Sam Everington - Engine by Starling: Why banks are renting Starling's core instead of building their own.

In 2016, Sam Everington joined Starling Bank as one of its first twenty employees. Starling did not have a banking licence yet. It had no product, no brand, and no customers. What it had was a room full of engineers who believed a bank could be built from the customer support desk backwards, rather than from a requirements document forwards.

Ten years and one banking licence later, Starling Bank has 4.5 million UK customers, half a million business accounts, and a 9% share of UK business banking. I met Sam and his colleague Mark Bernhardi, who runs Engine's Australia and New Zealand business, at the World Credit Union Conference in Sydney. What they described wasn't a fintech growth story. It was an answer to a question most bank boards are quietly avoiding: can you fix a core banking problem without a decade and hundreds of millions of dollars.

Engine by Starling: the neobank that became a core banking vendor

The easy version of this story is that Starling did well, and one of its early engineers is now senior. That's true, and it's the least interesting part of it.

Four years ago, Starling split its technology into a separate company, Engine by Starling, and started selling the core banking platform it built for itself to other banks. AMP Bank went live on Engine in under twelve months. SBS Bank in New Zealand signed a ten-year deal with Engine in February, its first mutual client. Salt Bank in Romania and Tangerine in Canada run on the same platform. That is not a digital bank licensing its brand. That is a neobank turning its core technology into a product category.

Why the neobank speed problem was always a core banking problem

Originally it was thought the neobank threat to incumbents was about interfaces: better apps, friendlier onboarding, cheaper fees. That was Fintech 1.0, the low hanging fuit. The real cost incumbents carried sat underneath, in core banking systems nobody outside a technology division ever sees, and in the capital required to replace them.

Sam puts the figure at hundreds of millions of pounds to build what Engine now licenses. Mark Bernhardi, who spent years selling core banking transformation at nCino, described the mechanism plainly: every bank on Engine runs the same software Starling runs, updated 40 to 60 times a day. A mutual bank in New Zealand and a digital bank in Romania are, in a real sense, running the same bank.

The commercial model is just as important as the technology. Engine is sold as a managed service rather than a licensed stack a bank has to run itself, which means the heaviest capital outlay only arrives once the platform is delivering usage, not before a single customer has signed up. That is a different risk profile to the traditional core banking RFP, where the cheque is written years before any benefit shows up, and it is the detail that gets a business case past a CEO who has already been burned by one transformation program.

A decade of watching core banking eat bank strategy

This is where my own decade in fintech recruitment lines up with theirs. I've watched two waves in Australian banking since 2016. The first was the restricted authorised deposit-taking institution licence wave of 2018 and 2019, when a run of digital challengers launched believing they could compete with the Big Four who have 85% market share. Almost none of them survive as standalone brands today.

The second wave, the one underway now, is quieter and involves no new logos at all. It is existing institutions, mostly mutuals and second-tier banks, buying the core banking technology stack a neobank would have built, rather than the neobank itself.

The first wave mostly failed for the reason Sam gave for Engine's existence: the big four were never going to lose on technology, because the profits that followed the GFC let them keep reinvesting in it while smaller players tried to catch up. A new licence without a distribution advantage or a technology advantage is just a smaller, less capitalised version of an incumbent. What changes the equation for a mutual bank or a credit union isn't a new licence. It's access to a core banking platform someone else already spent a decade paying for, priced as a service rather than a build.

SBS Bank is a member-owned mutual whose balance sheet wouldn't stretch to a fraction of what a major bank spends on technology in a single year. If Engine's model works there, the constraint that has kept Australia and New Zealand's smaller customer-owned banks running on ageing cores is no longer there.

The obvious objection is risk: touching the core has long been the accepted career killer for a banking CEO, because the value of a core migration typically lands in year four or five, well past the tenure of the executive who approved it and took on all the downside. Engine's answer is to compress that timeline, moving the growth benefit into year one and the full payback into year three, which is a genuinely different risk profile for a board to sign off on, including under stress testing.

What Engine by Starling means for digital banks in Australia and New Zealand

The next decade will be won by whoever can absorb change fastest without breaking governance, and that capability is now something a bank can buy off the shelf rather than something it has to build over a decade the way Starling did.

There is already a second order effect visible in the market, and it has nothing to do with cost. Bank leadership teams that get a modern core banking platform live quickly start talking about product with more conviction, not because pricing changed, but because they can finally ship something differentiated instead of a marginally cheaper version of the same term deposit. That shift in ambition, from defending margin to building product, is arguably a bigger prize for a bank's board than the capex saving, because it changes what the executive team believes is possible to build in the first place.

For Australia and New Zealand's customer owned banking sector specifically, that's a genuine opportunity to compete for customers. A mutual bank was never going to out invest CBA on technology. What it can do is leverage the same technology platform that has the major banks worried.

The talent question Engine by Starling raises for every bank board

Ten years ago, Sam Everington sat in a room with nineteen other people and no banking licence, building the systems that would eventually serve 4.5 million customers. What he described in Sydney wasn't the end of that story. It was that same build, packaged, priced, and now available to any bank willing to buy speed instead of spending a decade building it.

Buying the platform is the easy half of this decision. The harder half is who runs it once it lands. A core banking migration compressed from five years into one still needs an operator who has built and shipped inside exactly that kind of ambiguity, not a career banker who has only ever managed a core that was already stable. That's a different hire than the one most banks have on their bench today.

Racing to fix a legacy core and need the operator who can lead it? Talk to us about your search.

Peter Jones - Nimo Industries: Breaking Into Mutual Banks.

Peter Jones used to be the CEO he now sells to. It's helped him bootstrap a core banking platform that now holds 20% market share in Australia's mutual banking sector, without a dollar of outside capital.

We are celebrating the World Credit Union Conference coming to Sydney. Dexter Cousins will be bringing you interviews from the show and special announcements.

Peter Jones co-founded Nimo Industries with Leann Jones after living the core banking problem himself as a mutual bank CEO. Ten years ago, Peter and Leann were sticking Post-it notes to a glass wall in a Melbourne office. Today, Nimo is a platform serving over 20 Australian banks and lenders, entirely bootstrapped. 

The category Nimo competes in has seen huge raises. Mambu has raised $446 million. Thought Machine has raised $706 million. nCino raised $222 million in venture funding before listing on Nasdaq in 2020 at a $2.8 billion valuation. Nimo built a comparable platform on savings, sleepless nights, and two former bank executives' understanding of exactly what their customers would tolerate.

I've known Peter a while, and when we sat down for Fintech Chatter this month, two things stood out more than the product itself.

Most fintechs think you need to break into banks by overselling.

