI report, and the headline finding is that 79% of organizations are already engaging with the technology in some form. Below are the slides that caught my attention.

• A great case study from Bristol Myers Squibb, where AI now monitors bioreactor variables such as temperature, pH, and oxygen in real time, intervening mid-batch to reduce time to market and increase yield. [WSJ]

• 94% of manufacturing respondents reported an increase in freight spend in 2026, and 82% agree that maintaining their current procurement practices puts their organization at risk. [Xeneta]

• AI Adoption in Manufacturing with the COO at Ford [Google]

• CloudNC founder only now realizes AI is an enabler of value in manufacturing, and not where the value accrues. [Fortune]

• A great interview with Brad Porter, highlighting that the real value of Physical AI is converting labor into a digitally controllable operating layer consistently reprogrammable, akin to software. [WSJ]

  • argues something similar here in a post about the Physical AI application layer.

• NVIDIA and Corning announce a partnership that would result in 3,000 new jobs and 50% increase in production capacity [NVIDIA]

• Apple Manufacturing Academy is expanding its reach to help SMB manufacturers implement AI and smart manufacturing techniques [Apple]

• Oxford’s Environmental Change Institute found that 85% of industrial energy could be electrified by 2030, if the grid interconnect problem were addressed [Canary Media]

• An interesting conversation with Allentown, PA, Mayor Matthew Tuerk about the city's grand strategy for building back and sustaining its manufacturing base, implementing industrial policy at the local level, and how zoning has changed over the last decade. [Bloomberg]

• York Yang and Dyna Robotics highlight the deployment and integration hurdles that must be overcome to enable widespread robotics deployment. [X]

• Bosch’s Shopfloor Agent drives annual savings of around €850,000 for each Bosch plant by reducing unplanned downtime. [Bosch]

• Research with an amazing breakdown of the Humanoid Robotics ecosystem, well worth the read if you have the time.

  Citrini Research

  Humanoid Robots

  Yes, robots…

  Read more

  a year ago · 284 likes · 49 comments · Citrini

Products & Research:

Vinci proposes a new category called Continuous Physics Reasoning, enabled by a physics foundation model. The model should reason directly over physical structure rather than abstracted features, be deterministic, hit solver-grade correctness at manufacturing resolution, generalize to new geometries and materials out-of-the-box with no customer-specific training, operate as a single global model with no per-customer forks or forward-deployed engineering, and run efficiently across scales.

• Autodesk AI Lab released 1 million executable, interpretable CAD sequences synthesized entirely without real-world data [Autodesk]

• Julia Hub plans to demo Dyan 3.0, an AI-first environment to model, test, and validate industrial systems, on May 19 [JuliaHub]

  

• CoLab announced a partnership with Bombardier [CoLab]

• Nominal acquired FID Labs to connect the hardware data supply chain [Nominal]

• Emanate emerges from stealth, focusing on handling quoting, sales, and procurement across the industrial materials ecosystem [Fast Company]

• Genesis AI launches GENE-26.5, a foundational model with a dexterous robotic hand that mirrors the human hand exactly, after capturing proprioception, tactile, and action-training data via a data-collection glove.

Finance & Transactions 💵

Tessera Labs raised $60M from A16z and Foundation Capital
Agentic ERP • Series A • USA

Nova Intelligence raised $31M from Chemistry
Agentic ERP • Series A • USA

Spread AI raised $30M from DTCP Growth, IQT, and others
Industrial AI • Series B • Germany

Planned Downtime 🧑‍🔧

Robotic’s End Game

Breaking the Bottleneck is brought to you with support from our partners.

• Jiga - Is a platform that connects hardware teams with vetted manufacturers for custom parts. Instead of black-box instant quotes, you see exactly who’s making your parts and can talk to them directly.

• Upkeep - CMMS Software helping maintenance teams manage work orders, assets, and preventive tasks efficiently from any device

• Industry 4.0 Club - Leading cross-functional group accelerating the adoption of Industry 4.0, delivering better experiences to consumers, better profits to manufacturers, and better jobs to factory workers.

• Ironloop - Ironloop is a modern OT/ICS software platform that delivers configuration-level intelligence, giving manufacturers ground-truth security visibility and compliance confidence across air-gapped networks.

• T51 - T51 is an AI-native planning, scheduling, and optimization platform that integrates securely with your existing ERP, MES, and data stack.

💥 If you want to chat, feel free to reach out at aditya@machinafactory.org.

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Green Dashboards Don’t Mean Your Plant Is Actually Running Better🎙️

You were a process engineer at a membrane plant watching technicians walk back and forth to desktop terminals just to log work orders. What was the moment you decided to build the fix yourself rather than wait for a vendor to solve it?

I wouldn’t say there was one big dramatic moment where I just woke up and decided, “I’m going to go start a company.” It was honestly more the accumulation of seeing problem after problem in the physical world, and at the same time realizing that if you wanted to actually build something in that world, everything moved painfully slowly.

I went into engineering because I loved to build. One of the things that hit me pretty quickly working in a manufacturing plant was that in the physical world, even when you know exactly what should be better, making a change can take months or years because you are dealing with real equipment, real processes, real constraints, real safety considerations, and a lot of inertia. At the same time, I was seeing all these very obvious workflow problems around me, especially for technicians and frontline teams, where the software clearly was not built for how they actually worked. Watching someone walk back and forth to a desktop terminal just to log a work order sounds small, but it reveals how disconnected the software was from the real job.

What pulled me in was that I’ve always enjoyed tinkering and building. Once I started teaching myself to code, my perspective changed entirely because, for the first time, I had a way to see a problem during the day, build a solution that night, and potentially release it the next morning. That was completely different from what I had experienced in the plant, where the cycle time for change was so long. So it was not really one moment so much as a growing realization that there were all these problems in the physical world worth solving, and software was the fastest tool I had ever found to actually go solve them.

You talk about shifting maintenance KPIs from “work orders closed” to “uptime impact on margin.” What’s the most compelling example you’ve seen of a customer actually tying maintenance data to revenue? And for companies earlier in their data maturity, what are the first steps to getting on that path?

One of the examples that stuck with me the most was visiting a manufacturing customer who said something insightful: they did not actually care about PM compliance or closed work orders as the end goal. That may sound surprising coming from a maintenance organization, but their point was that those metrics alone can drive the wrong behavior if you are not careful.

What they had seen over time was that when you tell teams the goal is to close more work orders or hit a certain compliance percentage, people get very good at optimizing for the check box, and you can end up in a culture where everyone is creating activity and green dashboards, but not necessarily creating better plant performance. In other words, you can make yourself feel productive without actually moving the business. What they did instead was tie everyone back to production outcomes, so maintenance, operations, and reliability were all fundamentally being measured against the same thing, which was keeping the plant running, driving throughput, and supporting the output of the business. Once you do that, the conversation changes because maintenance is no longer isolated, measuring success in a silo; it directly connects to what the business actually cares about: production, revenue, and margin.

To me, that is the right mental model. Maintenance metrics still matter, but they should be understood as drivers of business performance, not the end state themselves.

For companies that are earlier on this path, I actually think the first step is less technical than people think and much more organizational. Before you get into AI, dashboards, or complicated data models, you need shared goals. If maintenance is measured one way, operations is measured another way, and reliability is measured a third way, then by definition, you are going to create competing behavior. Maintenance reduces downtime now, reliability reduces downtime in the future by improving how assets perform over time, and operations is driving the day-to-day output of the plant, but all 3 functions really need to understand how they connect to the same business outcome. Once you have that, the technology becomes much more powerful because you are now using the data to drive a single scoreboard instead of 3 different ones.

You’ve said many plants are still running technology from the 1990s. What’s the single biggest non-technological barrier keeping them there, whether cultural, financial, or regulatory? And what go-to-market strategies have you found actually break through that resistance?

I think the biggest barrier is that the cost of disruption is much higher in the physical world. Because of this, people are a lot more cautious, and honestly, they should be.

In software, if you roll something out and it is a little messy, that is frustrating, but in a plant, a warehouse, or a facility environment, when something breaks, the consequences can be much bigger. You can affect production, create safety issues, or slow down a team that is already stretched thin. Consequently, many organizations stick with bad systems not because they love them, but because those systems are familiar, and the pain of change feels more dangerous than the pain of living with the status quo.

So yes, culture is part of it, but I think just calling it “cultural resistance” undersells what is really happening. The physical world moves more slowly because the downside of getting it wrong is real. That is why old systems survive way longer than they should.

From a go-to-market standpoint, the thing that works best is not leading with some giant abstract transformation message and trying to force it top down. What I have found works much better is finding the early adopters inside the business, the people who are naturally curious, ambitious, and frustrated enough with the way things work today that they actually want change. Every company has these people. They are usually the ones who are trying to do more, trying to improve things, and are constrained by old tools. If you help those people win, and you give them something that actually makes their life better and produces a visible result in the real environment, then they become your champions. That is when adoption starts to spread in a much more durable way, because now you are not selling hope, you are showing evidence.

UpKeep Studio lets local site managers build their own apps with a no-code AI builder. How do you prevent that from creating a new mess of fragmented, inconsistent tools across a multi-site operation? How do you balance flexibility at the factory floor with the need for enterprise-level structure?

That is honestly the whole point of Studio. We repeatedly heard from customers that every business and site operates differently, with unique workflows. Much of the software they receive forces them to contort their business around the tool instead of letting the tool adapt to the business.

Now with AI and modern tooling, it has become easier than ever to build apps or prototype workflows quickly, and that is exciting, but that is also exactly where the mess can start if you are not careful. The easy part now is building something fast. The hard part is everything that comes after: How do you deploy it into production? How do you ensure it scales? How do you manage permissions? How do you keep the data model clean? How do you prevent every site from spinning up random disconnected tools that break consistency across the organization?

That is why Studio matters. Yes, it gives teams the ability to build and move fast, but it does it on top of UpKeep’s architecture, infrastructure, data model, and permissions layer, which means you are not just creating random one-off apps floating around outside the system. You are building on top of a shared foundation and shared source of truth. In my mind, the balance is that local teams get the flexibility they need because they have unique workflows, while the enterprise still gains structure, consistency, governance, and scale.

