David Hyman spent 13 years building Lendi Group into Australia's largest non-bank mortgage platform. $107 billion loan book. Five million customers. 220 retail stores. $350 million in annual revenue. He stepped down as CEO in January 2026. Instead of relaxing on a beach he was building again within days.
His new venture is Mono AI, a platform business that exists to help mid-market enterprise go through AI native transformation - before an AI native competitor makes them irrelevant. In his fourth appearance on Fintech Chatter, David sat down with Dexter Cousins to explain what he learned leading Lendi Group's AI transformation why he started Mono AI, and what business leaders are getting wrong about AI right now.
The Lendi story has three distinct chapters. The first was building the original digital mortgage platform from scratch, competing against banks and brokers at a time when you simply could not get a home loan online. The second was the acquisition of Aussie Home Loans from CBA and migrating everything onto a single technology stack. The third, and most recent, was what David calls the find, buy and own era: 140,000 property listings, 11 million property records, and a full end-to-end experience layered across the broker and retail network.
The moment David points to as the culmination of his time at Lendi was the company-wide conference in March 2025, where the Lendi team brought the AI native future to life for brokers, partners, and the wider real estate industry at a time when most businesses were still asking whether AI was relevant to them.
"We were early," he says. "We had a healthy level of resting anxiety every day because we were watching the AI models race ahead and challenging ourselves to keep going and keep going faster."
That experience, combined with time spent visiting businesses in the US, Europe, and Asia, gave David a clear view of the gap between businesses starting from a blank sheet and those trying to transform legacy operations. "The vast majority of businesses don't have the luxury of starting fresh," he says. "And in 12, 24, and 36 months' time, they're going to be competing with AI native businesses that are growing very, very quickly."
The term gets used loosely. David is specific about what it means in practice.
He draws a distinction between two modes of operation. Human motion is how most organisations work today: everything moves forward because a person takes action or asserts judgment. Agentic motion is where AI agents run in loops, 24 hours a day, handling what he calls the busy work, while humans remain on the loop for certain checkpoints and in the loop for specific high-judgment decisions.
At Lendi, this took shape in a product called Guardian, which David describes as a combination of humans on the loop and humans in the loop. The AI and the customer move through a process without interruption until guardrails are hit, at which point a human gets involved. Certain moments like product recommendations or identity verification are always human-in-the-loop by design.
What this is not, David is clear, is a technology problem. "AI has moved from being a technology problem to being a human change and business model problem," he says. The execution gap is not about tools. It is about whether leaders can help their teams lift their gaze, understand where the business is going, and move through the change without requiring certainty about every step.
David uses a single metric to frame the scale of what is coming: revenue per employee.
Traditional businesses run at $200,000 to $300,000 revenue per employee. The best enterprise SaaS businesses reach $500,000 to $700,000. Then there is the new cohort of AI native companies. ElevenLabs generates approximately $400 million in revenue with roughly 200 employees, including only 18 people in its go-to-market function. That is $2 million revenue per employee.
"That revenue is not from their revenue-producing employees," David says. "That is across everyone." And that is the operating leverage your business will be competing against within the next two to three years.
David is direct about what this means: businesses that do not go through AI native transformation will face a structurally lower-cost, faster-moving competitor that almost certainly has a better product. "What really gets me out of bed every day is helping businesses compete head on with those AI native businesses."
Mono AI is not a consultancy. David is emphatic about this. The business has three components.
The platform is a multi-agent orchestration system that is cloud agnostic and model agnostic. It connects to all major AI models either directly or via inference platforms like AWS Bedrock, Google Vertex, or Azure. It includes a memory system that compounds knowledge over time, fine-grained access controls at user, team, and organisation level, and out-of-the-box connectors to internal systems. The goal is to avoid relying on large data migration projects and instead reason directly over source data.
The wedge system is a library of playbooks for specific business domains, verticals, and industries. These playbooks map out the pathway to AI native and define where to start based on the business's actual objectives.
The forward deployed pods are senior Mono AI employees who co-build with the client and embed inside the organisation for the duration of the engagement.
The target market is mid-market companies with revenue between $200 million and $2 billion. David's reference point is Palantir, which serves the top end of government and enterprise. Mono AI is targeting the mass mid-market where the P&L pressure to act on AI is real but the resources and methodology to do so are not.
The enterprise sales cycle is one of the most well-documented traps in B2B technology: twelve months of stakeholder alignment, a key sponsor who leaves, and you start again.
