Who Is Ali Parandeh?
Ali Parandeh teaches companies how to adopt AI. But he refuses to use the tool everyone is hyping. That contradiction is not irony — it's the core of his philosophy. A chartered mechanical and software engineer who spent years as Head of Engineering, Ali launched Build Your AI to help heavy industries (rail, construction, aerospace) understand when to adopt AI and, more importantly, when not to. He runs corporate training through UK professional institutions. He built a requirements management product for engineering teams. And he deliberately does hard things manually: writes without ChatGPT, codes personally, spent three hours learning video editing for a 10-minute video.
In our conversation, Ali explained why. "I don't need to build this software," he said. "I could just sit down and do consulting and training all day. But I love coding. That's where you have the most fun." He speaks like an engineer—frameworks, systems, precision—but thinks like a philosopher wrestling with what expertise means when tools promise to automate it all away.
The Archetype: The Creator
The Creator
The Sage
The Return
The Creator builds. Not just products—meaning. Ali's relationship with artifacts is sacred: "You want to feel like it's yours. You want to be proud of it." He doesn't just ship Build Your AI and move on. He ensures every output reflects his values and standards. Every tool he chooses is evaluated on one criterion: Does it let him infuse quality into the work?
His secondary archetype is The Sage. He teaches. Not to push adoption faster, but to help engineering sectors think clearly about when and how to use AI. He references MIT studies on brain activity. He bookends the conversation with philosophy. He's more interested in explaining principles than selling products.
The Hero Match
Hephaestus
Hephaestus is the blacksmith god—the craftsperson who values the work itself over glory or power. He's precise, methodical, less flashy than his Olympian peers, but his artifacts are legendary. He worked in his forge obsessively, perfecting technique, never rushing. When his work was copied without care, he was offended not because his credit was stolen, but because quality degraded.
Ali matches Hephaestus in three ways. First: the sacred relationship with craft. When he defends why he codes personally, he's channeling the blacksmith's voice: "I don't like AI to do my paintings for me. That's the enjoyment I get... you want to feel a satisfaction from the pain of doing it." Hephaestus would understand this completely.
Second: the insistence on quality over speed. Hephaestus spent weeks on a single artifact. Ali spends three hours learning a 10-minute video. He iterated website copy for two to three days. He wrote his book by hand, not with ChatGPT. That's Hephaestus thinking.
Third: the teaching impulse. Hephaestus didn't just forge weapons—he taught. Ali teaches heavy industries how to think about AI adoption, not pushing the fastest adoption path. Like Hephaestus, he builds in solitude with precision, and he believes the artifact must bear the maker's hand.
Atticus Finch — To Kill a Mockingbird
Atticus embodies the slow, principled approach to hard problems. He doesn't rush. He reads deeply. He thinks before acting. When others panic or chase trends, he asks: "What does integrity demand?" He teaches by modeling thoughtfulness.
Ali is Atticus in three specific ways. First: he models his own principles. He teaches AI adoption through frameworks and case studies, not hype. Second: he believes process matters more than outcome. He works 40-50 hours a week deliberately—no 80-hour burnout. He protects time for thinking, learning, craft. Atticus would respect that discipline.
Third: he's comfortable being unpopular for principles. Atticus defended an unpopular client. Ali refuses to use OpenAI's most popular tool and questions the missionary fervor around AI adoption. Both are the slow voice in a fast room, and both are okay with that.
The Story Behind Build Your AI
Ali worked as Head of Engineering, then stepped back to consult and teach. But he noticed something: companies were adopting the hottest AI tools without understanding their actual needs. "Their data is not safe," he explained, describing engineering companies enrolling employees in Copilot without thinking about what they were building for.
He saw a teacher build an entire app in Lovable—a no-code tool powered by AI. The app worked beautifully in development. The school wanted to scale it. The teacher had no idea how to deploy to Google Cloud or Microsoft Azure. Lovable couldn't help her. The gap between "vibe coding" and "enterprise deployment" swallowed her whole.
This moment crystallized Ali's thesis: AI amplifies human judgment best when it doesn't replace it. The teacher needed to understand deployment. The manufacturing engineer needed to understand data safety. The aviation company needed to understand regulatory requirements. Tools could assist, but humans had to remain in the loop—not because AI was incapable, but because expertise cannot be outsourced at the decision level.
The founder's journey: Started as a systems engineer → became Head of Engineering → stepped back to teach and consult → launched Build Your AI to help industries adopt AI correctly.
The company's journey: Corporate AI training (immediate revenue) → enterprise requirements management product (long-term defensibility) → building distribution through professional institutions (compliance-ready pathway) → vendor lock-in and regulatory moats.
The parallel is exact: Ali's personal evolution mirrors Build Your AI's strategy. He moved from building things fast to understanding how things should work. The company does the same—it doesn't chase speed; it builds for safety, compliance, and human understanding.
