Who Is Wiley Jones?
Wiley Jones is a hardware engineer who spent years building physical products in Chinese factories, shipping medical devices at Athelas and security cameras at Verkada, before deciding that the operational software those businesses depended on was the real broken thing. He co-founded DOSS with Arnav Mishra to rebuild ERP from first principles — not by adding AI to an existing system, but by fusing intelligence into the data architecture itself. The company just raised $55 million.
What makes Wiley unusual among enterprise software founders is how he thinks. He doesn't pitch. He teaches. In our conversation, he moved fluently between 200-year-old control theory, the composable data models that PeopleSoft built in the 1990s, and a thought experiment about a bomb diffusion robot deciding whether to reveal classified information. He sees the current AI moment as comparable to the discovery of fire and the steam engine — not as a marketing line, but as a historically grounded position he can defend with specifics.
He also deliberately removed the word "AI" from the DOSS website because his customers — operations and finance professionals running physical supply chains — don't care about buzzwords. They care about whether their orders shipped correctly. Building architecturally AI-native software while marketing it as simply "the thing that works" is a tension that runs through everything Wiley does.
The Archetype: The Sage
The Sage
The Creator
Tests & Allies
Wiley's core drive is understanding how things work at the deepest level and making that understanding accessible. His most animated moment in our conversation wasn't about a customer win or a fundraise — it was about control theory. "Software engineers are re-deriving control theory from first principles," he said, visibly excited. "We discovered this 60 years ago — 200 years ago, the Russian scientists that figured out stable control systems and robustness and how you can create bounded outputs on unbounded inputs."
This is the Sage at work: someone whose deepest satisfaction comes from comprehending the system, not just operating within it. Wiley builds taxonomies the way other founders build pitch decks — computing as "nouns, verbs, and adjectives," the build-vs-buy decision as a matrix of error-cost vs. worth-building, hallucination reframed as a selection error in a control loop. Every framework makes a complex idea navigable.
His secondary archetype is The Creator. Wiley doesn't just understand systems — he builds them. His own domain-specific language (ZSL), his own transactional data warehouse, his company's internal shared brain, the vision of self-evolving software — these are acts of creation that make the Sage's understanding tangible. The two archetypes feed each other: the understanding informs what to build, and the building tests whether the understanding is real.
"I think a lot of people who are in kind of our generation will be like, those products suck," he said about legacy enterprise systems like PeopleSoft and Salesforce. "Yeah, but there's a reason that they're so composable and that they're in every enterprise. It's because they built something extremely powerful. And it's really hard to do that."
The Hero Match
Daedalus
In Greek mythology, Daedalus was the master craftsman and inventor — the architect of the Labyrinth, the creator of wings, the builder who saw the underlying structure of things and created solutions that operated on a plane others couldn't reach. He wasn't a hero because he led armies or defeated monsters. He was a hero because he understood how systems worked at a level no one else could match.
Wiley maps to Daedalus through something specific: the intermediate representation he built for DOSS. It's a language that lives "halfway between reading a code base and reading a markdown" — neither documentation nor source code, but a structured, graph-like system that lets software reason about itself. That's a Daedalian creation: something new that bridges two existing categories and makes possible what neither could achieve alone. The Labyrinth wasn't just a maze; it was an architecture that served a function no building had served before.
The other Daedalus parallel is more subtle. Wiley carries knowledge that a new generation of builders has forgotten. His observation that software engineers are re-deriving control theory from first principles — and finding it "really funny" rather than frustrating — is the master craftsman recognizing that the principles endure even when the practitioners have to rediscover them. He doesn't hoard that knowledge. He teaches it.
Tony Stark in the Cave Workshop — Iron Man (2008)
Not the billionaire showman of later Marvel films. The version of Stark who is locked in a cave with scrap metal and sees the architecture of something no one else can see. Wiley took the "scrap metal" of existing enterprise software — ERP systems from the 1960s through 1990s that he genuinely admires — and rebuilt them from first principles using composable, AI-native architecture.
The cave-Stark parallel also captures how Wiley presents his work. He described his company's most important internal tool — a shared memory system that indexes everything across Slack, Salesforce, call notes, and Google Drive into an agentic search engine — as "really, really dumb." That's cave-Stark energy: build something brilliant and call it simple, because the elegance is in the solution, not the presentation.
"SaaS was an embarrassment of riches, where we basically could throw eight gig RAM at a simple CRUD app. You didn't have to care about how any performance of anything worked."