Mono AI has structured its go-to-market specifically to avoid this. The wedge is a high impact, low risk use case that can be co-built with the client in two to six weeks on a fixed fee. Before the wedge, Mono AI helps the business map its current technology stack, define what an AI native version of its business could look like, and identify the best starting point, either a vertical (a division, region, or department) or a horizontal (executive intelligence, sales funnel acceleration, meeting intelligence).
"Let's not spend six months deciding what we're going to do," David says. "Let's get started in six weeks, have something we can all look at and say: these things worked, these things didn't, this was our view on AI native. These things still hold. Some things have shifted."
Because Mono AI is positioning at the CEO and board level rather than at the technology buyer level, the sales motion is fundamentally different from standard enterprise SaaS. "We're not trying to displace an existing spend. We're trying to work with businesses on their broader business model. The whole P&L is in question."
One of the more striking observations David shares is that the biggest blocker to AI strategy in most organisations is not the CEO. It is the CTO.
"When I speak to CEOs, the biggest blocker in most organisations to executing on their AI strategy is the CTO," he says. "They're great. I've worked with amazing technologists. But those closest to the technology are not necessarily lifting their gaze and thinking about how this plays outside of just providing services within a particular part of the business."
This is not a criticism of individuals. It reflects a structural misalignment: AI native transformation is a business model problem, and most technology leaders are still framing it as a technology delivery problem.
David is consistent on this point across the conversation. Two characteristics determine whether a person moves through the AI transition successfully.
The first is curiosity. Not a growth mindset as an abstract concept, but active willingness to break apart existing processes from first principles and ask whether they still make sense. The second is high agency. Not coming to the table with reasons why something cannot be done. Identifying what needs to be solved and moving toward it.
"We're seeing some people have the absolute time of their career in this change," David says. Those are the people who combine both traits.
David has spent significant time in the US, Europe, and Asia over the past two years. His conclusion about Australian talent runs counter to the conventional narrative that you need to be in Silicon Valley to build a global technology company.
"Australia really punches above its weight in terms of the quality of entrepreneurs, technologists, and product people compared to any other market around the world," he says. "The more you spend time in Europe or the US, you realise that the calibre of people here is just top world class."
Mono AI's plan is to launch additional markets over the next few quarters, establishing hubs in West Coast US, East Coast US, and EMEA, all supported by a Sydney base.
Mono AI is hiring. Not at scale, and not across every function.
The company is looking for people on the product and platform side as it continues to build out the core offering. On the go-to-market and delivery side, it is specifically interested in people from enterprise SaaS backgrounds and Big Four consulting who identify as being on the entrepreneurial end of those environments.
"If you're in Big Four consulting and you feel like you're more on the entrepreneurial side, whether it's on the go-to-market or delivery side, we'd love to chat," David says. Connect with him directly on LinkedIn. DMs are open.
Mono AI is a platform business founded by David Hyman that helps established companies go through AI native transformation. It combines a multi-agent orchestration platform, a library of business-domain playbooks (the wedge system), and forward deployed teams who co-build with clients. The company targets mid-market businesses with revenue between $200 million and $2 billion.
The wedge model is Mono AI's approach to getting enterprises started quickly rather than spending months on planning. A wedge is a high impact, low risk use case that Mono AI co-builds with a client in two to six weeks on a fixed fee. It is designed to generate tangible proof of value fast, after which the broader AI native plan is refined.
AI native transformation is the process of reimagining how a business operates in an era where AI agents can handle the majority of process-driven work. It moves businesses from human motion (everything requires a person to take action) to agentic motion (AI agents run continuously on routine tasks while humans focus on judgment-intensive work).
Guardian was Lendi's AI product, developed as part of Project Aurora. It combined humans on the loop (AI and customers move through workflows autonomously unless guardrails are triggered) with humans in the loop (specific high-trust moments like product recommendations or identity verification that always involve a human). It was a practical demonstration of AI native transformation in a regulated financial services environment.
Revenue per employee is the metric that shows the structural operating leverage AI native businesses are building. Traditional businesses run at $200,000 to $300,000 per employee. AI native businesses like ElevenLabs are running at approximately $2 million per employee. Within 12 to 36 months, most established businesses will be competing against companies operating at that level of leverage.
Mono AI is actively hiring on the product and platform side, and on the go-to-market and delivery side. The company is specifically interested in people from enterprise SaaS and Big Four consulting backgrounds who sit at the entrepreneurial end of those environments. Connect with David Hyman on LinkedIn to start a conversation.
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