He didn't start in Fintech. Peter spent close to two decades in banking before Nimo, including as CEO of Plenty Credit Union nearly 20 years ago, and in roles at ANZ, NAB and ME Bank. That's the exact profile of customer Nimo is targeting: more than 20 Australian banks and lenders, and counting are customers of the platform.

Core banking change has such a reputation for going wrong that Peter and his team used to have a name for it in their old jobs: "touching core is a CEO killer." Careers end over botched migrations, so most mutual bank CEOs simply don't touch the system, and the fragmented, twenty-year-old stack stays exactly where it is.

Peter told me the advantage isn't the technology he's selling. "I have walked in the shoes, I know how hard it is," he said, and it shows in how Nimo sells: no pre-sale promises it can't keep, because he's watched vendors make exactly that mistake from the buyer's chair.

That experience shows up in the compliance work most new entrants can't stomach. Nimo has built to ISO27001, SOC2 and APRA's CPS 230 and CPS 234 frameworks as a condition of entry, not a differentiator, and Peter is blunt that it's "really, really hard, especially for a new entrant." The pay-off is speed once trust is established: one Nimo client went live within five to six weeks of signing, where Peter says the old model runs to "twelve months or eighteen months."

Another client, South West Slopes, used Nimo to push further into open banking and CDR than most lenders twice its size. "We try to talk about promises made and promises kept," Peter said, and that line summarises the whole sales model.

Bootstrapping against some of the world's biggest Fintechs.

Nimo started out self-funded. Peter and Leann first built on Pega, then spent eighteen months on Salesforce, before realising neither platform let them control enough of the stack to price the product at what a mutual bank could actually afford.

That's when they went 100% serverless on AWS and stopped renting someone else's architecture. It's an Australian specific version of what Mambu, Thought Machine and nCino solved with hundreds of millions in venture funding.

Every fintech conference Peter goes to runs the same cycle: digital ID, open banking, neobanks, BNPL, and now AI, each one arriving with investors keen to see it in the pitch deck whether or not it belongs in the product.

Nimo never had that pressure, because it never had investor money. "We're pretty pragmatic at the core of it," Peter told me of Nimo's approach to AI, at a moment when most of his funded competitors are running the opposite play.

None of that made it easy. "There's been many sleepless nights, there's been many all-nighters," Peter said, and he's clear that staying "totally self-funded" for a decade was a choice made under real strain, not a badge of a honour.

What it bought Nimo is now, for the first time, a seat where it can choose who backs its next phase of growth, rather than needing anyone's money to keep the business alive. Peter told me those conversations are underway. Investor meetings are happening from a position most bootstrapped fintechs never reach.

Nimo on the World Stage.

Ten years after the Post-it notes went up on that WeWork wall, Nimo is a Platinum sponsor at the World Credit Union Conference, the globalised version of COBA. They've attended every year but have evolved significantly from what one client jokes was "a little orange stand in the back corner."

Leann is speaking at the plenary. What's notable is that their presence doesn't come from a term sheet and fresh round of funding. They've earned their standing from spending a decade being the kind of operator, and the kind of business, that couldn't afford to be anything other than exactly what it promised.

Find out more: https://nimoindustries.com/

Dexter Cousins is the founder of Tier One People, Australia's leading executive search firm for fintech. He has completed 200+ executive placements and hosts Fintech Chatter, Australia's longest running fintech podcast with 370+ episodes and 30,000 monthly listeners across 40 countries.

Andy Taylor - Stakk: The ten year overnight success.

Andy Taylor is Co-Founder of Stakk an embedded finance infrastructure solution. On 6 July 2026, ASX-listed Stakk Limited signed a definitive agreement to acquire US document intelligence firm ParaScript for US$63 million. I've interviewed Andy more than once over the past decade, way back when he was just starting to build Douugh out of Tank Stream Labs. Talking to him last week about the deal and the pivot to Stakk, three things stuck with me, perhaps even more than the acquisition itself.

Being early can almost be as challenging as being wrong.

When I interviewed Andy in 2018 he described an AI concierge for Douugh called Sophie, a digital assistant that would manage someone's entire financial life. His vision blew my mind so much so that I called him a visionary at the time. He was also about eight years too early. Large language models didn't exist. Open banking data was still screen-scraped. Customers weren't ready to hand a chatbot their financial life, let alone trust its advice. The idea was right. The infrastructure to build it wasn't there yet, and neither was the customer.

Douugh didn't get the chance to wait for the world to catch up. Capital dried up overnight in 2022 as Russia invaded Ukraine. Investors moods changed from grow at all costs to cut costs at all costs. Douugh relied on infrastructure and rails provided by other Fintechs. Unfortunately they didn't survive 2022. Overnight Douugh's business model came crashing down. "I probably still have nightmares about it," Andy told me.

Douugh had to pivot fast and become something else to survive: They repurposed what they had built with Douugh into an embedded fraud and identity platform, licensed to companies like Chime, Robinhood and T-Mobile rather than sold to consumers.

"It's a data game," Andy said of the pivot, and the ParaScript deal is the next evolution of Stakk. Three decades of document intelligence, clients including USPS and Deloitte, bolted onto a business now claiming 99.99 per cent decisioning accuracy for the fraud programs it runs.

If Douugh hadn't survived 2022 it would have just been another failed neobank to the armchair critics. Being early and being wrong look identical to people from the outside. But for those who've been around Fintech a long time, what often determines success is whether the business is still standing when the market catches up to the idea.

"The days of raising money on a vision are gone," Andy said. What's left is whether you built something that can pay its own way until the vision becomes obvious to everyone else too.

Visionary is a small part of the job.

Being a visionary used to be the main role of a founder. But it's not what got Andy through 2022 and turning the business around. What he talked about instead was grit: keeping a team believing in a plan when the plan has just been completely rewritten.

Shutting out the external noise and pressures and always showing strength for the team is probably the hardest trait for any founder. "You can never be seen to be showing that weakness," he told me.

It's at odds with most of the leadership advice doing the rounds in fintech, which tells founders to be open about their struggles and to lead with vulnerability. I don't disagree with Andy being vulnerable may be useful advice for founders when we are in "Peace Time" but the reality is Fintech is in "War Time" mode.

In times of challenge people need a leader to be strong. Andy's instincts have led to Stakk growing rapidly, expansion into the US and a $63m acquisition.

Maybe the pivot isn't product, but location?

Andy's advice to any young Australian founder listening is blunt: move. Go to a market that backs risk-taking and has real access to capital. It's confronting advice because Andy is a founding father of Australian fintech. He co-founded SocietyOne in 2011, the first peer to peer lender in Australia. He's forged a path for others to follow.

Fifteen years on, his advice to the next generation is to build somewhere else more supportive of innovation and startups. If the person who helped build the local industry is telling founders to leave, then maybe the pivot founders should consider in this market isn't embedded finance or AI - but Australia versus Singapore or Dubai or the USA.