That was the vision from the beginning. We did not want customers to have to choose between rigid software that does not fit their business and total chaos, where everyone builds their own disconnected tools. Studio is meant to give them flexibility without fragmentation.

As the U.S. pushes to reshore manufacturing, what does the maintenance technician’s role look like in a facility designed from the ground up to be highly automated and AI-integrated? For generative AI specifically, what guardrails should be built around “Autonomous Actions,” such as those in Nova, for the technology to be effective in safety-critical environments?

I think the technician becomes more important, not less. I believe much of the mainstream conversation around AI misses this point because it is too software-centric and not grounded enough in what work in the physical world actually looks like.

AI is not going to climb a ladder, it is not going to turn a wrench, it is not going to walk a site and notice something feels off in the way an experienced technician does. In safety-critical environments, there is still an enormous amount of judgment, physical presence, situational awareness, and real-world accountability required, and I think that remains true for a long time. What AI can do, though, is make that technician dramatically more effective by helping prioritize, helping triage, surfacing patterns earlier, automating the administrative layer of the job, and helping the team make better decisions faster. So instead of replacing the technician, I think what it really does is elevate the role and increase the leverage of the best people.

That is why I actually think this is one of the most exciting moments for people in the industry. The winners will be the technicians, supervisors, operators, and reliability leaders who learn how to use AI and software to augment themselves and move faster, not those who assume this technology is just hype and ignore it.

On the guardrails side, especially in safety-critical environments, I think the wrong way to think about autonomous actions is as unchecked automation. The right way to think about it is in terms of layered trust. There are some things the system should absolutely be able to suggest, some things it should be able to prepare, some things it should be able to execute with approval, and some things that should always remain gated by a human. You need clear permissions, clear audit trails, and a very clear understanding of what the system did, why it did it, and what data it used to reach that conclusion. In these environments, explainability matters, accountability matters, and human oversight matters. So I think AI becomes incredibly powerful here, but the near-term model is not “remove the human,” it is “make the human far more effective while keeping the right safety and trust boundaries in place.”

To contact Ryan, reach out to him on LinkedIn here. He’s always open to chatting and sharing valuable insights.

This newsletter is brought to you with support from our partners: Jiga, Upkeep, Industry 4.0 Club, Ironloop, and T51.

Content I Enjoyed Last Week 🗞️🔬 📚

Some Interesting Reads:

with an interesting analysis of the WSJ article stating that we were in a “stealth manufacturing boom.” The uptick is the result of the AI CAPEX boom, with labor data supporting the clear bifurcation in job growth between the advanced manufacturing (semiconductors, electrical equipment, and power gear) and the traditional manufacturing ecosystem.

Noahpinion

No, America is not in a "stealth manufacturing boom"

Greg Ip of the WSJ is one of my favorite economics writers, and you should always read what he writes. But in a recent post about manufacturing, I think he gets the main narrative wrong. Greg writes that America is in the middle of a “manufacturing revival”, which his headline writer calls a “stealth manufacturing boom…

Read more

2 months ago · 287 likes · 26 comments · Noah Smith

• A BCG analysis of the applications of AI across different companies in the Aerospace and Defense industry. A quick snapshot of some of those initiatives is below.

  

• A case study with GE Appliances and Gemini embedding Gemini Enterprise into its Brilliant Factory manufacturing data platform. [Google]

• Semantic context architectures drive a 42% TCO reduction versus stovepipe architectures in manufacturing [Kearney]

• Bain’s 2026 industrial automation analysis argues that value in the sector is migrating from the traditional control layer toward AI-enabled software and data platforms at the top, and intelligence-embedded smart field devices at the bottom. [Bain]

• KPMG released its Industrial Manufacturing Global Tech Report, with 76% of respondents citing unreliable data as a top AI risk, and 48% of executives plan significant increases in cybersecurity investment. [KPMG]

• Bessemer’s Robotics and Physical AI predictions for 2026. [BVP]

• An interview with the CEO of Neura Robotics on the Sim-to-real gap and the European opportunity for Physical AI. [McKinsey]

• A great breakdown of what’s needed to drive effective AI adoption in manufacturing: a structured digital model of the value stream as context, a closed-loop plan-versus-actual feedback mechanism anchored to that value stream, and human-controlled digital actions. [Threaded]

• The significance of the workforce availability problem on reshoring, tariffs, tax policy, and deregulation. [Dynamo Ventures]

• Ford’s joint venture with Geely is politically fraught but strategically interesting. Rather than competing head-on with China’s manufacturing machine, it creates an opportunity to learn from it directly. [WSJ]

• A pretty neat breakdown of Proto Town and the ecosystem of industrial tech startups that are using it as a test bed for building technologies [Bloomberg]

Products & Research:

The architecture behind Google’s 8th-generation TPU with two purpose-built systems, 8t for pre-training and 8i for inference and reasoning. 8t focuses on 47 petabits/sec of higher-bandwidth and 10x faster storage access, with an accelerator to reduce the bottleneck in the matrix multiplication units (see the ASIC block diagram below).

The 8i architecture cuts the chip-to-chip network diameter from 16 hops to 7, adds 3x more on-chip SRAM, and includes an acceleration engine that reduces on-chip latency by 5x (see the ASIC block diagram below). [Google]

Hannover Messe Highlights:

• Sight Machine launches Agent Crews to simulate possibilities, determine optimal settings, and make recommendations. [Sight Machine]

• Litmus launches Data Catalog to drive visibility, lineage, and governance when managing industrial data. [Litmus]

• Siemens released the Eigen Engineering Agent to help with common engineering tasks such as PLC coding, HMI visualization, and device configuration. [Siemens]

• Bobcat uses Arbortext within PTC Windchill to create and manage BOM‑linked technical publications and to help optimize the service supply chain. [PTC]

• Rockwell demonstrated a new AI Factory System design tool with the Emulate3D digital twin, Copilot in VSCode as an AI‑assisted engineering interface, and FactoryTalk Design Studio. [Rockwell]

• NVIDIA demonstrates the future of AI-driven manufacturing at Hannover Messe 2026 alongside a trove of partners [NVIDIA]

• NVIDIA Omniverse libraries and the open source NVIDIA Isaac Sim robotics simulation framework are now available on Google Cloud Marketplace [NVIDIA]

• ABB revealed Genix Industrial AI on Microsoft Azure, which acts as a real-time industrial co-pilot on the factory floor by analyzing streaming data from equipment and sensors. [Microsoft]

• SAP revealed a Production Master Data Agent and Production Planning and Operations Agent [SAP]

• Autodesk Fusion MCP and Fusion Data MCP launch to connect tools, help with understanding, and manage designs in context. [Autodesk]

Blogs and Threads

on the core bottleneck in automated engineering simulation: the cognitive and setup costs of building parametric automation logic that connects the intermediate steps, and the obsolescence of that logic once pipelines change.

Generative Engineering

Removing the bottlenecks in Engineering AI

Engineering systems are getting more complex, and at the same time the pressure to ship them faster is increasing. Teams that can explore more of the design space and iterate faster have a decisive advantage…

Read more

2 months ago · 4 likes · 1 comment · Laurence and Generative Engineering

thoughts’ on Mythos in Manufacturing

Breaking the Bottleneck

Manufacturing Mythos

Breaking the Bottleneck is a weekly newsletter and interview series covering manufacturing technology and physical AI. Want to chat? Reach out at aditya@machinafactory.org or connect with me on Linke…

Read more

2 months ago · 18 likes · 3 comments · Manufacturing Tech

Finance & Transactions 💵

Project Prometheus is raising $10B at a $38B from JP Morgan and Blackrock
Physical AI / Robotics • Capital • USA

Humble raised $24M from Eclipse
Physical AI / Autonomous Freight • Seed • USA

Rillian raised $17.5M from 8VC
Cybersecurity • Seed+ • USA

C-Infinity raised $16M from Canaan
CAD/Manufacturing • Series A • USA

Antioch raised $8.5M from A* and Category Ventures
Physical AI / Robotics • Seed • USA

Crewline raised $7.1M from Initialized Capital and Nebular
Physical AI / Autonomous Construction • Seed • USA

Planned Downtime 🧑‍🔧

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Manufacturing Mythos

Anthropic's Project Glasswing is the most important defensive cybersecurity initiative in years. At first glance, it has a glaring blind spot, not a single OT OEM or industrial cybersecurity company in the room.

A couple of weeks back, Anthropic unveiled Claude Mythos Preview, a frontier model the company says is too dangerous to release publicly. Mythos can autonomously discover zero-day vulnerabilities, write working exploits, and chain multiple flaws together across complex software stacks. It has reportedly already surfaced thousands of high-severity bugs in every major operating system and browser, including a 27-year-old flaw in OpenBSD. Project Glasswing is the coalition Anthropic built around it: twelve launch partners using Mythos to find and patch vulnerabilities in the world’s most critical software before adversaries develop similar capabilities. A frontier lab voluntarily withholding a model and routing its power to defenders first, that’s the right instinct. My problem is who’s announced to be in the room, and who isn’t.

AWS, Anthropic, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks are the hyperscalers, security vendors, chipmakers, and one bank that form the substrate beneath modern digital life. Anthropic has extended access to 40+ additional unnamed organizations, some of which may well be industrial. But the named cohort sets the public narrative, and that narrative is IT-first. Notably, the launch partners are all software producer organizations whose code Mythos can directly audit. That logic explains v1. It also tells you exactly what v2 should look like.

The absence of utilities, grid operators, water systems, refiners, chemicals, automotive, aerospace, rail, ports, and defense primes is conspicuous, especially for a company navigating a complicated relationship with the Department of Defense, the single largest buyer of industrial hardware in the country. The threat data corroborates it.

There's an IT arrogance in fielding a cybersecurity coalition without the industry that has been the most attacked sector on the planet for five consecutive years. Manufacturing accounted for 27.7% of all incidents across top sectors last year. The most common entry point is the exploitation of public-facing applications, which drove 32% of breaches, exactly the IT-exposed foothold that precedes OT compromise. Browsers get patched in days. The systems that deliver water, food, and energy to hundreds of millions of people run on firmware that hasn't been tested in a decade.