How Ali Leads
Ali owns his decisions. When he describes past choices, it's always "I decided," never "we kind of fell into it." He decided which tools to use based on ecosystem fit. He decided to bootstrap instead of raising VC. He decided to code personally even when he could hire purely. That clarity is rare.
But he's not ego-driven. He states positions directly without hedging—"Copilot is not very good"—but admits when he's not expert ("copywriting is new to me"). He references influences: Daniel Priestley's business frameworks, his girlfriend's therapy expertise, Chip Huyan's AI books. He attributes success to systems and effort ("I learned it," "I quality-controlled it"), not luck.
His core conviction is this: Scale without quality is empty. He could grow Build Your AI faster by delegating everything. Instead, he works 40-50 hours a week and protects time for craft. He could automate his entire workflow. Instead, he deliberately does hard things manually.
Founder Superpowers
Translating Complexity into Principles
When Ali explains enterprise AI adoption, he doesn't list features. He extracts the principle. "AI is like a calculator for human information," he said. "Overusing AI is like eating junk food—it degrades your body and brain health." Delegating everything is "exploding the kitchen with mess"—you've got to clean up between dishes.
These aren't metaphors for charm. They're teaching tools that stick. When a manufacturing engineer hears "AI is a calculator," they stop chasing autonomy and start asking the right questions. His corporate training works because companies leave understanding how to think, not just what tools to use.
Building Credibility Through Constraint
Most founders in his space showcase speed. "I built this in a day with AI." Ali showcases the work. "I spent three hours learning a 10-minute video." "I iterated copy for two or three days." "I wrote my book by hand."
This isn't a weakness—it's a positioning superpower. In a market flooded with "AI will do everything," his voice stands out: "Sometimes doing it manually is better." That position is credible because he practices it. When he teaches rail, construction, and aerospace companies how to adopt AI safely, they trust him—not because he's hyping adoption, but because he's clearly thinking critically about what should and shouldn't be automated.
Identifying Where Humans Must Remain in the Loop
His core insight: AI amplifies judgment best when it doesn't replace it. "AI will do 80% of the job," he said. "You don't want to use it for 100%." He sees the inflection points where humans must remain: quality gates between pipeline stages, domain expertise before tool selection, human judgment after AI output.
His requirements management product embeds this principle. AI assists engineers in capturing requirements. But engineers remain decision-makers. This is why his approach scales in safety-critical industries. He's not automating expertise away—he's augmenting it while protecting the human loop.
What It's Like to Work With Ali
Ali is deliberate. He pauses before answering. He chooses words precisely. You won't hear corporate filler from him. That's not coldness—it's respect for accuracy. He asks reciprocal questions. He follows conversational threads. When he's comfortable, he becomes a mentor: longer answers, more detailed examples, leaning in instead of waiting for the next question.
By the end of our conversation, he wasn't being interviewed—he was thinking out loud with someone listening. That's the conversation style he's drawn to: deep, principled, peer-level problem-solving. He doesn't do small talk well. He doesn't need to.
He's intrinsically motivated. "I don't need to build this software," he said plainly. "I could do consulting and training all day. But I love coding. That's where you have the most fun." Revenue and growth matter—he thinks strategically about distribution and moats. But they're not what lights him up. The work itself is.
Why This Matters (For You)
If You're an Engineering Company Adopting AI
You've probably been told to move fast, adopt aggressively, use the most popular tools. Ali's question is different: "Does your team understand what you're building for?" Before Copilot, before OpenAI agents, before the newest tool—what's your actual need? What data is sensitive? What decisions can't be automated? Build Your AI's approach is to answer those questions first, then select tools. It's slower. It's also safer. If you're in aviation, rail, construction, or any regulated industry, that difference matters.
If You're an Engineer Building AI Systems
Ali's framework: quality control the pipeline, not the output. He uses Notion AI, Claude, JetBrains AI, Figma Make—but he's in the middle of each step, evaluating, adjusting, ensuring the final artifact reflects his standards. He doesn't automate thinking away. He uses AI to handle the repetitive work, then he handles the judgment. That's the model that preserves expertise while leveraging tools. It's slower than "dump it all to AI," but your code is better, and you remember why you wrote it.
If You're Early in Your Career
Watch how Ali learned. He didn't wait to be confident before building. He's a chartered engineer who learned video editing in real time. He's consulting to major industries while learning copywriting. He's bootstrapping a company while writing books. He's not trying to be perfect at everything—he's asking the right question: "What do I need to learn to move this forward?" Then he does the work. That's how expertise actually builds.
If You're Considering Joining Build Your AI
Ali codes personally. He works intentionally—40-50 hours a week, no heroic burnout. He makes decisions by asking "What does this principle demand?"—not "What does the market want?" He's building for safety and expertise preservation, not for scale-at-all-costs. The culture is deliberate, principled, slow by startup standards. If you're drawn to that—to building rigorously, to preserving human judgment in an age of automation, to work that feels meaningful because it's done carefully—that's the signal.
Go Deeper
The full conversation with Ali Parandeh is on its way. Check out other episodes in the meantime.
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