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.

Request a confidential briefing

Checkbox AI CEO Evan Wong: How to Build Enterprise Software That Scales

How Checkbox AI CEO Evan Wong Built a $100M Legal AI Company in 9 Years

Checkbox AI CEO Evan Wong just closed a $23 million Series A at a $100 million valuation. The journey from bootstrapped startup to serving over 100 enterprise organizations—including SAP, PepsiCo, Telstra, and Woolworths—took nine years, three funding rounds, and a strategic pivot that changed everything.

On this week's Fintech Chatter podcast, Evan shared the lessons learned building Checkbox AI from stealth mode to becoming the "AI Legal Front Door" for Fortune 500 companies.

The Strategic Pivot: From Multi-Sector to Legal AI

When Checkbox AI launched in 2016, the vision was broad: a no-code platform that could automate workflows across any business function—legal, HR, compliance, procurement.

The product worked. Early customers found value. But scale remained elusive.

"At some point, we made the decision to go from being a platform that could serve multiple functions to focusing purely on legal," Evan explained. "That's not easy—you're essentially choosing to walk away from potential customers and revenue."

The pivot unlocked growth. By positioning Checkbox as the AI Legal Front Door for enterprise, the company found product-market fit with in-house legal teams struggling to manage increasing volumes of requests from business units.

Today, at Hitachi, 83% of routine legal and compliance requests are partially or fully automated through Checkbox's platform—freeing legal teams to focus on high-value, strategic work.

Building for Enterprise From Day One

Most SaaS companies start mid-market and struggle to move upmarket. Checkbox AI took the opposite approach, targeting enterprise customers from the beginning.

"We dealt with the pain upfront," Evan said. "Had to do SOC 2 early. Had to build robust features for enterprise at the get-go."

The strategy paid off. Today, Checkbox's average sales cycle for Fortune 500 companies is approximately three months for six-figure USD deals.

But building for enterprise requires discipline around product development. With large customers comes the temptation to build what they ask for—not necessarily what the broader market needs.

"Take everything from a customer as a data point, not as an instruction," Evan advised. "Customers are experts in the problem. You're the expert in the solution."

The $23M Series A: Touring Capital, Peak XV, and Strategic Angels

Checkbox AI's Series A round was led by Touring Capital, with participation from existing investors Peak XV (formerly Sequoia Capital India) and Tidal Ventures, plus new investors Conductive Ventures and Five V Capital.

The round also included Jerry Ting, VP and Head of Agentic AI at Workday, who was formerly Co-Founder and CEO of legal AI company Evisort.

The capital validates Checkbox's approach to legal AI: rather than building standalone chatbots, Checkbox embeds AI agents directly into enterprise workflows—email, Slack, Microsoft Teams, Salesforce, and intranet portals.

"As more legal work becomes AI-assisted, the winners will be the platforms that can route requests intelligently, integrate across the legal tech stack, and turn institutional knowledge into scalable workflows," said Evan Wijaya, Principal at Touring Capital.

Competing in a Market with $5B+ Valuations

Legal AI is attracting massive capital. Harvey raised at a $5 billion valuation. EvenUp hit $1 billion. How does Checkbox compete?

"It's actually a good sign," Evan said. "Higher tides raise all boats. There are billions of dollars in productivity to be unlocked in legal."

Checkbox differentiates through its focus on workflow orchestration and enterprise integration rather than broad AI assistants. The company was named in Gartner's 2025 Hype Cycle for Legal Tech as category-defining infrastructure.

The Nine-Year Journey: Bootstrapping to Series A

From founding in 2016 to Series A in 2026, Checkbox raised only $6 million USD before this latest round, remarkably efficient for a company generating eight figures in annual recurring revenue.

The timeline included:

- 2016-2018: Bootstrapped in stealth mode, founders not drawing salaries

- 2018: $1.77M angel round

- 2022: $6.3M pre-Series A led by Sequoia India's Surge and Tidal Ventures

- 2026: $23M Series A at $100M valuation

"Australian companies are really good at building with a frugal mindset," Evan noted. "Coming to the US, investors are always impressed with how efficient we are."