That neglect has real consequences. As we move into a more geopolitically fraught world, the ability to fight wars, move goods, and maintain a stable energy base depends on industrial systems, not browsers. Look at Jaguar Land Rover’s March 2025 cyberattack, which shut down manufacturing operations and rippled through automotive supply chains for weeks. Or the December 2025 attack on Poland’s energy sector, where threat actors compromised internet-facing edge devices, deployed wiper malware, damaged RTUs, and destroyed HMI data, causing operators to lose view and control of distribution systems. Or the recent Stryker breach, where Iranian-linked actors claimed an intrusion, a reminder that industrial-adjacent companies are being hit and that OT exposure often isn’t yet public. Colonial Pipeline is worth remembering too, not as an OT attack, but as proof that even an IT-side compromise can lead to catastrophic physical-world outcomes. Billing systems went dark, and fuel to the East Coast shut off for five days. The 2003 Northeast blackout cost an estimated $6 billion in two days.

The common thread isn’t browser zero-days. It’s an IT compromise, whether it pivots into OT or simply paralyzes the business systems that keep industrial operations running, combined with the direct targeting of internet-exposed assets: Modbus, OPC, PLCs, RTUs, HMIs, historians, and a long tail of legacy controllers running two-decade-old firmware never designed to touch a network.

The next cohort should be industrial, OT OEMs, their customers, and the security vendors that protect them, and it has to look different from v1. “Glasswing Industrial” should pull in OT vendors upstream of every plant floor, including Schneider, ABB, Emerson, Honeywell, Yokogawa, GE Vernova, and Mitsubishi, alongside OT-native security vendors that monitor these environments. These are the code producers in the OT stack, and they're where Mythos can do the work it's built for: auditing firmware, engineering software, and protocol stacks at the source, so fixes flow downstream to every asset owner.

Making this work is harder than assembling a new partner list. Mythos’s demonstrated strength is auditing modern, well-documented codebases. PLC firmware lives inside walled gardens, vendor-proprietary RTOSes, and IEC 61131-3 dialects with virtually no open-source visibility. A Siemens S7 blob is not Chromium. Any external auditing model is entirely dependent on vendor cooperation and access to sources. Even with access, the vendor tooling gap is significant. Simulated environments that let you confidently validate whether a firmware change is safe barely exist at scale. IT software rolls forward and back. OT firmware doesn’t. That’s precisely why v2 can’t just be v1 with new logos. It needs a shared, air-gapped testbed of representative OT stacks, donated controllers, virtualized DCS environments, and emulated protocol traffic where models can experiment without operational risk.

There’s also a legitimate question about whether Glasswing is even the right vehicle for this, or whether the industry can self-organize. Open-source models are closing the capability gap with frontier systems on a roughly six-month lag, and that gap is shrinking. If an OEM like Schneider or Honeywell can run Mythos-class vulnerability discovery in-house on their own firmware within a year using open-weight models, the need for a centralized Anthropic-led coalition diminishes. The more interesting long-term question may not be “will Anthropic invite industrial companies?” but “will industrial companies build this capability themselves before they’re invited?” The answer probably depends on how seriously OEMs treat security tooling as a product obligation rather than a compliance checkbox. But until that capability matures, any coordinated program still needs ground rules, and Glasswing's current rules don't fit.

The disclosure regime has to change, too. Glasswing’s 135-day clock works for browsers. It’s a nightmare for PLCs, where patch cycles routinely run 12 to 24 months due to validation, outage windows, and safety recertification. Glasswing Industrial would need a disclosure framework built around compensating controls, segmentation, virtual patching at the firewall, and detection rules as legitimate interim mitigations while firmware fixes are validated. Findings should route through CISA, which already runs the federal coordinated disclosure program for ICS, alongside DOE CESER for energy and the sector ISACs. Unlike the IT cohort, where vendors patch and push, OT fixes require asset-owner coordination at sector scale.

Glasswing v1 is the right idea executed against the wrong threat surface for half of the economy that physically produces things. The gap between “securing the world’s critical software” and “securing the world’s critical infrastructure” is the gap between a browser zero-day and a substation going dark. One is an inconvenience. The other is how cities and economies stop functioning.

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💥 If you want to chat, feel free to reach out at aditya@machinafactory.org.

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Why Manufacturing Vision Is a Systems Problem, Not Just an AI Problem 🎙️

You spent four years at SalesforceIQ shipping SaaS before making a hard pivot into industrials and computer vision. What was the specific gap you saw in manufacturing quality control that convinced you the problem was yours to solve? Additionally, what did you get wrong about the industrial world in your first year building UnitX, and how did your outsider’s perspective shape your early approach?

What convinced me was how much of manufacturing quality control was still basically stuck between human inspection and brittle rule-based vision. Factories were producing incredibly complex products, but the inspection stack was often either a person staring at parts for hours or a classical vision setup that worked only when the environment was tightly controlled. The gap was obvious: deep learning AI had become good enough to understand high-variance visual data, but it had not yet been turned into a product that could survive real factory conditions.

What made it feel like our problem to solve was that this wasn’t just an AI problem. It was a systems problem: imaging, lighting, data, deployment, and integration all had to work together. That’s exactly the kind of problem where we can make an impact, because we love precision hardware, imaging optics, reliability engineering, in addition to AI.

What I got wrong in year one was underestimating the deployment effort needed for manufacturing vision. Coming from SaaS, I thought that if the model worked, adoption would follow. That’s not how factories work. A model that performs great on benchmarks in a lab is meaningless if it can’t pass SAT, run nonstop in production, survive lighting drift and vibration, or integrate with a PLC effortlessly. I also underestimated how conservative the buying motion is. In software, you can sell a vision of where the product is going. In manufacturing, customers care much more about whether it will work on Tuesday at 2 a.m. when the line is under pressure.

At the same time, being an outsider helped. I didn’t inherit the assumption that machine vision had to stay a services-heavy business. We approached it with a more product-centric mindset: why should lighting be fixed? Why should training need hundreds of defect images? Why should every deployment require custom engineering? That outsider, first-principled view is part of why we ended up with UnitX’s unique software-defined lighting, small-sample AI, and a much stronger focus on repeatability. This is what enabled UnitX to win the trust of 190 manufacturing customers, including 10 of the world’s top 50 automotive manufacturers.

Lighting is the Achilles’ heel of industrial vision. How does OptiX’s software-defined lighting actually work in practice when you’re dealing with a shiny aluminum radar case versus a translucent battery coating on the same line?

The core idea is simple: instead of treating lighting as a fixed hardware choice, we treat it as something programmable. UnitX lowers the barrier to vision engineering, so a non-engineer can learn to use it in minutes.

In a traditional setup, you pick a light, mount it, test it, and hope it works across the full range of parts and defects. If it doesn’t, you start over with another light or another fixture. That’s one of the biggest reasons machine vision deployments become slow and expensive.

With OptiX, the hardware is designed to allow the illumination pattern to be changed in software. In practice, that means we can sweep through different lighting directions and patterns, see which ones create the best contrast for the surface or defect we care about, and then lock in the best recipe for production.

Take your two examples. A shiny class-A painted surface is hard because specular reflections can saturate the signal. A translucent conformal coating is hard for almost the opposite reason: the light interacts with the material in a more diffuse, subsurface way, and the defect signal can be subtle. You want a single versatile hardware platform that can present the part under many different lighting conditions and let software determine which lighting conditions reveal the defect best.

So in practice, the system captures the same part under multiple illumination states very quickly (up to 30 frames per second), and the software selects or combines the views that maximize useful contrast. That’s why we say imaging becomes programmable. It gives us two advantages: first, better defect contrast; second, much faster dial-in when the part, surface, or defect changes.

SE Ventures said they “looked at every major startup in this category” before investing. What did they see in UnitX’s go-to-market that the other vision startups were missing? As you scale, you’ve shifted toward a Certified System Integrator model rather than direct deployment. What happens when a third-party integrator, not your team, is responsible for getting a UnitX system to Site Acceptance?

I think what they saw was that we have real deployments in production. A lot of vision startups can show an impressive model demo. Far fewer have a credible plan to turn that into a repeatable, scale-deployed base in manufacturing.

From the beginning, we focused on hard inspection problems where the pain is obvious, and the ROI is measurable. We also learned pretty early that if the product only works when our own best engineers are on site, that’s not a company - that’s a consulting organization. Now that we have deployed across 190 manufacturers, our go-to-market evolved toward self-serve software and eventually an integrator-led deployment model.

That’s where the UnitX Authorized System Integrator Network comes in. The goal is not just to outsource labor. The goal is to productize deployment so that a trained partner can deliver a successful SAT with a defined playbook repeatably at scale.

When a third-party integrator owns SAT, we create a win-win situation. System integrators can solve a previously hard problem more easily with a next-generation AI vision system. UnitX can help more manufacturers produce higher-quality products more efficiently and make them more competitive. This fulfills UnitX’s mission to help manufacturers.

You’ve described a future where CorteX sends a command directly to an upstream CNC machine or laser welder to self-correct. How far away is that closed loop in practice, and what’s the technical or organizational bottleneck holding it back?

Technically, pieces of the loop are already here. We can detect patterns, identify drift, and localize where the issue is happening. So the question isn’t whether a closed loop is possible in principle. It is. The question is how to make it actionable in production.

The bottleneck is less about AI inference and more about trust, process ownership, and control boundaries. If an inspection system flags a defect, that’s already valuable. If it starts telling an upstream machine to change a parameter in real time, now you’re crossing into process control. That touches manufacturing engineering, quality, and control teams, and, in some environments, even safety or validation requirements.

So my honest answer is: the first closed-loop systems will appear in narrow, well-bounded cases sooner than people think, but broad adoption will take longer because organizations need confidence in AI, not just technical capability. You need high confidence in AI accuracy and reliability, and someone in the factory who is comfortable giving the system that authority.

In other words, the limiting factor is not “can the AI send the command?” It’s “does the factory believe the system understands the process well enough to deserve that authority?” That trust will be earned one bounded application at a time. Every year, the value of products inspected by UnitX is $15B. Chances are, you have a phone in your pocket or are driving a car with components inspected by UnitX to ensure safety and quality. We are making good progress here at UnitX helping manufacturers adopt AI.

