Simon Lancaster: Manufacturing Is Finally Entering Its Software Era

Simon Lancaster has spent his career at the intersection of hardware, manufacturing, and emerging technology — first inside companies like BlackBerry and Apple, and now as the Founding General Partner of Omni Ventures.

Based in San Francisco, Omni focuses on capital-efficient software and AI companies serving industrial workflows, from engineering and factory operations to supply chain intelligence and robotics orchestration.

“Manufacturing still runs on a lot of Excel, email, and paper,” Lancaster says. “If we can bring industrial workflows closer to the level of digitization we’ve seen in commerce or finance, that is an enormous investment opportunity.”

Where It Started

Growing up in Portugal, Lancaster developed an early interest in building and engineering, with a clear ambition to become an inventor. He later joined BlackBerry, then at the peak of consumer electronics and among the first devices to bring email into your pocket.

“I fell in love with being on the factory floor, solving real problems, engineering, learning something new every day,” he recalls.

By 2007, Lancaster had set his sights on Silicon Valley and secured an internship at Google. That same year, Apple announced the iPhone — a device widely dismissed at the time for lacking basic features like copy-paste and a physical keyboard. Lancaster took what he calls a “risky bet” and joined the company.

At Apple, he became an “expert generalist,” working on emerging technologies across global R&D labs.

“In a roundabout way, I did fulfill my childhood dream. I became an inventor, powered by Apple,” he says.

Over time, his role shifted toward evaluating startup technologies and backing teams — shaping the foundation of his later work as an investor.

In 2019, he left Apple to join Arris Composites, helping scale the company from Seed towards its Series C. He later exited to build his own fund, Omni Ventures.

The Manufacturing Tech VC

Omni Ventures launched in 2021 with a focused thesis: backing companies modernizing manufacturing — one of the largest contributors to global GDP, yet one of the least technologically advanced sectors.

At the same time, recent supply chain disruptions have exposed structural weaknesses in manufacturing, accelerating the shift toward more resilient, localized production.

Lancaster believes the industry is now entering a period of accelerated catch-up. While sectors like finance, commerce, and software have scaled through digitization over the past two decades, manufacturing has lagged behind.

In part, that is due to fragmented data, legacy ownership structures, and slower adoption cycles. In practice, many manufacturers still manage critical processes through spreadsheets and email.

“A common misconception is that modern manufacturing is highly automated. In reality, even at the most advanced companies, there are still critical workflows where human judgment, manual coordination, and fragmented data remain central. ” Lancaster says. “Many of the workflows that matter most in manufacturing still look surprisingly similar to 20 years ago — especially around data collection, engineering coordination, quality, and production planning.”

That is starting to change. Younger operators are driving the adoption of modern tools, while labor shortages are forcing companies to improve efficiency through software, automation, and robotics.

“We’re going to see a massive catch-up in technology adoption,” Lancaster says.

Global Dynamism

For Lancaster, the opportunity is not just reshoring. It is what he calls ‘global dynamism’: rebuilding manufacturing capacity in a more distributed, software-defined, and resilient way across allied markets.”

Rather than replicating China’s mass-production model, the opportunity lies in deploying capital-light technologies — vertical AI, workflow automation, and robotics software — to rebuild manufacturing capabilities in a more distributed, flexible way.

“Are we really going to start making shoes and iPhones in Europe and the US? Not exactly,” Lancaster says. “Not every category will be reshored, and we should not expect the US or Europe to simply recreate China’s mass-production model. The bigger opportunity is to build new manufacturing capacity around the categories where speed, resilience, precision, and national security matter most.”

Backing Near-Frontier Startups

Unlike many deep tech funds betting on long-term moonshots, Omni Ventures positions itself as “near frontier,” targeting technologies that can be commercialized within two years. This directly shapes the type of founders they back.

“We tend to gravitate toward founders who have spent 5-10 years inside the industry,” Lancaster says. “The pattern we like is deep technical fluency combined with firsthand exposure to the commercial pain point”

The typical Omni founder is a mid-career professional in their early-to-mid 30s — deeply technical, but commercially minded, with firsthand experience of the problem they’re solving.

Many founders and investors, Lancaster argues, underestimate the complexity of manufacturing — particularly the difference between capital-light software and capital-intensive industrial systems. That gap makes firsthand experience critical.

“There are many brilliant technical founders who naturally gravitate toward the elegance of the technology. The rare profile is someone who combines that depth with urgency around commercialization.” he says.

This kind of expertise, he notes, cannot be learned from textbooks or online — it comes from years spent inside the industry, on factory floors and in real-world production environments.

The Data Problem

When it comes to AI in manufacturing, Lancaster is blunt about the biggest bottleneck: data.

“Data. Data, data, data. The challenge is not building AI — it’s training it well. There are no large, publicly available datasets for manufacturing — not for process control, quality control, or specifications,” he says.

This creates both a barrier and an opportunity. Companies that secure proprietary data through early design partners and customers can build strong, defensible positions.

But access to that data is difficult — requiring trust, close collaboration, and often creative business models to incentivize early data sharing.

For Lancaster, the companies that win will not simply have better models. They will have better access to proprietary industrial data, better workflows for capturing it, and deeper trust with customers.

Humanoids Won’t Take Over Factories

Lancaster has a contrarian view on one of tech’s most discussed trends: humanoid robots.

“I don’t think humanoids are the end-all, be-all,” he says.

His argument is grounded in a simple observation: specialized tools tend to outperform general-purpose ones.

“We don’t have a dishwasher that washes clothing and dishes and also cleans the floor. We don’t have a car that flies and is also a boat,” he points out. “Despite those things existing, the specialized version of each tool is so much better.”

The same logic applies to manufacturing. Factories are not designed for humans. They are designed for machines. Rather than deploying general-purpose humanoids, Lancaster expects the next wave of automation to be highly flexible but specialized: systems built for specific tasks, capable of adapting quickly without mimicking the human form.

That shift is already underway. One of Omni’s portfolio companies, Rembrain.ai, is building robots that learn tasks by observing humans, enabling more flexible automation across industrial environments.

The Future of Manufacturing

Looking ahead, Lancaster sees the biggest near-term opportunity in the application layer — vertical AI and digital tools addressing decades of underinvestment in manufacturing.

A key area of transformation is the supply chain, where startups are making fragmented, hard-to-access information usable. By applying AI to large datasets — such as millions of electronic components — companies can enable complex searches, substitutions, and optimization tasks that previously required days of manual work.

For Omni, that means staying focused on the earliest stages, where conviction matters most and where a specialized investor can help founders navigate both technical complexity and commercial adoption.

“There’s no more impactful investor than the first high-conviction investor,” Lancaster says.