Too often for founders what seems like the light at the end of the tunnel has been a train coming at them full speed. The acquisition is a rare moment to reflect and congratulate Andy and the team on a fantastic turnaround.

You can find out more https://stakk.tech/

Dexter Cousins is the founder of Tier One People, Australia's leading executive search firm for fintech. He has completed 200+ executive placements in Fintech and hosts Fintech Chatter, Australia's leading industry podcast with 370+ episodes and 30,000 monthly listens across 40 countries.

Hiring for AI native fintech: what Lorikeet's $50M raise tells us

When QED Investors, Blackbird, Square Peg and Airtree all back the same early-stage Australian AI startup, the funding round is news. What matters more for anyone hiring or being hired in fintech right now is why they all said yes, and what the founder behind it says about building and hiring in an AI native company from the ground up.

Steve Hind, co-founder and CEO of Lorikeet, joined me on Fintech Chatter after closing more than $50 million USD in funding, the first time since Canva that all three of Australia's top venture firms have backed a company at this stage. What he shared about the decade of experience he brought into founding Lorikeet, across BCG, Bridgewater Associates, Stripe and climate tech company Watershed, is some of the most useful thinking on fintech hiring and AI native leadership I've heard on the show.

Lorikeet - Why it Matters for Fintech

Lorikeet builds AI concierges for high-complexity, high-regulation businesses in financial services, healthcare and energy. The platform handles customer interactions across phone, chat, SMS and email, 24 hours a day, and is designed to resolve problems rather than deflect them to a FAQ page or a human queue.

The distinction matters for fintech recruitment and AI adoption alike. Most AI customer support tools were built for simple SaaS or e-commerce businesses. Lorikeet was built from the start for businesses where the wrong answer carries real risk: regulated financial products, sensitive health data, compliance obligations across multiple jurisdictions. Customers include Airwallex, Linktree and Eucalyptus, the Australian telehealth business recently acquired by Hims & Hers in a deal valued at up to $1.15 billion.

Co-founder Jamie Hall, who ran LLM research at Google Brain before leaving to build with Steve, is the architecture behind why Lorikeet works where off-the-shelf AI tools don't. Rather than taking an existing model and wrapping it, Hall and Hind built their own architecture for the specific requirements of regulated industries, one that can take actions, follow standard operating procedures and apply judgement in a way that a knowledge-base chatbot cannot.

What Stripe taught Steve about fintech hiring

Steve spent several years at Stripe in product roles during a period of rapid headcount growth. He ran hundreds of interviews, focused heavily on hiring product managers, and saw what it looks like when every qualified candidate in the market wants to work for you. It is, by his account, the opposite problem to the one he faces at Lorikeet.

At Stripe, the work was filtering: everyone with an impeccable CV was applying, so the challenge was finding the genuinely good ones inside a very large pool. At an AI native startup like Lorikeet, the candidates with the best CVs have no shortage of offers, including from frontier AI labs like Anthropic and OpenAI. The only way to win them is to offer something the market at large won't: a role with a scope they wouldn't get elsewhere, a technology bet they want to be part of, or a career step the obvious employers won't give them yet.

His framework for fintech hiring at startup stage: find people who need this role to work out, not people who can afford for it to go either way. Mutual alignment is the foundation, not the nice-to-have. If a candidate doesn't need your company to succeed for their career to go where they want it to go, you will never get the commitment and ownership the role requires. This is a lesson that applies directly to fintech recruitment across the board, not just AI native companies.

What BCG and Bridgewater taught Steve about operating with rigour

Hind's path into fintech and AI is not linear. He started at BCG, moved to Bridgewater Associates, completed an MBA at Harvard Business School, joined Stripe, then went to Watershed before founding Lorikeet. He is explicit that if he had known what he wanted to do earlier, the detours would have been costly. Because he didn't, they compounded.

BCG gave him what he calls an excellent apprenticeship in structured thinking and problem solving. It also taught him what he didn't want: the frustration of generating recommendations he never got to implement. That push toward execution rather than advice is visible in how Lorikeet operates, and in how Hind describes the leaders he wants to hire.

Bridgewater is where the operating principles came from. The culture demands obsessive focus on what is true rather than being right, and a low ego approach to feedback that is easier to describe than to practise. For fintech executives and board members evaluating AI native leadership candidates, these are the qualities that separate people who can function in a high-uncertainty environment from those who can only operate off a defined playbook. Fintech and AI are both fields where the playbook is being rewritten faster than most people can read it.

AI native leadership: what it actually requires

One of the most direct statements Hind made on Fintech Chatter is worth quoting in full for anyone in fintech recruitment or building a leadership team in 2026: if your leaders are not personally and actively using frontier AI tools in their day-to-day work, they cannot coach their teams effectively. They don't know what's possible, they can't challenge timelines or estimates with any accuracy, and they're running a 2021 playbook in a 2026 environment.

For fintech and financial services businesses hiring senior leaders right now, that is a filtering question, not a preference. A chief operating officer, chief product officer or chief compliance officer who has not built personal fluency with AI tools is carrying a material capability gap. The AI native era has changed what every leadership role requires, not just the technology roles.

Hind is also specific about what AI native does not mean at leadership level. It is not about removing experience. It is about being able to tell which parts of accumulated experience still apply and which parts are now obsolete, and being honest about the difference. The leaders who can't make that distinction will, in his view, get left behind regardless of how strong their prior track record is.

Regulation and compliance as a competitive advantage in AI and Fintech

One of Lorikeet's differentiators is that it built for regulated industries first rather than treating compliance as a constraint to work around later. Operating across financial services, healthcare and energy meant building a single architecture capable of meeting the requirements of multiple regulatory environments, an approach that has proved more scalable than building bespoke solutions for each client.

The compliance observation that resonates most from Hind's conversation is also the most counterintuitive: AI, when built correctly, is already more rule-consistent and auditable than human agents. Policy changes propagate instantly rather than going through a retraining cycle. Every decision can be logged and its reasoning made explicit. Consistency doesn't degrade with volume, time of day or workload.

For fintech recruitment, this reshapes what a head of compliance or chief risk officer actually needs to be in 2026. The tick-and-bash compliance professional is being displaced by AI doing the rules-based work consistently and at scale. The compliance leaders who are thriving are the ones who have become more commercial and more creative, able to set intelligent guardrails rather than just enforce existing ones. They are also, as Hind notes, rarer. The supply of that profile has not kept pace with fintech demand, and it shows up every time a client calls Tier One People with a compliance brief.