A bit of a change in pace, but humanoid robots are getting enormous attention as the future of flexible manufacturing. Your thesis is that perception is the missing piece, not more actuators or dexterity. So I was curious, do you think the humanoid wave is solving the right problem for manufacturing, and where do you see robotics becoming truly pervasive on the factory floor?

I don’t think humanoids are the right form factor for manufacturing. In manufacturing, the question is not “can a robot look human?” The question is “can it do useful work reliably, safely, at the right cost, within the cycle time, inside an existing production line?”

Where I think robotics will become pervasive first is not in generic humanoids. It’s narrower and more pragmatic: perception-heavy tasks where there is already structure, repeated motion, and clear economic value. Inspection is one. Then you move into manipulation tasks that are tightly coupled to the same perception problems, where non-rigid contact dynamics have historically kept robots out.

The right form factor for manufacturing will be between automation and AI robots. There is so much to respect, appreciate, and learn from good old automation. The magic happens when one combines AI perception understanding with process automation principles. That’s why UnitX’s roadmap is eyes first, then brain, then hand. While we solve real pain points with deployed units. We think that’s the more grounded path to real adoption of robotics in manufacturing.

To contact Keven, reach out to him on LinkedIn here. He’s always open to chatting and sharing valuable insights.

This newsletter is brought to you with support from our partners: Jiga, Industry 4.0 Club, Ironloop, and T51.

Content I Enjoyed Last Week 🗞️🔬 📚

Some Interesting Reads:

A great breakdown on how China mastered the unglamorous, electricity-intensive metallurgical and chemical process industries that sit underneath the electro-industrial stack fed by coal and hydropower well before renewables scaled, and now producing 9-10x the next-largest global player. No Western country can replicate the stack without first rebuilding that upstream foundation, which surfaces an uncomfortable implication: much of the electrotech supply chain lives in the hardest 30% of the economy to decarbonize, and insisting on a clean-only path may be precisely what's perpetuating the industrial gap. [Breakthrough Institute]

• The significant impact of tariffs across the automotive supply chain [WSJ]

• Tax cuts and tariffs, while promised to deliver a "blue-collar boom," have actually led sectors like manufacturing and construction to shed roughly 150,000 jobs over the past year, with manufacturing hiring down 40% since 2022 and job openings collapsing to 2018 levels. Meanwhile, health care and social assistance led the 2025 job growth. [NYTimes]

• NAM with recommendations on improving R&D Tax Credits to drive greater AI adoption within manufacturing [NAM]

• Intel partners with SpaceX for the new Terafab [WSJ]

• A really interesting breakdown on the impact of polyethylene prices as a result of the Iran War. While chemical companies were able to adjust to the shift in supply from the Middle East, Asia, and Europe, absorb demand, and raise prices, downstream packaging manufacturers like Amcor and Magnera have seen sharp share-price declines, and Toy Manufacturers are taking hits to margins. [WSJ]

• F Prime Capital with their State of Robotics Report for China [F-Prime]

  

• A breakdown of the advantages and challenges of the Chinese general-purpose robotics ecosystem, specifically hardware and the physical aspects of the robotics ecosystem. [X]

• A great perspective from on hardware as a GTM wedge, a great strategy for the industrial economy. [X]

• Why AI fails in supply chains and creates a hidden “AI trust tax” of manual verification, workarounds, and second-guessing that compounds across the organization. [Inc.]

• A few examples of supply chain AI agents. [Microsoft]

• BCG modeled variables across China and the US to try to understand what best predicted wind and solar capacity growth. The formula: strong demand growth, cheap land in resource-rich areas, and proactive investment in high-voltage transmission, not resource abundance, not national policy targets, and not energy security concerns

• $99 for a Smart Manufacturing Landscape Report [IoT Analytics]

Products & Research:

’s conversation with Qasar Younis and an inside look at their Physical AI Garage

• Assembly, a new virtual process simulation solution designed to help manufacturers identify assembly issues earlier in development [Keysight]

• GreyMatter Robotics and HII announce partnership to improve shipbuilding operations [Manufacturing Dive]

• Glue addresses the paradox in Test & Measurement by removing ambiguity, yet the workflow remains stubbornly manual. Glue is building AI-augmented test authoring that translates plain-language test intent into instrument commands, abstracts away driver complexity, generates structured results, and flags anomalies against historical baselines. [Glue]

• An interesting comparison guide comparing CNC machining versus laser cutting. It breaks down the key differences in precision, materials, geometries, and cost to help engineers choose the right process. [Jiga]

  

• Siemens and NVIDIA have achieved a major design verification breakthrough, using Siemens’ Veloce proFPGA CS with NVIDIA’s performance-optimized chip architecture [Siemens]

• Flow Engineering shared their Iterative Systems Engineering Handbook. Check out the volumes here. It’s a fantastic read.

  

• Pronto, a vision-first, OEM-agnostic approach to Autonomous Hauling, is acquired by ex-Uber CEO Travis Kalanick’s startup Atoms. [Pronto]

• Foundry Robotics comes out of stealth [Foundry Robotics]

• Ency Software launches a new AI CAM tool in closed Beta [Ency Software]

• ABB Robotics to integrate Jacobi’s OmniPalletizer AI software [Jacobi]

• Synopsys launches an AI-powered analog layout synthesis capability [Synopsys]

Blogs / Threads

Generalist is unveiling GEN-1, a robotics foundation model trained from scratch on what they claim is the world's largest physical interaction dataset (500,000+ hours), and they're deliberately refusing to label it as either a VLA or a world model, arguing that both framings are method-driven distractions from the real goal of physical AGI. [X]

Finance & Transactions 💵

Foundry Robotics raised $19M from Khosla Ventures and others
Physical AI / Robotics • Seed • USA

Hybron raised $25M from Marque Ventures
Composite Manufacturing • Seed • USA

Planned Downtime 🧑‍🔧

Robotics Legend and Alfred Lin of Sequoia Talk AI

A Great Conversation Between The Leading Scientists at Google and NVIDIA

Breaking the Bottleneck is brought to you with support from our partners.

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• Industry 4.0 Club - Leading cross-functional group accelerating the adoption of Industry 4.0, delivering better experiences to consumers, better profits to manufacturers, and better jobs to factory workers.

• Ironloop - Ironloop is a modern OT/ICS software platform that delivers configuration-level intelligence, giving manufacturers ground-truth security visibility and compliance confidence across air-gapped networks.

• T51 - T51 is an AI-native planning, scheduling, and optimization platform that integrates securely with your existing ERP, MES, and data stack.

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The Cost of Change Management Will Exceed the Cost of the Tools Themselves 🎙️

You spent five years on the A-10 production line at Fairchild Republic in the late ‘70s and early ‘80s, then grew a strategic business unit from $135M to over $600M at General Dynamics. With that firsthand experience, you describe a “Deployment Paradox,” that the U.S. is great at inventing manufacturing technologies but terrible at deploying them at scale. Where exactly does the breakdown occur between a proven technology and a factory actually using it? And what should a plant manager or VP of operations look for to avoid buying technology that never makes it past the pilot phase?

I’ve lived both ends of this problem, from the A-10 line at Fairchild Republic to scaling a business at General Dynamics, and the breakdown is rarely about the technology itself. It’s almost always about the system around it. The failure point sits squarely between proof and production. We’re very good in the U.S. at demonstrating that something works in a controlled environment. But scaling requires integration into messy, real-world operations, including legacy equipment, workforce variability, supply chain constraints, and production pressures. That’s where things stall. I call it a “deployment paradox” because the more advanced the technology, the harder it is to deploy. Not technically, but organizationally.

So what goes wrong? The technology gets validated in isolation rather than in the context of the full production system. There’s no clear ownership for scaling beyond the pilot. Workforce readiness is treated as an afterthought. ROI is modeled theoretically rather than operationally. And most importantly, leadership underestimates the change management required.

If I were a plant manager or VP of operations, I’d ask three questions before buying anything. First, where has this run at production scale, not just in a lab or demo cell? Second, what changes in my workflow, staffing, and metrics will this require? And third, who owns deployment, and are they accountable for outcomes rather than just installation? If you can’t get clear answers to those, you’re not buying a solution. You’re funding a science project.

You led the standup of DMDII, now MxD, as its founding executive director. What did you learn about getting competitors to collaborate in the same room on pre-competitive technology, and how do you measure the ROI fostered by those partnerships and collaborations?

Standing up the Digital Manufacturing and Design Innovation Institute, now MxD, was one of the most instructive experiences of my career. The biggest lesson is that competitors will collaborate, but only under the right conditions. You have to create a trusted neutral environment where the work is clearly pre-competitive. That means no one is giving away their “secret sauce,” the focus stays on shared infrastructure, standards, and foundational capabilities, and the outcomes benefit the entire ecosystem rather than just one player.

The second piece is alignment. You need industry, government, and academia all solving the same problem from different angles, but toward a shared objective. And third, you have to move fast enough to matter. Industry will disengage quickly if collaboration turns into an academic exercise.

As for ROI, it’s not measured like a traditional capital investment. It shows up in three ways. There’s risk reduction, where companies avoid duplicating expensive trial-and-error efforts. There’s acceleration, where technologies move from concept to deployment faster. And there’s workforce readiness, where people are trained on emerging tools before they hit the factory floor. If a collaboration isn’t producing those outcomes, it’s not delivering value. The organization I took over in 2019, the National Center for Defense Manufacturing and Machining (NCDMM), runs the America Makes program. It preceded the DMDII award, and in my opinion, NCDMM does it better than any of the other Manufacturing Innovation Institutes. Admittedly, I’m biased.

You’ve advocated for the Model-Based Enterprise, where the digital model is the single source of truth across a product’s entire lifecycle. That requires mechanical engineers to fully embrace data science. Realistically, how’s that cultural shift going inside legacy defense, aerospace, and automotive manufacturers? And what should manufacturers expect the transition to cost them, not just in software, but in time and organizational change?

The vision of the Model-Based Enterprise is absolutely the right one, but the cultural transition is harder than the technology. In legacy sectors like defense, aerospace, and automotive, I’d say we’re somewhere between early adoption and cautious scaling. There’s real progress, but it’s uneven. The biggest challenge is that this isn’t just a tooling change. It’s an identity shift.