The Story Behind DOSS
Wiley's path to enterprise software started on a factory floor. He spent years building physical products — robots in Chinese factories, medical devices at Athelas, security cameras at Verkada — and kept running into the same problem. The operational software those businesses depended on was the weakest link. Planning happened on a fixed Wednesday schedule whether you were producing 40,000 units a year or a million Roombas. The systems were rigid, the implementations took months or years, and the intelligence sat in the consultants' heads, not in the software.
The moment DOSS became inevitable was when Wiley saw GPT-2 transition to GPT-3 and watched a language model write SQL for the first time. "Oh my god, it's going to work," he thought. He'd estimated it would take ten years for AI to be good enough to power self-evolving enterprise software. It took four. He and co-founder Arnav Mishra built DOSS with a bet that most people in enterprise software thought was reckless: rebuild ERP from zero, don't wrap AI around the existing thing.
The bet required inventing new infrastructure. Wiley built his own transactional data warehouse (Postgres, Iceberg, Data Fusion, DuckDB), his own workflow automation engine (a Zapier equivalent), and his own domain-specific language — ZSL — that functions as "Terraform for the application layer." When he explains the architecture, the excitement is palpable. He leans forward, the pacing quickens, and the analogies multiply. This is a founder who is building because the building itself is the point.
The Founder's Journey ↔ The Company's Journey
Hardware engineer in Chinese factories who saw operational software as the weak link, watched GPT-2 to GPT-3 and realized AI could close the gap, bet on rebuilding ERP from zero, assembled a team that values systems thinking over credentials, now deep in the building phase with conviction that this moment is as significant as fire and the steam engine.
Founded 2022 from the insight that ERP's consulting model was broken, built composable AI-native architecture from first principles, landed ~30 customers (Verve Coffee, Eight Sleep, Mezcla), raised $18M Series A from Theory Ventures, acquired Genie for Shopify distribution, shipped DOSS 2.0, raised $55M Series B, now building toward self-evolving software that reduces implementation from months to days.
The Sage who studied centuries of technology history is building the company that applies those lessons. Wiley's admiration for PeopleSoft and Salesforce's composable data models from the 1980s and 1990s isn't nostalgia — it's the foundation. DOSS is what those systems would look like if they were rebuilt with modern AI capabilities, by someone who understood why they were powerful in the first place.
How Wiley Leads
Wiley leads with frameworks. When a customer objects that consolidating their tech stack is impossible, he doesn't argue — he reframes: "Then you're probably going to stick your business at the current size it's at because either you will continue to grow in the tools that you have, or you will consolidate them." When his team evaluates whether to build or buy a tool, he constructs a matrix: does correctness matter (Waymo: don't build it yourself) and is it worth the effort (Calendly: just buy it). When explaining how DOSS's AI handles hallucination, he collapses the entire concept into control theory: "Hallucination is actually just a selection error. We deviated from our target and we will get back to that and then that becomes feedback."
His hiring philosophy follows the same first-principles logic: "Creativity, agency, judgment. Those are the main things that matter now." He doesn't care about academic credentials or specific technical skills — "Do I care if someone knows PyTorch or CUDA kernels? I don't care. AI will be better at writing all that code than you in a year anyways." What he tests for is whether a candidate can hold a complex idea in their head, break it down, reason about each piece, and sequence them. He'd rather have fewer people, paid more, who think in systems.
Founder Superpowers
Compressing Complexity into Frameworks Live
Wiley doesn't recite pre-packaged analogies — he generates frameworks in real time to match whoever he's talking to. When I asked why demos take two hours but implementations take three months, he built the homework analogy on the spot: "You do one example problem with the teacher in class... And they're like, okay, cool. Now here's 40 homework questions." When explaining computing primitives, he reduced them to "nouns, verbs, and adjectives." When mapping build-vs-buy decisions for executives, he constructed a matrix with Waymo and Calendly as anchors. Each framework was tailored to the conversation, not retrieved from a slide deck.
Seeing Historical Patterns Across Centuries
Most founders reference recent startup history. Wiley maps current technical problems onto parallels spanning decades and centuries. He cited the Russian scientists who figured out stable control systems 200 years ago, the composable data models PeopleSoft built in the 1980s, and the reorganization of society after the industrial revolution — all to explain what's happening in enterprise software right now. "The last best time was when we discovered the steam engine. Before that was when we discovered fire." This cross-temporal pattern recognition lets him build on foundations that most current engineers have forgotten exist.