The raise, the VCs, and what it signals for fintech hiring

Lorikeet's $50 million USD raise, led by QED Investors with Blackbird, Square Peg and Airtree all participating, is notable for two reasons beyond the dollar figure. First, it is the largest early-stage consensus among Australian venture funds since Canva. Second, QED Investors is the leading global fintech VC fund, and their investment signals that Lorikeet is being evaluated against a global peer set, not just the Australian AI startup market.

For fintech recruitment and talent attraction, that backing matters in practical terms. It signals to senior candidates that the business has the institutional credibility and capital runway to support a serious career move. It also signals to the market that AI native fintech infrastructure is a category worth building careers in, not a hype cycle to wait out.

Airwallex, one of Australia's most recognised fintech success stories and a Lorikeet customer, is a useful data point here. Fintech talent in Australia has watched Airwallex scale from a payments startup to a global platform. The same arc is available in AI native infrastructure, and Lorikeet is one of the earliest credible entrants in that space in this market.

What this means for fintech executive search in 2026

The Fintech Chatter conversation with Steve Hind covers a lot of ground, but the talent and hiring signal running through all of it is consistent: the gap between founders and executives who understand AI native operating models and those who don't is widening, and it's widening fast.

At Tier One People, the briefs we're seeing in fintech recruitment are reflecting this. Clients are asking for leaders who have built or operated inside AI native environments, not just leaders who are open to AI. The two are not the same profile. Fintech companies that are still hiring for the former profile when they need the latter are going to lose time and ground to the ones who are calibrating their search correctly.

If you're building a fintech leadership team in 2026, or if you're a senior fintech executive thinking about your next move, the Lorikeet story is worth studying closely. The $50 million is the headline. The operating philosophy sitting behind it is the more useful thing to understand.

Listen to this episode of Fintech Chatter

Steve Hind, co-founder and CEO of Lorikeet, on raising $50 million USD, hiring for an AI native startup, and what BCG, Bridgewater and Stripe taught him about building.

00:00 Introduction
01:32 Understanding Lorikeet and Its AI Concierge Solutions
05:54 The Origin of Lorikeet and Its Founding Story
09:50 Navigating the AI Landscape and Company Growth
12:49 Regulatory Challenges in FinTech and AI Compliance
15:12 The Role of AI in Compliance and Customer Relationships
20:03 Steve's Career Journey and Lessons Learned
23:51 The Art of Startup Hiring
28:34 Navigating AI's Impact on Work
33:36 The Future of Work and Leadership
39:05 Optimism in the Age of AI
41:57 Advice for Aspiring Founders

What we discuss

Links

This show is brought to you by Tier One People, where we work with founders like Steve to find the 1% who redefine what's possible. If you're scaling your fintech leadership team, start at tieronepeople.com.

About Tier One People

Tier One People is Australia's specialist executive search firm for fintech, banking and the digital economy. We find the 1% who redefine what's possible. If you're upscaling your leadership team connect with Dexter Cousins at tieronepeople.com.

Fintech Chatter is Australia's longest-running fintech podcast, hosted by Dexter Cousins. 350+ episodes, 30,000 monthly listeners across 40 countries.

Clayton Howes - MONEYME: The leadership shift every founder must make to scale

Looking to scale your fintech leadership team? Start at tieronepeople.com

Clayton Howes is the co-founder and CEO of MONEYME (ASX: MME), a Sydney-based non-bank lender that has originated over $5 billion in consumer credit since 2013 and manages a $1.9 billion loan book today. In this conversation, Clayton covers the full arc: bootstrapping without external capital, listing on the ASX two months before the pandemic, acquiring SocietyOne on the day Russia invaded Ukraine, and how MONEYME's proprietary Horizon platform has become a competitive moat in Australian consumer finance.

About Clayton Howes

Clayton Howes is the co-founder and CEO of MONEYME (ASX: MME), which he has led since founding the business in 2013 after nearly 10 years at Vodafone Hutchinson Australia in commercial finance, sales strategy, and retail transformation. He holds an undergraduate degree from Oxford Brookes University and previously worked at GlaxoSmithKline in the UK in M&A analysis.

The MONEYME journey

Links and Resources

Fintech Chatter is brought to you by Tier One People, executive search for Fintech - where we work with founders like Clayton to find the 1% who redefine what's possible. If you're upscaling your leadership team, start at tieronepeople.com.

From Shoebox to Bank: Michelle Bagnall and the story of Bank First.

Michelle Bagnall is the CEO of Bank First, a mutual bank with 92,000 members and $4.7 billion in assets that exists to serve nurses and teachers in Australia. In this episode she tells Dexter the origin story of a bank that started with $480 in a shoebox, explains why nobody at BankFirst gets paid a bonus, and makes the case that mutuals were the original fintech startups.

Bank First. Forty-eight teachers and a shoebox.

I have spent almost two decades placing executives into fintech companies. I have watched founders raise millions on pitch decks that promised to give people access to fair, transparent, lending. Every investor presentation I saw between 2015 and 2022 had the same slide: banks are broken, we are the fix, we are changing the world. Some genuinely were, others were all about changing their world.

Last week I sat down with Michelle Bagnall, CEO of BankFirst, and she told me a story that made me feel passionate about Fintech again. In 1972, a group of 48 teachers in Victoria decided they were being ignored by the banks. So they pooled $10 each, put $480 in a shoebox, and started lending to each other. Their first loan was $250 to a single female teacher who needed help getting into a house.

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Today, BankFirst has 92,000 members and $4.7 billion in assets. Nobody at the bank gets paid a bonus. Not the CEO, not the board, not the branch staff. Every dollar of profit goes back to members or into the communities the bank serves.

About Michelle Bagnall

Michelle Bagnall is the CEO of BankFirst with more than 20 years in banking spanning NRMA, RBS in the UK, and multiple listed and mutual institutions. She grew up in Southwest Sydney, went back to university as a mature age student at 23, and describes herself as an accidental banker who found her way home in the mutual sector.

The Peer to Peer model and Fintech.

When SocietyOne launched in 2012 as Australia’s first peer-to-peer lending platform, the pitch was simple: cut out the banks, connect borrowers and lenders directly, give everyone a better deal. It raised $46 million in venture capital. It was backed by James Packer, News Corporation, Kerry Stokes and Westpac.

RateSetter quickly followed and Peer to Peer lending was the buzzword in Fintech right up until 2018 when NeoBanks and BNPL started to grab the attention of investors.

In 2021, MoneyMe acquired SocietyOne for $94 million. The peer-to-peer model was quietly shelved.

Zopa launched in the UK in 2005 as the world’s first P2P lending platform. By 2020 it had applied for a banking licence. By 2021 it had closed its P2P arm entirely and pivoted to become a regulated digital bank offering savings accounts and credit cards.