Mechanical engineers are being asked to think like data engineers. Drawings are being replaced by data-rich models. And decisions are increasingly driven by digital continuity rather than tribal knowledge. That’s a big lift. Resistance isn’t irrational. It’s rooted in decades of success using the old methods.

What should manufacturers expect? On time, this is a multi-year transition rather than a quick implementation. On cost beyond software, the real investment is in process redesign, data governance, and training. And on organizational friction, you’re breaking long-standing silos between engineering, manufacturing, and IT. In many cases, the cost of change management will exceed the cost of the tools themselves. The companies that succeed treat this as a business transformation rather than an IT project.

After working on these problems, digital thread, smart manufacturing, SME modernization, for over a decade now, and when you look at the American industrial base, where are the biggest opportunities for AI to drive improvement? And if you’re a mid-size manufacturer evaluating AI vendors for the first time, what’s the first problem you’d solve, and what would you look for in a partner?

After working on digital thread and smart manufacturing for over a decade, I see AI as a powerful but often misapplied tool. The biggest opportunities in the American industrial base are not in flashy applications. They’re in closing execution gaps. Specifically, that means process optimization in real time, predictive quality and defect prevention, supply chain visibility and resilience, and maintenance and asset utilization.

If I were a mid-size manufacturer evaluating AI for the first time, I would start with quality. Why? Because it’s measurable, it’s expensive when it goes wrong, and it touches every part of the operation. The question isn’t “Where can I use AI?” It’s “Where is variability costing me the most money?”

When evaluating a partner, I’d look for a few things. Do they have domain understanding, meaning do they understand manufacturing or just algorithms? What’s their data readiness approach, and can they work with imperfect, real-world data? What’s their integration capability, and will this plug into my existing systems or create another silo? And what’s the time to value, meaning can they deliver something meaningful in months rather than years? If they lead with technology instead of outcomes, that’s a red flag.

Finally, as we shift into a new set of supply chain operating conditions, you’ve advocated for “friendshoring” as a strategic response to tariff volatility and supply chain risk. Yet the U.S. seems to be leaning toward an onshoring approach, with trillions in industrial investment promised by corporations and countries. Is this actually viable for most manufacturers, and what is needed to address the industry-wide skills and labor gap?

I’ve been a strong advocate for friendshoring because it reflects reality. No industrial base operates in isolation anymore. The current push toward onshoring, backed by significant public and private investment, is directionally important, but it’s not universally viable. For many manufacturers, especially mid-sized firms, full onshoring introduces challenges around higher labor costs, workforce shortages, capital intensity, and the time required to build and scale.

The real answer is a hybrid model, with strategic onshoring for critical capabilities combined with resilient, trusted global partnerships. That’s what friendshoring is about. The biggest constraint, by far, is the workforce. We don’t just have a labor gap. We have a skills gap. Closing it will require modernizing how we think about manufacturing careers, embedding digital skills into technical education, scaling apprenticeships and work-based learning, and upskilling the existing workforce rather than just hiring new talent.

Technology alone won’t solve this. In fact, without the right workforce, it amplifies the problem. If we don’t address skills at the same pace as investment, we’ll end up with underutilized capacity, with factories that are built but not fully staffed or optimized.

To contact Dean, reach out to him on LinkedIn here. He’s always open to chatting and sharing valuable insights.

This newsletter is brought to you with support from our partners: Jiga, Industry 4.0 Club, Ironloop, and T51.

Content I Enjoyed Last Week 🗞️🔬 📚

Some Interesting Reads:

An interesting set of slides and breakdown from BCG on how AI can be leveraged as a critical tool for managing today’s cost pressures. Here are some of my favorite slides:

• Jason Furman on the challenges of reversing America's manufacturing decline. He argues that reshoring efforts have largely shifted jobs to other sectors rather than rebuilding the manufacturing base, and that much of the policy thinking is rooted in nostalgia for an era when manufacturing wages commanded a premium over the information economy. [NYT]

• The ever increasing ransomware problem in manufacturing. Attacks surged 61% year-over-year in 2025. Smaller manufacturers are the primary target within the sector, with companies with fewer than 200 employees and $25M in revenue most frequently hit. [Halcyon]

  • More here on the importance of OT threat hunting [Dragos]

• Some thoughts on TPG’s $600 million bet on Proficy [Rain Eng]

• A pretty neat breakdown from AWS on Volkswagen’s end-to-end marketing image generation and evaluation pipeline. The approach leverages a dual-track LLM as a Judge model, with synthetic data trained on brand compliance standards, to create company-specific branding. [AWS]

  

• Intel is acquiring a stake in Fab34 for $16.2B that it sold for $11B two years ago [Manufacturing Dive]

• Deloitte with a perspective on the agentic supply chain [Deloitte]

• A case study from Onshape with Fauna Robotics [PTC]

• An interesting study by PwC and the Manufacturing Institute examining the lack of bottom-up adoption of AI with the manufacturing frontline. 54% of respondents rate their frontline leaders’ competence to lead AI-driven change as low or very low, and 72% cite employee resistance, and 57% cite training gaps as barriers. [PWC]

• The Physical AI inflection point [Antler]

Sponsored By Jiga: The worst part about a missed deadline isn’t the delay; it’s finding out too late to do anything about it. Most platforms give you a tracking number and hope for the best. On Jiga, you communicate directly with the manufacturer and get proactive updates on production status. When something shifts, you know early enough to adjust, not after your line is already waiting.

Products & Research:

Generalist introduces GEN-1, trained on 500,000 hours of physical data, improving average success rates to 99% from 64% compared to previous models, being roughly 3x faster than other state-of-the-art approaches, and requiring only 1 hour of robot data for results.

• Trace Space with an AI agent built for systems engineers [Trace Space]

• A great thought piece from Oleg at OpenBOM on why PLMs need re-architecting. The next generation should have product-aware memory, action-level permissions, event-driven architecture, and workspaces where agents can act with human approval. [Beyond PLM]

• Highbyte reveals a feature that enables manufacturers the ability to configure, understand, and edit industrial data pipelines via conversation [Highbyte]

  

• CI/CD for hardware validation and test []

• A conversation with the Vinci4D founder, Hardik Kabaria, on why hardware design requires a different kind of AI. [Vinci4D]

• Leo can search internal and online parts, and generate a full CAD assembly [Leo]

• A great breakdown from Sofiane on why agentic CAD has not been built and why it’s so hard. He argues this is due to the focus on static geometry with no history, relationships, or intent; the training data desert compared to other image libraries; the limited context capture of system constraints; and the inability to capture the true art of surface modeling.

• TD Engine’s breakdown on the value of an asset-centric vs event-centric approach to industrial data modeling. [TD Engine]

• Oracle released a design-to-Source Workspace for PLM [Oracle]

• A Snowflake piece argues that real-time manufacturing quality control requires a hybrid architecture connected via a Unified Namespace (MQTT + Sparkplug B) to eliminate OT/IT blind spots. [Snowflake]

Blogs / Threads

A breakdown and perspective on the above US Manufacturing NY Times OP-ED from the .

Radical Centrist

Reviving Manufacturing Employment

Jason Furman takes on an old issue, reviving manufacturing employment [https://spon.ca/every-president-tries-it-it-never-works/2026/04/02/] in the US…

Read more

3 months ago · 6 likes · 8 comments · Thomas L. Hutcheson

Finance & Transactions 💵

Cognichip raised $60M from Seligman Ventures
Semiconductors / ECAD • Growth • USA

Anvil Robotics raised $5.5M from Matter Venture Partners
Physical AI • Seed • USA

Planned Downtime 🧑‍🔧

A Long Strange Trip with Jack Dorsey and Roleof Botha

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• Jiga - Is a platform that connects hardware teams with vetted manufacturers for custom parts. Instead of black-box instant quotes, you see exactly who’s making your parts and can talk to them directly.

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• Ironloop - Ironloop is a modern OT/ICS software platform that delivers configuration-level intelligence, giving manufacturers ground-truth security visibility and compliance confidence across air-gapped networks.

• T51 - T51 is an AI-native planning, scheduling, and optimization platform that integrates securely with your existing ERP, MES, and data stack.

This newsletter is brought to you with support from our partners: Jiga, Industry 4.0 Club, Ironloop, and T51.

Simscale’s State of Engineering AI 2026

Simscale released The State of Engineering AI 2026 surveying 350 engineering leaders across the United States, the United Kingdom, and Germany. Below were some of the key takeaways:

• Agentic AI adoption is concentrated in a few key areas: simulation and CAE (19%), followed by design and CAD (11%), and requirements engineering (10%).

• 92% of respondents report deploying surrogate models to accelerate simulation or design exploration workflows in 2025

• AI simulation has had a profound effect.

  • On average, simulation requests take 17 hours with conventional workflows, compared with 6 hours with AI-enabled workflows. This is close to a 3× faster turnaround.

  • Furthermore, AI-enabled workflows are associated with one daily design iteration being tested in simulation, compared with one design every 2.5 days using conventional workflows.

  • Companies are now assessing 56 design variants per program, compared with 17 variants with conventional workflows.

    

• Secure data governance and access controls (70%), clear cross-functional ownership for moving pilots into production (65%), and an executive mandate with a defined budget (56%) are key drivers for scaled AI adoption.

• The survey identifies three primary blockers: data preparation/availability for AI (74%), governance and compliance concerns (48%), and software interoperability challenges (42%).

  

Check out the full report here!