Reframing Problems to Eliminate False Categories
The hallucination-as-selection-error reframe was the sharpest intellectual move in our conversation. Wiley took a concept that makes most people anxious and collapsed it into something precise and solvable: "Hallucination is actually just a selection error... we deviated from our target and we will get back to that and then that becomes feedback." He does this consistently — stripping away misleading labels to expose the real mechanism underneath, then building the solution on the mechanism instead of the label.
What It's Like to Work with Wiley
Working with Wiley means working with someone who thinks in systems. Every problem gets categorized before it gets solved. Every analogy is purpose-built. Every conversation is an opportunity to build a framework that the team can use going forward. He described the ideal role of humans in AI-native systems as "gardeners of sorts, cultivating and letting these systems flourish and grow, but we're not going to be sitting there watching the grass grow." That gardener metaphor likely extends to how he manages people — set the conditions for growth, provide clear directives, then let talented people find their own way to the goal.
He values transparency. The shared brain system he built for DOSS — an internal knowledge engine that indexes Slack channels, call notes, Salesforce, and Google Drive — runs on a principle he enforces culturally: "If you're talking about certain topics, do not put them in DMs, put them in public channels so that we can all benefit from seeing them." He wants an organization where information flows openly and people can access what they need without asking permission.
"Hire better people, pay them more, have less of them." That's the team philosophy in one sentence. He hires for how people think — "can they hold a complex idea in their head, break it down into pieces, reason about each piece and then sequence them" — not for what languages they know or what companies are on their resume. For someone who values systems thinking, judgment, and intellectual honesty, this is likely a high-intensity, high-autonomy environment where the expectation is that you figure things out and bring your reasoning, not just your output.
"Deeply understanding people and how to solve their problems is still very durable. That will be durable for a very long time."
Why This Matters (For You)
If You're Running a Physical Operations Business
Wiley's core insight is that the operational software most physical-product companies run on was designed for accountants, not operators. If you're managing inventory across multiple sales channels, wrestling with procurement across suppliers, or trying to get a single view of your operations without stitching together five different tools — the pattern Wiley described is your pattern. His framing of "pools of pain" in B2B is worth sitting with: the companies that win are the ones that go where the problems are and deeply understand how the pain maps across the ecosystem, not the ones with the best pitch deck. Whether or not you use DOSS, the question Wiley raises is worth asking: is your current operational software actually adaptive, or is it a rigid system you've learned to work around?
If You're an Engineer Building AI-Native Enterprise Software
Wiley's architecture decisions reveal a philosophy that most AI application builders haven't internalized. The intermediate representation — a structured, graph-like layer that lives between the code base and documentation — lets the system reason about itself. "If you could only take Cursor, Claude Code, ChatGPT, Codex, whatever, and could only run them on the logs, that would be much less useful than actually having them be able to access the code base." His argument is that AI-native means the application and its lifecycle are fused into one runtime, not that you bolted a chatbot onto an existing interface. If you're building enterprise AI applications, ask yourself: can your system introspect about its own implementation? Can it create a branch of itself, test a change, and flag a human when the change touches a hot path? If not, you're building AI-assisted software, not AI-native software.
If You're Early in Your Career
Wiley's career path is a lesson in following the structural problem rather than the obvious one. He didn't go from hardware engineering to enterprise software because it was a logical career move. He went because he kept seeing the same broken operational systems at every company he worked at, and eventually decided to fix the underlying cause rather than keep working around it. His advice on finding direction was characteristically unsentimental: "You can't read a book and it's going to change your view on the world. This is not how things work. You have to find, figure out inside yourself what the thing you're looking for is." And on what to learn: he doesn't care about specific technical skills — "AI will be better at writing all that code than you in a year anyways." What he hires for is the ability to hold a complex idea, break it down, and reason about the pieces. Systems thinking, not syntax.
If You're Considering Joining DOSS
Wiley builds for people who think in systems and value intellectual honesty. "Creativity, agency, judgment" — those are his hiring criteria, in that order. He doesn't care about your academic pedigree or which frameworks you know. He cares about whether you can reason through a novel problem and make good decisions under uncertainty. The company runs on radical transparency — public Slack channels feeding a shared knowledge system, open calendars, a culture where information flows to where it's needed. The team is deliberately small ("hire better people, pay them more, have less of them"), which means high autonomy and high expectations. If you want clear directives, room to figure out the how, and a founder who leads with frameworks rather than mandates, this is that environment.
Go Deeper
The full conversation with Wiley Jones is on its way. Check out other episodes in the meantime.