LendingClub, founded in 2006 in the US, acquired Radius Bank for US$185 million and shut down its retail P2P platform the same year. RateSetter, which had roughly £1 billion in loans on its books, was acquired by Metro Bank. The entire P2P lending sector converged on the same conclusion within a 12-month window.

Ravi Anand, managing director of ThinCats, a UK alternative lender that also shut its doors to personal investors, summarised it bluntly. P2P lending, he said, was a "moment in time response" to the global financial crisis. The model worked when trust in banks was at its lowest. Once trust recovered, the structural economics of crowdfunded lending could not compete with deposit-funded balance sheets.

Every major peer-to-peer lending platform in the world eventually decided it wanted to be a bank. And while they were spending a decade and hundreds of millions of dollars figuring that out, a model that already did exactly what P2P promised was sitting in a shoebox in Victoria. Community-based lending. Aligned incentives. Lower costs. Running since 1972.

50 years of patient capital.

The Australian mutual banking sector holds $178.4 billion in assets as of 2025, according to KPMG’s Mutuals Industry Review. That is 2.7% of all authorised deposit-taking institution assets in the country. It is not large relative to the big four, who control about 70% of the market. But it is not supposed to be.

Mutuals are not a niche. They are one of the oldest forms of organised lending on the planet. The credit union model traces back to 19th century Germany. Friedrich Raiffeisen built lending cooperatives in rural communities where formal banks refused to operate. The principle was identical to what 48 teachers did in Victoria a century later: people who share a common bond pool their capital and lend to each other on terms that a distant institution would never offer. It is the same principle that SocietyOne launched on. The difference is that the mutual version was never designed to generate an exit.

The structural difference is not scale. It is incentives. A mutual has no shareholders. There are no quarterly earnings calls. There is no investor pressure to prioritise short-term margin over long-term member outcomes. Capital allocation decisions are measured in decades, not funding rounds.

In the venture-backed fintech world, the dynamics are inverted. Australian startups raised $5.48 billion across 390 deals in 2025, a 31% increase on the prior year. Fintech was the second-highest funded sector at $868 million. But 46% of investors surveyed by Cut Through Venture saw at least one portfolio company shut down during 2025, and 77% reported layoffs across their portfolios. The capital is flowing, but the structural pressures of the VC model create a set of incentives that are fundamentally incompatible with building patient, community-first financial infrastructure. Grow fast, demonstrate unit economics, exit within seven to ten years. That timeline does not suit a bank built to last 100.

Michelle Bagnall has a phrase for the BankFirst model. It is not "not for profit." It is "for profit, for purpose." The distinction matters. The bank generates returns. It just does not distribute them to external shareholders. The more successful BankFirst becomes, the more it invests back into nurses and teachers. That feedback loop has been compounding for over 50 years.

Why understanding credit risk is essential for investors.

The advantage of the mutual model is not ideological. It is structural.

Consider credit risk. BankFirst lends heavily to nurses and teachers. These are professions with high rates of casualised employment. Shift work, agency contracts, no guaranteed hours. The standard credit risk frameworks used by the big four were not designed for this workforce. They were designed for full-time employees with fixed salaries and predictable income streams.

BankFirst can design products specifically for casualised workers because it has no pressure to maximise net interest margin across a diversified portfolio. It can take a longer view on credit performance. It can underwrite people the major banks would reject on automated scorecard alone. And it can do this not because it is more charitable, but because its capital structure allows it.

Then there is the no-bonus model. In listed banking, variable compensation drives behaviour. Risk appetite, product design, sales culture, hiring priorities: all of it flows downstream from incentive structures tied to short-term financial targets. BankFirst removed that lever entirely. Nobody gets a bonus. The CEO included. Michelle Bagnall does not frame this as sacrifice. She frames it as alignment. When nobody is being paid to optimise for the quarter, decisions default to what is right for the member over the long term. You can build a faster app. You can lower origination costs. You can use AI to automate credit decisions. But you cannot engineer away the structural tension between investor return timelines and the long-term, patient relationship that community lending requires. The mutuals solved that problem in 1972. They did it with governance, not technology.

Inventing a model that already existed.

The fintech industry has spent 15 years and billions of dollars trying to rebuild something that already existed. The narrative was that banks were broken and technology would fix them. What nobody factored in was that a different ownership structure, not a different technology stack, was the real disruption.

The mutual sector in Australia is consolidating. Fewer, larger mutuals are emerging through mergers. Bank Australia absorbed Qudos Bank and became just the fourth mutual to reach $20 billion in assets. The sector grew assets by 5.8% in 2025. This is not a sector in decline. It is a sector figuring out how to apply the structural advantages of member ownership at economies of scale.

The talent signal is hard to ignore. I place executives for a living. The candidates I speak to who have spent 10 or 15 years inside listed banks are asking different questions than they were five years ago. They are less interested in total comp and more interested in what happens after they leave. They want to point at something they built that still exists, that still serves the people it was designed for. That is not idealism. It is the natural career trajectory of someone who has already earned enough to care about meaning. Mutuals offer that. The difference now is that highly talented Fintech operators are making the move.

Tier One People is working exclusively with Bank First to hire their first ever Chief Product Officer. If you’ve built consumer lending products in a high growth Fintech reach out to me.

Jamie Twiss: How Beforepay uses AI credit risk to destroy payday lending

Looking to scale your fintech leadership team? Start at tieronepeople.com

Jamie Twiss took Beforepay Group from a pre-IPO startup into a profitable ASX-listed fintech writing 40,000 loans a week with a 99% repayment rate. He explains why the company exists to destroy payday lending, how Carrington Labs is selling AI credit risk models to US lenders, and why he believes AI will fundamentally rewire the entire finance sector.

About Jamie Twiss

Jamie Twiss is CEO of Beforepay Group (ASX: B4P) and Carrington Labs, with over 20 years in financial services spanning McKinsey, Commonwealth Bank, and Westpac where he served as Chief Strategy Officer and Chief Data Officer. He holds a degree in Slavic Languages and Literature from Harvard and an MBA from Stanford.

Beforepay Group and Carrington Labs

• Why Beforepay exists to destroy the payday lending sector and how it charges one tenth the cost

• How the company’s AI credit risk models analyse hundreds of variables to achieve a 1.1% default rate

• Beforepay’s H1 FY26 results including $4.2 million net profit, up 50% year on year

• Why 2026 is the year of personal loans with originations up 73% quarter on quarter

• How Carrington Labs packages Beforepay’s risk IP into a SaaS product for US lenders

• Jamie’s comparison of AI to electricity and why he believes it will rewire entire sectors

• Why he backs capability over experience every time when hiring

• The culture formula of accountability, kindness and obsessive data analysis

• How studying Russian literature at Harvard prepared him for running a fintech

LINKS & RESOURCES

Jamie Twiss on LinkedIn: linkedin.com/in/james-twiss

Beforepay Group: beforepay.com.au

Beforepay Investor Hub: beforepaygroup.com/investors

Carrington Labs: carringtonlabs.com

ASX: B4P

Fintech Chatter is brought to you by Tier One People - Executive Search for Fintech, where we work with founders like Jamie to find the 1% who redefine what’s possible. If you’re upscaling your leadership team, start at tieronepeople.com.