Content I Enjoyed Last Week 🗞️🔬 📚

Some Interesting Reads:

McKinsey also dug deep into Engineering AI, interviewing 24 senior simulation leaders. Early adopters are already reporting up to 50% reductions in time to market, 15-60% improvements in product performance, and 30-50% gains in workforce productivity. One of the more interesting tensions in the report is around tooling, where 60% of interviewees expect a shift toward best-of-breed modular tools over monolithic software suites, though a vocal minority argues that workflow simplicity will keep companies anchored to comprehensive single-vendor platforms. [McKinsey]

• A breakdown on the importance of REE. [Deloitte]

• GM has built software that takes hand-drawn sketches from multiple angles and assembles a complete 3D model of the vehicle [Business Insider]

• Fast Company revaled it’s most innovative manufacturing companies for 2026, with a few friends included. [Fast Company]

• Apple is expanding its American Manufacturing Program, or AMP, with new programs with Bosch, Cirrus Logic, TDK, and Qnity Electronics to manufacture essential materials and components in the U.S [CNBC]

• Another Robotics market map from Emily Yu at Boost [X]

• Another $1B in funding for Physical Intelligence [TechCrunch]

• Elon is building a new Terafab facility near Tesla’s Austin headquarters, which is an absurdly hard problem, but excited to see how he’s able to reinvent the process. [TechCrunch]

• An interesting transition from SES AI from advanced lithium metal batteries for major industries like electric vehicles to an AI materials discovery platform [MIT Tech Review]

• Some examples of ways automotive manufacturers are accelerating smart factory investments [Automotive World]

• The Helium crunch looming over the global economy [NYTimes]

Products & Research:

Laminar on what it actually takes to bring AI-driven technology into a food and beverage manufacturing facility [Laminar]

• MachineMetrics ran a pretty neat experiment with five discrete manufacturers to build solutions that close the operational gaps that standard MES software never quite addresses. Harvey Performance built a lights-out scheduling dashboard that calculates dynamic setup times. Flexco built a forklift dispatch system from scratch. Johnstech’s build surfaced a decade of unprocessed tool-change data already sitting in the MachineMetrics platform, retroactively filling 90 days of tool-life history with no new hardware, while also closing a 10-to-15-year gap in planned-versus-actual production-time visibility via a live ERP integration. [Machine Metrics]

• A great robotics market map and breakdown on the efficient frontier, the intelligence stack, and the robotics data problem from Jonathan Healy. [Cathay Innovation]

• Augury and MaintainX partner to drive closed loop maintainence. When a machine anomaly is detected via Augury, a work order is automatically created in MaintainX with recommended actions, diagnostic context, and supporting data, so technicians can act immediately. [MaintainX]

• Cognite adopts NVIDIA’s NV-Tesseract family of models to operationalize foundational forecasting models with early success at Celanese.[Automation.com]

• WORC launches next week, check it out here! [emm0sh on X]

Blogs / Threads

An awesome breakdown of the ramifications of the Iran War on Supply Chains and the Industrial Economy from.

Defy the Odds

Mapping the Damage from the Iran War I: The Timeline of the Supply Chain Shock

The war with Iran has now been running for a month. Most investors know the Strait of Hormuz matters. Many already understand that the disruption goes well beyond crude oil, into diesel, LNG, petrochemicals, fertilizer, and further…

Read more

3 months ago · 62 likes · 3 comments · Defy the Odds

A really interesting essay from arguing that manufacturing is following the same AI adoption arc as software development, but will face more complex conditions that will make the transition slower.

Burhop

From Code to Atoms: How AI Will Change Manufacturing by Following – and Departing From – Software

Over the last few years, software development has become the first large domain where AI is embedded in day‑to‑day production work. AI systems now help write specifications, generate code, run tests, triage incidents, and perform parts of deployment with humans in supervisory roles. Manufacturing is beginning a similar transition, but under harsher cond…

Read more

3 months ago · 3 likes · Burhop

Finance & Transactions 💵

Doss raised $55M from Madrona and Premji Invest
Manufacturing AI / ERP • Series B• USA

Normal Computing raised $50M from Samsung Catalyst Fund
Semiconductors • Seed • USA

Entrix raised €43M from Junction Growth Investors and Korys
Battery • Seed • Germany

Sift raised $42M from StepStone
Data / AI • Series B • USA

Planned Downtime 🧑‍🔧

Fred Again in London

Breaking the Bottleneck is brought to you with support from our partners.

• Jiga - Is a platform that connects hardware teams with vetted manufacturers for custom parts. Instead of black-box instant quotes, you see exactly who’s making your parts and can talk to them directly.

• Industry 4.0 Club - Leading cross-functional group accelerating the adoption of Industry 4.0, delivering better experiences to consumers, better profits to manufacturers, and better jobs to factory workers.

• Ironloop - Ironloop is a modern OT/ICS software platform that delivers configuration-level intelligence, giving manufacturers ground-truth security visibility and compliance confidence across air-gapped networks.

• T51 - T51 is an AI-native planning, scheduling, and optimization platform that integrates securely with your existing ERP, MES, and data stack.

One Big Thing

Rhodium Group just dropped its latest Clean Investment Monitor report, with the renewable value chain now a nearly $2 trillion annual capital allocation event. But the growth rate tells a different story. Clean-tech deployment investment grew 33% in 2022, 28% in 2023, then hit a wall, 8% in 2024, 7% in 2025. And while deployment keeps climbing, manufacturing investment has fallen off a cliff.

Global manufacturing and industry investment totaled $155 billion in 2025, a 35% decline from 2024 and over 40% below the 2023 peak. Solar manufacturing alone collapsed 69%. The primary driver? China. Chinese firms accounted for over 70% of global growth in clean-tech manufacturing through 2023, then pulled back sharply as Beijing tried to rein in the overcapacity that sent prices plunging across the EV and battery supply chains, and created an “involution” in the EV market. Ye,t even after that drawdown, China still holds 92% of global solar cell capacity, 74% of wind nacelle capacity, 84% of battery capacity, and 69% of EV manufacturing capacity. The investment is declining because the factories are already built.

The U.S. story is the exact opposite. Q4 2025 set a record for canceled manufacturing investment, with roughly $8 billion in projects killed. Across 2025, 19 EV supply chain manufacturing projects representing $22 billion in planned investment were canceled, exceeding the $21 billion in new announcements. When cancellations outpace new commitments, that's not a slowdown. That's capital fleeing.

What makes this worse is that China isn't just sitting on its domestic capacity. For the first time in 2024, Chinese companies invested more in EV supply chain capacity overseas than at home. The U.S. isn't competing with China's factories. It's competing with China's ability to project manufacturing capability into friendlier jurisdictions. The risk for the U.S. and Europe is that their domestic manufacturers never reach the scale to survive a global price war with producers who've already amortized their capital costs.

There are green shoots, though. Of all new battery cell manufacturing investments announced in 2025, BESS-specific projects hit a record of over $15 billion globally. In the U.S., 97% of new battery manufacturing investment was geared toward energy storage, up from essentially nothing two years earlier. Several U.S. companies have already pivoted production lines from EVs to BESS, collectively accounting for nearly a quarter of current operational capacity, as grid-scale storage demand accelerates on the back of renewable integration and the data center buildout. That kind of manufacturing flexibility is exactly the adaptive behavior the U.S. clean-tech sector needs more of. And if the U.S. can't be the alternative to China, India might be. Their clean-tech manufacturing investment grew 17% in 2025 to $11.4 billion, with the country on track to have the largest manufacturing capacity for batteries, solar, and wind outside China and the U.S. by 2030.

This is what should be driving the conversation in Washington. Domestic manufacturing of solar panels, batteries, and storage equipment means jobs that are hard to automate, rooted in physical production, and resistant to offshoring once established. It hedges against energy volatility, the kind playing out right now with the Iran conflict, and it gives the rest of the world an alternative to Chinese-produced energy infrastructure. [Report Link Here]

Content I Enjoyed Last Week 🗞️🔬 📚

Some Interesting Reads:

A great breakdown of World Models from , is well worth the read alongside the World Model Research Archive he’s put together. The Archive will definitely keep me busy for a few weeks.

Not Boring by Packy McCormick

World Models: Computing the Uncomputable

Welcome to the 458 newly Not Boring people who have joined us since our last essay! Join 260,170 smart, curious folks by subscribing here…

Read more

3 months ago · 252 likes · 10 comments · Packy McCormick

• An interview with Tractian CEO, Igor Martinelli, around the future of physical AI and the playbook that has fueled Tractian’s rapid growth. [NGP Capital]

• GM launched its engineering blog this past week with a few interesting blog posts on its approach to scalable autonomy [GM]

• A great breakdown from Aaron Slodov on what to read around reindustrialization and manufacturing. [X]

• Skild is deploying its models with ABB, UR, and NVIDIA. [Skild]

• An interview from CES with Mikell Taylor, head of robotics strategy at General Motors, on the future of robotics. [McKinsey]

• The Chinese adjustment to the rebalancing of supply chains. [Fortune]

• A new market map with 50 Physical AI startups [Bessemer VC]

  

• Uber is investing up to $1.25 billion in Rivian to deploy autonomous robotaxis [Rivian]

• Jeff Bezos is raising $100 billion for a new fund that would roll up manufacturing companies and integrate AI. [WSJ]

• Here are some of the highlights from NVIDIA GTC:

  • Built on NVIDIA Warp and OpenUSD NVIDIA announced Newton 1.0 GA a production-ready foundation for dexterous manipulation and locomotion tasks.

  • The NVIDIA CloudXR 6.0 suite of streaming technologies, now natively integrated with Apple Vision Pro to enable spatial interaction with apps like Immersive for Autodesk VRED.

  • A Roche case study demonstrating the use of NVIDIA Omniverse libraries to build digital twins of production facilities to simulate and optimize complex systems before they go live.

  • McLaren is leveraging the Rescale digital engineering platform powered by NVIDIA to reduce simulation times.

  • A detailed breakdown of how robotics companies are using Newton and Groot to build robotics with AI.

  • NVIDIA is partnering with Cadence, Dassault Systèmes, PTC, Siemens, and Synopsys to bring GPU-accelerated tools and Omniverse to major manufacturers, including Honda, Mercedes-Benz, Samsung, TSMC, and PepsiCo.

Products & Research:

Jiga, put together a guide on the 7 DFM principles that should underpin every design decision. Covers why manufacturability needs to happen early, not as a final check, and how to work with suppliers before your leverage disappears. Check it out here.