Chris Brycki Stockspot: Building Australia’s Largest Robo-Adviser

Chris Brycki built Stockspot the wrong way, according to most of the advice that was circulating in Australian fintech between 2019 and 2022!

While the sector spent those years scaling headcount, chasing VC and pivoting into whatever category was attracting capital, Chris ran a different playbook. He founded Stockspot in 2013, rejected venture capital, built his customer base through content and referrals, and stayed entirely focused on one thing: generating long-term investment returns for everyday Australians at the lowest possible cost.

In a recent episode of Fintech Chatter, I sat down with Chris in person at the Stockspot offices in Tankstream Labs, Sydney. It was the first time we had recorded face to face, and the conversation covered 13 years of building one of Australia's most quietly successful fintechs. The numbers tell the story.

Stockspot by the numbers

How Chris Brycki built Stockspot without VC

Chris left institutional finance in 2013 after recognising a structural problem: everyday Australians could not access sensible investment portfolios without paying fees that eroded their returns or using self-directed platforms where most people lost money. He pitched the idea to engineering friends in a pub, validated interest, built a basic website manually, and gave himself two years to make it work before his savings ran out.

He deliberately avoided venture capital from the outset. His reasoning: VC growth timelines and a wealth management business are structurally incompatible. Building trust and track record cannot be accelerated with capital. Throwing money at paid marketing in a category dominated by Commonwealth Bank's marketing budget is a losing proposition. Stockspot grew instead through content, referrals, and a steady compounding of client results.

"VC wasn't the right source of capital," Chris told me on the podcast. "You can't throw $100 million at a wealth business and make it work. Unless you've got five or ten years of returns to show, consumers still aren't going to trust you."

The Fat Cat Funds Report and content-led growth

One of Stockspot's most effective early moves was the Fat Cat Funds Report. Chris manually collected performance data and fee information from Australia's major super funds and published a report naming and shaming the worst performers. The logic was straightforward: the evidence for low-cost, index-based investing was clear, but most of society did not know it, and the financial media had little incentive to say so clearly.

The report drove media coverage, triggered regulatory attention, and contributed to real industry change. Several of the funds named have since shut down or merged. The approach also established Stockspot's content-led growth model: produce research that proves your thesis, publish it clearly, and let the evidence do the sales work. Paid marketing was never going to compete with CBA's budget. Proprietary research could.

Staying lean while the rest of the sector hired big

The 2019 to 2022 period tested Stockspot's hiring discipline. Capital was cheap, fintechs were scaling headcount aggressively, and the meme stock trading boom of 2021 put direct pressure on Chris to add single stock trading to the platform. He declined. The statistics on retail traders losing money were, in his assessment, too clear to ignore. Stockspot was not a gambling business. It was not going to become one because the market was excited about GameStop.

The result: when March 2022 arrived and the liquidity taps turned off, Stockspot had nothing to restructure. No mass layoffs. No emergency pivots. No forced profitability targets from investors. They kept doing what they had always done. With 28 people, they manage $1.5 billion.

"We were very careful in hiring," Brycki explained. "Whenever we did hire someone, it was someone that we could support through good times and tougher times. We've been fortunate that we haven't had to do mass layoffs like a lot of the other fintechs."

What Chris Brycki sees coming next

Chris is watching the next generation of fintech founders closely. His view is that the tools available now, including AI-assisted development and lean infrastructure, mean you can validate and build faster and cheaper than at any point in the industry's history. The constraint is no longer capital or technology access. It is having a clear thesis and the discipline to hold it.

He is seeing more founders who can reach $10 million in revenue with fewer than 10 staff. He believes many of the next significant fintechs will be bootstrapped or lightly funded, built by people who have been through the 2019 to 2022 cycle and have no desire to repeat it. The frictionless business, lean by design and close to the customer, is the model he sees winning.

"If you can avoid it, capital raising reduces one level of stress and complexity," he said. "And you get to stay true to your original vision."

Listen to the full episode

The full conversation with Chris Brycki is available now on Fintech Chatter. We cover the origin story, the Fat Cat Funds Report, the regulatory path Stockspot navigated, why he rejected VC, and what he would do differently if starting Stockspot from scratch in 2026.

About Tier One People

Tier One People is Australia's leading executive search firm for fintech and the digital economy. We work with founders like Chris Brycki to find the 1% who redefine what's possible. If you're scaling your leadership team, start at https://tieronepeople.com.

Raiz CEO: 10 Years - $2.1 Billion FUM and What Comes Next

When Brendan Malone brought the Acorns micro-investing concept from the US to Australia in February 2016, the idea was straightforward: break down the barriers to investing so that every Australian could get into the stock market for as little as $5. A decade on, that idea has compounded into $2.1 billion in funds under management, 340,000 active monthly users and over $5.5 billion invested in total.

Brendan joined Dexter Cousins on Fintech Chatter to mark the 10-year milestone and talk through what it actually takes to build a durable fintech in Australia.

From Acorns to Raiz: the first 10 months

The business started as a joint venture with US-based Acorns Grow. The deal was straightforward: Acorns provided the technology and Raiz built the operational and regulatory infrastructure for Australia. That meant spending the first 11 months navigating ASIC, learning the payments system and selecting infrastructure partners who would still be operating a decade later.

"You want to set up a business for sustainability," Brendan said. "We're sitting here in 2016 going, who's going to be around in 10 years to take us on that journey?"

The business launched publicly in February 2016, listed on the ASX as Raiz Invest in April 2018 and has operated under its own brand since.

The roundup innovation and $2.1 billion in small amounts

The core product is still the roundup. Link a debit or credit card, spend $6.50 on coffee, the app rounds it to $7.00 and holds the 50 cents. Once the accumulated roundups hit $5, the amount is direct debited from the linked bank account and invested in the chosen portfolio.

It is not complicated, but the compounding effect is. Raiz has paid over $230 million in dividends to customers, many of whom received a dividend for the first time through the platform. The business operates on a subscription model: $2.50 per month for the Light tier, $5.50 for Regular and $6.50 for Plus.

Southeast Asia: the right market, the wrong timing

Indonesia's 280 million population made the expansion case easy to argue. The revenue model is user-based, so scale matters. Local governments had financial inclusion mandates that aligned with Raiz's mission. The smartphone had already skipped the laptop generation.

The challenge was the market's preference for crypto over equities, the absence of an ETF market equivalent to Australia's and fragmented payment infrastructure. Brendan is candid about the lesson: "We were probably a bit too early for all that coming together."

It is the same lesson Netflix learned arriving in Australia before broadband was ready.

CDR: a decade of roundtables with no consumer outcome

Consumer Data Right has been one of the recurring frustrations of Australian fintech's first decade. Brendan's position is direct: the problem is who is being consulted. The conversations have been dominated by legal and technical stakeholders, not consumers.

"They're not talking to middle Australia, the masses," he said. Raiz put a survey in-app last year and received 66,000 responses in 48 hours. That is the type of consumer signal the CDR process has consistently lacked.

Raiz has deliberately chosen not to be a first mover on CDR implementation. The strategy is to wait for the second or third wave, once the kinks are resolved and adoption is real.

42 people, $2.1 billion: what a lean fintech looks like in 2026

Raiz runs on a team of 42, with 7.3 FTEs handling customer support. When investors ask Brendan why he cannot cut staff the way a major bank has by deploying AI, his response is that he does not have 3,000 support staff to cut. He never hired them in the first place.

The product team runs three meaningful development projects at any time: two customer-facing and one back-of-house. The internal principle is not to become an owner builder whose house is never finished. AI is embedded in the workflow, not bolted on.

"RAIZ, R-A-I-Z. AI is in our name," Brendan noted. "We've been using machine learning for years. That's how we do what we do with 42 staff."

The next 10 years: ecosystem, consolidation and endurance

Brendan's product roadmap centres on building an ecosystem that spans a customer's full financial life. Raiz Kids already serves the under-18 cohort. The vision is that a child who opens a Raiz Kids account and turns 18 migrates into the adult product and stays in that ecosystem indefinitely.

He also expects consolidation among micro-investing platforms within the next few years. His argument is that several players do different things well but none does everything well, and that consolidation would deliver a better, cheaper experience for customers.

The endurance principles he identifies in the fintechs that have survived a decade: stay close to customers, resist the bright shiny things, stick to your strategy three, five and 10 years out. Raiz has navigated the buy-now-pay-later hype, the crypto boom, the CDR promises and now AI without pivoting away from its core.

"A lot has changed," Brendan said, "but there's still a massive ramp for the next ten."

Listen to the full episode

Available on Spotify, Apple Podcasts and all major podcast platforms. Watch on YouTube at Fintech Chatter TV.

AI Native Transformation: Mono AI

David Hyman's Next Chapter After Building a $107B Platform

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.

From Mortgage Platform to AI Platform: Building AI Native.

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."

What AI Native Transformation Actually Means

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.

The Revenue Per Employee Benchmark Every Leader Should Know

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."

The Mono AI Platform: Three Components, One Goal

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 Wedge Model: Six Weeks to Proof of Value

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."

The CTO Problem Nobody Talks About

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.

Two Traits That Define Who Thrives in the AI Era

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.

Why Mono AI Is Building From Sydney

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.

What Roles Mono AI Is Hiring For

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.

Frequently Asked Questions

What is Mono AI?

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.

What is the wedge model?

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.

What is AI native transformation?

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).

What was Guardian at Lendi Group?

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.

Why is the revenue per employee metric important for AI strategy?

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.

What roles is Mono AI hiring for?

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.

What AI job losses tell us about the next decade

In March 2021, I sat on national television and said we were at the precipice of a quantum shift. AI was removing task-based roles. The organisations that would survive were the ones with leaders who had already learned to deliver results in chaos and constraint.

Five years later, the numbers arrived. All at once.

I wrote the full analysis for Startup Daily. Here are two of the key arguments.

Why the market rewards AI job cuts

Block cut more than 4,000 roles last week. Stock up 24%. WiseTech Global cut 2,000 roles the same week. Stock up 11%. Commonwealth Bank eliminated 300 technology positions. Investors barely flinched.

The pattern is clear. When a company cuts staff because it is in financial distress, the market punishes it. When it cuts because AI enables the same or better output with fewer people, the market rewards it.

Block was not in distress. Its gross profit grew 24% in the quarter it announced the layoffs. WiseTech reported a first-half profit 6% ahead of consensus on the same day it announced the cuts.

These are not companies retreating. They are companies restructuring around AI as infrastructure, not as a feature.

Who leads what's left after the cuts

The restructuring decision is easy. A board can make that call in an afternoon. The hard question is what comes next.

When you take headcount from a thousand to five hundred, when you collapse three functions into one, when you rebuild around AI as infrastructure, the people who remain need to operate at a level most of them have never been asked to reach. They need to make decisions that committees used to make. Lead teams at a pace that large organisations were never designed to move at.

AI does not eliminate the need for exceptional leaders. It eliminates the buffer that average leaders used to hide behind.

The leaders who already operate this way

The executives who can lead a restructured, AI-native organisation already exist. They were forged by a decade of startup conditions: no budget, no playbook, constant change, relentless pressure.

I wrote about this operator profile back in 2022 for Startup Daily, when I predicted the talent market would shift from a supply crisis to a capability crisis. The talent shortage was never really about headcount. It was about finding people who had built under constraint and could do it again at scale.

That profile, someone who runs lean by instinct, context-switches across product and operations, makes irreversible decisions with incomplete information, is now exactly what every restructuring organisation needs.

What this means for founders and CEOs hiring right now

The organisations that thrive in the next decade will not be the ones with the most sophisticated AI stack. Those tools are a commodity. Every competitor has access to the same models, the same infrastructure.

The differentiator is the human who knows how to use it. Who has already built in the conditions that AI restructuring creates. Who does not need a playbook because they wrote the last one themselves.

Finding that person requires a network built inside the ecosystem where they were produced. Not a LinkedIn search filtered by job title.

Read the full piece on Startup Daily →

Hiring the leader who takes your organisation through this shift? Talk to us about your search.

Who leads what's left - AI restructuring.

Last week, 4,000 people at Block were told they no longer had a job. The stock rose 24%. WiseTech Global cut 2,000 roles - nearly a third of its global workforce - as part of a two-year AI restructuring plan. Commonwealth Bank eliminated 300 technology positions the same day. Three AI restructuring announcements. Five days. Three share prices up across the board.

That is today's headline. But the story begins a decade ago, and it starts with a bet I made in 2016 - not on a product or a market, but on a type of person. The founders I was working with in fintech were operating in conditions the rest of the corporate world had not experienced yet. I believed those conditions were coming for everyone. Last week, they arrived.

Why AI layoffs sent three share prices higher

The market is not mourning these cuts. It is rewarding them. That is the fact worth sitting with, and it is the one that most of the coverage has moved past too quickly. In a traditional framing, a company cutting half its workforce is in crisis. Investors flee. The narrative is failure. That is not what happened.