• Velotic launches, combining Proficy, GE Vernova’s former manufacturing software business, and PTC’s ThingWorx and Kepware businesses. [TPG]

• PreScalar, a new AI and automated-testing platform for manufacturing, launches! [PreScalar]

• High availability in Litmus Edge is now available. [Litmus]

• Fictiv introduces Instant Injection Molding Quoting [Fictiv]

• An interview as part of YC’s Hard Tech, with the founders of Solugen talking through their enzymatic and catalytic production process, the transition from a $7K PVC reactor to a scaled plant in Houston, and the future of American manufacturing. [YC]

• Databricks demoed a case study alongside Toyota for its new real-time streaming product Zerobus Ingest, integrated with global IoT connectivity from Soracom, to mitigate maintenance costs and transform its manufacturing operations. [Databricks]

Blogs / Threads

Part 2, and a must-read, on PoC Valley in Industrial Tech from

Deploy 95

Surviving the POC Valley

This is Part 2 of a six-part series on selling technology with a physical footprint into legacy industries: manufacturing, industrial, and operationally complex enterprises…

Read more

3 months ago · 7 likes · Trista Li

Finance & Transactions 💵

Roboforce raised $52M from YZi Labs
Robotics / Physical AI • Series A • USA

Solugen raised $50M from Idealist Capital and Canada Growth Fund
Chemical Manufacturing • Growth • USA

Tenkara raised $7M from True Ventures
Manufacturing AI • Seed • USA

Planned Downtime 🧑‍🔧

Dune Part 3

Breaking the Bottleneck is brought to you with support from our partners.

• Jiga - Is a platform that connects hardware teams with vetted manufacturers for custom parts. Instead of black-box instant quotes, you see exactly who’s making your parts and can talk to them directly.

• Industry 4.0 Club - Leading cross-functional group accelerating the adoption of Industry 4.0, delivering better experiences to consumers, better profits to manufacturers, and better jobs to factory workers.

• Ironloop - Ironloop is a modern OT/ICS software platform that delivers configuration-level intelligence, giving manufacturers ground-truth security visibility and compliance confidence across air-gapped networks.

• T51 - T51 is an AI-native planning, scheduling, and optimization platform that integrates securely with your existing ERP, MES, and data stack.

One Big Thing

A bit late to the party on this one, but I came across SEG's State of Industrial and Manufacturing Software report and wanted to share a few standout slides. Check out the full report here, and also their AI Reset report: How SaaS Founders Can Reinvent, Defend, or Exit Stronger.

• The tariff parallels are hard to ignore, with the same headwinds hitting the manufacturing sector in 2025 looking remarkably similar to those in 2019.

  

• The modern hybrid industrial architecture is also evolving well beyond the rigid ISA-95 and Purdue hierarchies, increasingly designed to enable AI-driven orchestration across the stack.

  

• And on the M&A side, buyers are placing a greater premium on operational intelligence, building end-to-end platforms rather than pursuing indiscriminate roll-ups, a shift that also reflects the very real pain of post-merger integration.

  

Content I Enjoyed Last Week 🗞️🔬 📚

Some Interesting Reads:

A fascinating breakdown of China's embodied intelligence push. After Beijing officially designated "embodied intelligence" as one of six priority industries in its 2026-30 five-year plan, there has been a proliferation of humanoid robot training farms staffed by young graduates who spend their days manually piloting robots through repetitive household and industrial tasks. The Hubei Humanoid Robot Innovation Center in Wuhan, for example, produces roughly 100 hours of labeled robot-motion data daily across 46 robots and 70 human instructors, though how transferable that data will be across different hardware platforms remains an open question. [FT]

• The DOE announced a funding opportunity of up to $500 million to expand U.S. critical mineral and materials processing and derivative battery manufacturing and recycling. [DOE]

• Schaeffler is currently using Agility Robotics’ Digit humanoid to handle repetitive material transfer tasks at an auto parts plant. The displaced worker was moved to a higher-skilled inspection role, a narrative that will face real scrutiny as deployments scale. [WSJ]

• BMW’s use of Physical AI [Manufacturing Digital]

• The Anybotics CEO on the importance of full-stack security ownership with robot deployments, from the sensor hardware through to the cloud, citing Unitree’s recent backdoors as a cautionary tale. [Robot Report]

• ABB deploys NVIDIA Omniverse into its RobotStudio programming and simulation suite, driving an improvement in development and deployment costs [NVIDIA]

• Ahead of GTC, some examples of NVIDIA’s Omniverse being deployed in the real world, though little mention of ROI for the highlighted projects [NVIDIA]

• An assessment on where AI can play a role in the additive ecosystem, namely, closed-loop process control and machine orchestration [TCT]

• Worldwide purchases of raw materials, commodities, and critical components rose at the fastest pace in almost four years, according to the GEP Global Supply Chain Volatility Index, though primarily driven by Asian demand. [GEP]

• The impact of increasing fuel and diesel prices on the industrial landscape [WSJ]

• Another surprising side effect of the Iran War on Stryker’s operations [WSJ]

• GE Aerospace to invest $1B in American manufacturing [GE]

• KPMG with a thought piece on AI in drug manufacturing and its value in closed-loop process control, smart monitoring and maintenance, and supply chain resilience. [KPMG]

• Radical AI's mechatronics engineer Matthew Borgatti lays out the case for soft robotics. Check out their discovery and prototyping process here! [Radical AI]

Products & Research:

Harbinger unveiled the HC Series Cab, a new breed of medium-duty, low-cab-forward (LCF) vehicles available now for order as both an electric and plug-in hybrid model

• Microsoft launched Phi-4, a compact, smart open‑weight multimodal reasoning model that leverages carefully curated training data and synthetic reasoning traces to improve model performance. [Microsoft]

• Autodesk launches Wonder, its Text and Image to 3D product, enabling users to quickly generate 3D characters or objects from a simple text prompt or a reference image [Autodesk]

• AI Build launches an OS that integrates work processes across CAD, CAE, and CAM, and largely automates them

• Synopsys launched a digital twin for high-value automotive use cases, with companies like Volvo leveraging the platform for 'shift left' test and validation [Synopsys]

• Crusoe launched Spark Factory, a turnkey, prefabricated modular AI factory to bring low-latency compute to the network’s edge. [Crusoe]

• GenCAD, an image-conditioned CAD generation model [GenCAD]

Blogs / Threads

A great thread on the impact of the war on Helium from Flexport CEO Ryan Petersen

An interesting thread on the equipment financing from Denver Rayburn

Another great blog post on Simulation from

AI Opportunities

AI-Physics Simulation Opportunities

This is part two of a two part series (though I may end up expanding it further eventually). You can find part one here. This is something I’m still learning about. I’m early in my understanding here, so consider this an exploration of recent conversations with some experts in the field as well as what I’ve been reading and listening to…

Read more

3 months ago · 13 likes · 5 comments · Annelies Gamble

Finance & Transactions 💵

Mind Robotics raised $500M from Accel and a16z
Robotics / Physical AI • Series A • USA

Rhoda AI raised $450M from Premji Invest and Capricorn Investment Group
Robotics / Physical AI • Series A • USA

Sunday Robotics raised $165M from Coatue
Robotics / Physical AI • Series B • USA

Oro Labs raised $100M from Goldman Sachs and Brighton Park Capital
Procurement • Series C • USA

Isembard raised $50M from USV, Tamarack Global, and IQ Capital
Robotics / Physical AI • Series A • UK

BackOps raised $26M from Theory Ventures
Supply Chain • Series A • USA

Axiomatic AI raised $18M from Engine Ventures and Kleiner Perkins
Testing and Verification • Seed • USA

Planned Downtime 🧑‍🔧

An Interview with the founders of

Breaking the Bottleneck is brought to you with support from our partners.

• Jiga - Is a platform that connects hardware teams with vetted manufacturers for custom parts. Instead of black-box instant quotes, you see exactly who’s making your parts and can talk to them directly.

• Industry 4.0 Club - Leading cross-functional group accelerating the adoption of Industry 4.0, delivering better experiences to consumers, better profits to manufacturers, and better jobs to factory workers.

• Ironloop - Ironloop is a modern OT/ICS software platform that delivers configuration-level intelligence, giving manufacturers ground-truth security visibility and compliance confidence across air-gapped networks.

• T51 - T51 is an AI-native planning, scheduling, and optimization platform that integrates securely with your existing ERP, MES, and data stack.

💥 If you want to chat, feel free to reach out at aditya@machinafactory.org.

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From Paper to AI, Building Software That Frontline Workers Actually Want 🎙️💬

Nick Haase is the co‑founder of MaintainX, the leading maintenance and frontline work execution platform. He has spent thousands of hours on the shop floor helping businesses transform their operations with intelligent, frontline‑friendly software. He regularly writes and speaks about digital transformation.

He is the host of #TheWrenchFactor, a LinkedIn Live series exploring the emerging trends and technologies shaping the future of industrial operations and asset management.

Follow @MaintainX to get notified when the next episode goes live.

You came from private equity and consumer startups like Loot! before co-founding MaintainX. What was the "magic moment" when you saw an opportunity to impact factory workers, and how did that shift your understanding of what industrial software needed to be?

I was doing consulting work for Fortune 100 companies on future technologies, and I came into manufacturing with a Silicon Valley mental model of “digital transformation.” I assumed the ecosystem would be much further along.

Then I started spending time inside plants, including best-in-class operations, and I was genuinely surprised by how much critical work was still done on pen and paper. That was the moment. It made something obvious feel urgent: you cannot become data-driven if you are not capturing the data digitally.

The more time we spent with teams, the clearer the stakes got. Labor constraints. Tribal knowledge aging out. And this wave of AI coming fast that will only be as good as the foundation beneath it. If your foundation is still paper, you end up with a huge chasm and a missed opportunity.

It also reshaped my view of industrial software. It cannot start as a corporate initiative and hope it trickles down. It has to start with the people doing the work, and it has to be simple, practical, and helpful in the flow of a shift.

You've said digital transformation is a "sales job" where shop floor workers are the customers, not just stakeholders. How do you handle resistance on the factory floor? What specific product features or behavioral tactics have actually worked to win over skeptics?

Most resistance is rational. People have seen tools come and go, and they have been asked to do extra work “for visibility” that does not help them.

So I start with one question: “What’s in it for me?” The frontline does not wake up excited to make leadership’s dashboards prettier. If the product does not make their day easier, it will not stick.

What works is reducing friction in ways technicians can feel immediately. Less chasing information. Less rework. Faster shift handoffs. Clearer context attached to the job. Fewer interruptions because the history lives with the asset, not in someone’s memory.