What happened is that investors looked at Block's AI restructuring and concluded the company will be more valuable with fewer, more capable people and a properly deployed AI stack than it was with a larger, more expensive, less leveraged workforce. The cuts were not a symptom of decline. They were the mechanism of transformation. Block CEO Jack Dorsey was unambiguous in his letter to shareholders: 'Intelligence tools have changed what it means to build and run a company. A significantly smaller team, using the tools we're building, can do more and do it better.' WiseTech CEO Zubin Appoo was equally direct: 'The era of manually writing code as the core act of engineering is over.'

These are not euphemisms or careful corporate language. They are executives stating on the record that their previous headcount was a legacy of how organisations used to have to operate, and that AI has made that model obsolete. The market agreed, loudly, both times. Block is not alone and it will not be the last. Every week the number of similar announcements grows, and every week somewhere in a boardroom the same conversation is happening: we need to restructure, we need to go leaner, we need AI to do what teams used to do. What almost nobody is saying in that conversation is what comes after the cuts.

What I predicted about AI and jobs in 2021

I find myself thinking about 3rd March 2021, sitting in front of a camera for Ausbiz TV. The interview was about remote work. The world had just spent twelve months working from home and everyone was trying to figure out whether that was permanent or a blip. The conversation turned to productivity, to AI, to what the jobs market was actually telling us beneath the headline numbers.

I had been doing my own research at the time. Tracking job ad data in fintech, running surveys across our network, talking to founders every week about what they actually needed versus what the market was supplying. The challenge with remote work, I argued, was not technology. The technology worked fine. The challenge was leadership. Leaders were struggling to build and maintain high-performing teams they could not see, and we were starting to see dips not in task completion but in the collaborative moments that produce the ideas nobody plans for.

'We are at the precipice of a quantum shift. Not just in how we work. In the economy. In everything. AI is removing task-based roles. The roles that remain will require a different kind of person. This is happening. Just because you don't see it doesn't mean it's not.'

The interviewer moved on. The segment ended. The world kept going. That was five years ago.

What AI restructuring leaves behind after the cuts

When I started Tier One People in 2016, the Australian fintech ecosystem was young and full of promise that not everyone believed in. The founders I worked with were building companies the incumbents did not take seriously, competing for talent against organisations with resources they did not have, solving problems that had never been solved before in markets that were still being defined. They had no budget, no playbook, and no margin for error.

The people who joined those companies were self-selecting into a formation that a traditional career path cannot replicate. I wrote in 2022 that the expectations placed on fintech employees are closer to elite sport than to corporate banking. In elite sport, players are hired not just for their skills but for their ability to perform under intense pressure. Delivering results without process was the only option because there was no process. Decisions had to be made fast because slow ones were fatal. Running lean was not a strategy; it was the only budget available.

These executives built cultures under pressure, scaled teams mid-flight, restructured while shipping, and did all of it under the scrutiny of investors who expected quarterly proof that the thesis was working. Becoming AI-native was not a priority on a roadmap; it was the only way to compete with organisations ten times their size. That is not a job history. That is a decade of conditions that produced a very specific kind of executive - one who has already lived through what every AI restructuring organisation is now trying to build.

Why the leader above the AI stack is the differentiator

When you eliminate the middle layer, collapse three functions into one, and rebuild your organisation around AI as infrastructure rather than AI as a tool, the people who remain need to operate at a completely different level than the people who left. This is not a technology problem. The AI stack is available to anyone. You can buy it, build it, deploy it. The technology is not the differentiator.

The differentiator is the human sitting at the top of that stack. The executive who can run a leaner, faster, higher-stakes organisation. Who can make irreversible decisions without a committee. Who can context-switch across product, data, operations, and culture without losing momentum. The assessment framework I built in 2016 has not changed: skills plus learning ability plus performance under pressure equals outcomes. The number one predictor of a leader in the AI age is the ability to context-switch. Fintech executives have been doing this ten times a day for a decade.

The current conversation is dominated by two camps. One says AI will take everyone's jobs and the future is bleak. The other says AI is just a tool and humans will always be needed. Both are wrong in the ways that matter to the people making hiring decisions right now. AI does not eliminate the need for exceptional leaders. It eliminates the buffer that average leaders used to hide behind: the layers of process, the large teams, the slow decision cycles that kept organisations running despite mediocre leadership at the top. What remains is a direct line between the quality of the leader and the performance of the organisation. In that environment, the difference between a good hire and a great one is not marginal. It is existential.

What a decade in fintech produced that no other sector did

The organisations that get the AI restructuring right will do so because they solve the talent problem correctly. They will understand that the cuts are the easy part, that a board can make that decision in an afternoon. The hard question is what comes after: who leads an organisation with no redundancy, no process layers, and a direct line between leader quality and organisational performance.

The ones that get it wrong will make the cuts and then hire the same profile they always hired. They will promote the most experienced person in the room rather than the most capable one. They will apply traditional executive search methodology to a talent profile that traditional executive search was never built to find. They will discover, six to twelve months later, that the AI restructuring did not work. Not because the AI was wrong or the numbers were wrong, but because the person at the top of the stack was the wrong person. In a restructured organisation operating with no redundancy, that is a mistake that is potentially fatal.

The executives who built Australia's fastest-scaling fintechs are the most valuable leaders in any sector right now. Not because of their fintech credentials, but because of what those credentials represent. They have already done what every organisation undergoing AI restructuring is now trying to do. Functions collapsed, lean was built, ambiguity was led through without a safety net. Finding them requires a network built inside the environment where they were forged, not a LinkedIn search filtered by job title.

How to hire a leader for an AI-native organisation

1 March 2016. A conviction.

3 March 2021. A prediction.

27 February 2026. A reckoning.

WiseTech. CBA. Block. Share prices up across all three. The market rewarding the AI restructuring. The era of large teams as a proxy for value officially over.

I did not build Tier One People to be right about a prediction. I built it because I believed, and still believe, that finding the right person for the right role at the right moment is the highest-leverage decision any organisation makes. The conditions that forged the operators in my network were brutal and clarifying in equal measure: no budget, no playbook, constant change, relentless pressure, results or nothing. Those conditions are now the operating reality for every organisation serious about competing in what comes next.

Those people are ready. They have been ready for a decade. The question is whether the organisations that need them are ready to find them. I have spent ten years building for this moment. It is here.

Dexter Cousins is the founder of Tier One People, Australia's leading executive search firm for fintech. Since. He has completed 200+ executive placements and hosts Fintech Chatter, Australia's leading fintech podcast with 350+ episodes and 30,000 monthly listeners across 40 countries.

If you are restructuring and facing the question of who leads what's left, that is the question Tier One People was built to answer.

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