And the behavior matters too. You have to listen first, improve based on what you hear, and make wins visible in a way that feels supportive, not surveillant. The shop floor knows instantly whether you built something for them or for someone above them.

MaintainX scaled to 10,000+ customers initially using product-led growth in an industry notorious for multi-year sales cycles. How did you architect a bottom-up adoption model early on that forced corporate buy-in from the floor up, and what's the one friction point that still kills PLG motion in conservative manufacturing environments?

We built for the frontline worker first. The person with no budget authority, but who determines whether these initiatives succeed or fail.

Early on, the goal was to make the tool useful for a technician as an individual and as a team, and then make the value compound as more people participate. When that is true, adoption naturally moves upstream because the improvement shows up in the work itself.

Some of our strongest enterprise deployments started with a single champion at a single plant. Someone frustrated with the status quo who just wanted to make life easier for their team. The crew adopts it. The plant manager notices things are running better. Then regional leaders see the lift and want to scale it.

The friction point that can still kill PLG is the moment IT gets involved. And they are right to care. Nobody wants unvetted software inside an enterprise. So bottom-up only works at scale if you can quickly earn IT’s trust with security, controls, and real partnership.

You advocate for "Standardize to Scale" and digitizing tribal knowledge before veterans retire. But capturing undocumented intuition is incredibly hard. How can AI extract the "quirks" and edge cases that a 40-year veteran just knows? What's being lost in translation when we digitize expertise?

You cannot capture a veteran’s entire brain by asking them to list everything they know. The real context shows up at the point of work, when they are diagnosing a failure, making a repair, or handling an edge case that never appears in a manual.

So step one is making it easy to capture that context in the moment, in whatever format is natural. A quick note. A photo. A voice message. A few lines added to a checklist. Lightweight, immediate capture is how the nuance survives.

There is also a clear benefit for the veteran. It creates a record they can reference later, and it helps colleagues on other shifts solve issues without waking someone up at 2 a.m.

Where digitization can go wrong is when it flattens nuance into rigid structure. Some knowledge is procedural and should be standardized. But a lot of expertise is contextual judgment. The goal is to preserve that context and make it usable, not reduce it to a dropdown.

With recent notes on multi-agent failure detection and LLM-generated data models, you’re clearly moving toward “agentic” maintenance. What does the technician’s role look like in five years? What guardrails need to be put in place around the agent-human workflow?

There is incredible innovation happening in AI right now, but manufacturing is not an environment where you can tolerate risky failures. People can get hurt. Downtime is expensive. So the next five years look like trust but verify.

I think technicians will have faster access to the right information, clearer recommendations, and more proactive support, but still remain the decision-makers. AI should reduce search time, surface relevant history, and suggest next steps, not pretend certainty where it does not have it.

Guardrails start with grounding and transparency. Where did this recommendation come from? What evidence supports it? How confident is it? And can the system say “I don’t know” when it cannot answer safely?

That is how we think about it. Our AI tools should not roam the open internet or pull from random forums. They should rely on trusted, context-specific sources like manuals, work history, and asset data. If the answer is not grounded, the system should be explicit and route the user to the right human.

And in five years, technicians are still central. Even in more automated plants, humans remain the best mechanics and problem-solvers for keeping complex systems running. AI should amplify that craft, not replace it.

To contact Nick, reach out to him on LinkedIn here. He’s always happy to help and share more about MaintainX.

Breaking the Bottleneck is a weekly-ish newsletter and interview series about manufacturing and physical AI. If you want to chat, feel free to reach out at aditya@machinafactory.org or on LinkedIn here!

One Big Thing… Physical AI

Physical Intelligence’s π0.6 model with Multi-scale Embodied Memory (MEM) tackles one of robotics’ hardest unsolved problems, keeping a robot coherent across tasks that span minutes, not seconds. MEM combines a video encoder for short-term frame-based memory with a language-based system for long-term task tracking, letting π0.6 handle tasks up to 15 minutes long. Embodied approaches are still roughly 12 to 18 months behind their non-embodied counterparts, but memory-driven in-context adaptation is a genuinely exciting step forward. It gives robots the ability to recognize a failed attempt and adjust on the fly, rather than blundering into the same mistake twice.

The model has been validated in real commercial deployments. At Weave's laundromat operations, π0.6 cut missed grasps by 42% and human interventions by 50%. At Ultra's e-commerce warehouses, one deployment ran an entire shift at 96.4% autonomy, though classical picking systems have cleared that bar for years. The headline autonomy number isn't really the point. What matters is how π0.6 handles the long tail of edge cases compared to first-generation vision-based methods, and whether it can reduce deployment costs while making robots genuinely easier to stand up in real industrial environments. That's where I'm hoping memory can make a real dent.

Content I Enjoyed Last Week 🗞️🔬 📚

Some Interesting Reads:

A great review from breaking down what Elon Musk has called the hardest unsolved problem in humanoid robotics. Link Here!

• Google revealed its 2026 AI Agent Trends in Manufacturing [Google]

• Baukunst has a pretty neat breakdown on the future of Manufacturing and AI, with perspectives from a few new startups. [Baukunst]

• An interesting read from the President of Tokai Carbon on AI use case development and AI ROI in manufacturing. [Chief Executive]

• Revel raises again from Index Ventures. I’m a huge fan of this market, and it’s been crying out for disruption. Nominal and Revel are two great new products going after LABVIEW. Check out Nominal’s recent raise here!

• For you CAD and modeling nerds, an awesome breakdown of two companies, Rapid Liquid Print and Variant3D, and their applications, modeling approaches, and product strategies [Blake Courter]

• Former OpenAI head of research launching a new platform to automate manufacturing [WSJ]

• A pretty awesome map of CAD modeling software, their underlying geometry kernels, and the companies that own them! [Conor James Kapoor]

• A great breakdown of the practical realities of deploying automation in manufacturing within the United States. [NY Times]

• Another OpEd on the realities of bringing back domestic manufacturing on domestic jobs [Bloomberg]

• Another example of the impacts of a misguided manufacturing policy, especially given the impending energy crisis [Manufacturing Dive]

• Though a nominal percentage of its supply chain, early green shoots are emerging from Apple’s effort to reshore its chip supply chain. [WSJ]

• ASML found a way to boost the power of the light source, driving a 50% improvement in yield [Reuters]

Products & Research:

Misumi and Fictiv released their 2026 State of Manufacturing & Supply Chain report. Some highlights include:

• Nearly all of the 300+ manufacturing leaders surveyed (99%) say supplier tariff and trade expertise is now essential in partner selection, and 98% report that rising raw-material costs are driving more resilient sourcing strategies

• 95% AI and automation are helping address workforce shortages, but cannot replace specialized expertise, with many leaders anticipating productivity gains of 50% or more.

• 77% say trade compliance requirements are too complex to manage without external expertise

• 83% of engineers spend four or more hours per week on procurement-related workflows

• 98% see clear optimization opportunities, particularly in quality management, supply chain design support, and DFM, costing, and engineering services

• New Model-Based Definition (MBD) updates to Onshape [Onshape]

• Vinci announced the launch announced the availability of its second core physics capability: thermo-mechanical simulation [Vinci4D]

• An interesting research project available on Hugging Face, where David and Devon trained on a publicly available dataset of one million parametric parts to enable search across designs. [Final Rev]

• i3x launches as an open, common API initiative proposed to address a growing interoperability challenge in modern manufacturing architectures [HighByte and CESMII]

• A new chemical digital twin from Berkeley that allows a real-time analysis of chemical compounds and offers feedback in real time, enabling researchers to compress discovery timelines. [Berkeley Lab]

• Dexterity revealed a state-of-the-art world model and a 4D box packing agent. I’m curious about the economics and necessity for world models here. [Dexterity]

Build Real Supplier Relationships With Jiga - Most hardware teams are stuck choosing between two bad options: instant-quote platforms that hide suppliers behind a black box, or local shops that hit capacity walls right when you need them most. One trades away relationships for speed. The other offers trust but can’t scale.

Jiga gives you both. Direct access to vetted manufacturers with reliable capacity from prototype through production. You speak directly to the people making your parts. And when you come back, they already know your specs and technical needs. Same shop, same knowledge, consistently reliable quality without starting over every time.

Podcasts / Events

An awesome, in-person panel next Monday at NVIDIA GTC from the world’s leading robotics companies on the future of physical AI, and what it will take to unlock truly capable humanoids.

Scaling AI at Audi [Industrial AI Podcast]

Blogs

A great read for anyone building at early stage startups on industrial software GTM with

Deploy 95

Introducing the POC Valley

This is Part 1 of a six-part series on selling technology with a physical footprint into legacy industries: manufacturing, industrial, and operationally complex enterprises…

Read more

4 months ago · 21 likes · 4 comments · Trista Li

A great blog post on PINNs and Simulation from

AI Opportunities

PINNs, Neural Operators, and the Future of Simulation

This is part one of a two part series. This is something I’m still learning about. I’m early in my understanding here, so consider this an exploration of recent conversations with some experts in the field as well as what I’ve been reading and listening to…

Read more

4 months ago · 6 likes · Annelies Gamble

Finance & Transactions 💵

Axelera AI raised $250M from BlackRock and Innovation Industries
Semiconductors • Growth • USA

Nominal raised $80M from Founders Fund
Testing • Series B2 • USA

Revel raised $150M from Index Ventures and Redpoint Ventures
Testing • Series B • USA

Encord raised $60M from Wellington Management, YC, and CRV
Robotics / Physical AI • Series C • USA

Flux raised $37M from 8VC
ECAD, Design Software • Series B • USA

Planned Downtime 🧑‍🔧

F1 is Back, Wired Questions with a Chief Mechanic

New Rules Explained

Breaking the Bottleneck is brought to you with support from our partners.

• Jiga - Is a platform that connects hardware teams with vetted manufacturers for custom parts. Instead of black-box instant quotes, you see exactly who’s making your parts and can talk to them directly.

• Industry 4.0 Club - Leading cross-functional group accelerating the adoption of Industry 4.0, delivering better experiences to consumers, better profits to manufacturers, and better jobs to factory workers.

• Ironloop - Ironloop is a modern OT/ICS software platform that delivers configuration-level intelligence, giving manufacturers ground-truth security visibility and compliance confidence across air-gapped networks.