Building with AI-First Mindset

As Checkbox scales from 75 to 100+ employees, the company is embedding AI-first thinking throughout the organization.

"Do you first jump to hire someone when you need bandwidth, or do you consider what AI tools can give you leverage?" Evan asked. "That order of operation in your brain—that's the mindset we're building."

AI proficiency is now a hiring criterion. Every team member is expected to leverage AI tools to increase their output and impact.

"There's companies like Cursor who have gone to really big scales of revenue with very lean teams," Evan said. "The tools are there now. It's about leveraging them."

Lessons for Enterprise Software Founders

On Product-Market Fit

"People think achieving product-market fit is a moment in time. But there's a key word—market. And markets change constantly. You have to keep chasing it."

On Customer Development

"Customers often explain what they want in solution form, but they're not the solution experts. You're the solution expert. The customer is the expert in the problem."

On Strategic Focus

"Who are NOT your customers is just as important as who ARE your customers. That gives you clarity in who you want to build for."

On The Long Game

"Most startups either scale much faster or they die. We sustained the business through a longer timeline by building efficiently and staying focused on enterprise customers who could deliver meaningful contract values."

What's Next for Checkbox AI

The Series A capital will fund two primary areas:

1. Engineering, product, and design: Expanding the team primarily based in Sydney, Australia

2. Go-to-market: Scaling sales and marketing to compete globally

"We want to become the cornerstone technology for enterprise legal teams," Evan said. "You wouldn't start a sales team without Salesforce. You shouldn't start a legal team without Checkbox."

Hiring Enterprise Software Leaders?

At Tier One People, we specialize in placing executives who can scale enterprise software companies from Series A to market leadership. 

Whether you need a Chief Revenue Officer who understands enterprise sales cycles, a VP of Product who can balance customer demands with strategic vision, or engineering leaders who can build for scale we connect you with the 1% who define what's possible.

Contact Dexter Cousins: Contact

About Evan Wong

Evan Wong is Co-Founder and CEO of Checkbox AI. A serial entrepreneur who founded Hero Education at age 17, Evan has built Checkbox from a bootstrapped Sydney startup into a legal AI platform serving 100+ organizations including Fortune 500 companies. Named to Forbes 30 Under 30 in 2019, Evan recently closed a $23 million Series A at a $100 million valuation.

About Fintech Chatter

Fintech Chatter is Australia's leading fintech podcast with 350+ episodes reaching 30,000+ fintech professionals in 40 countries monthly. Hosted by Dexter Cousins, the show features conversations with fintech CEOs and founders about how they're building category-defining companies.

Watch the full conversation:YouTube link

Build Your Professional Brand Using First Principles

Last week, you built your Career Balance Sheet. You listed every problem you've solved. You put real numbers on your impact.

Maybe you automated processes and saved $1.5M. Maybe you closed a deal worth $20M in ARR. Maybe you cut sales cycles from 6 months to 8 weeks.

But here's the first principles question: What's the fundamental truth underneath all those achievements?

Strip away the job titles. Strip away the company names. Strip away the activities.

What's left is your pattern. Your superpower. Your professional brand.

Not a vague statement like "I'm a strategic leader." A precise statement built from fundamental truths that makes someone say "I need that person right now."

Breaking Your Career Down to First Principles

Look at your Career Balance Sheet. All your achievements are there. Now look for the pattern.

What's the thread that runs through everything you've done?

Let me show you with real examples.

The CFO who raised Series A ($5M), Series B ($15M), Series C ($40M), and took the company public ($200M valuation).

Pattern: Takes companies from early stage funding to IPO.

Professional brand: "I'm the CFO who takes companies from seed to IPO."

The CRO who joined at $2M ARR, built the sales team from 3 to 15 people, and left at $22M ARR.

Pattern: Scales revenue in the $2M to $20M range.

Professional brand: "I'm the CRO who scales revenue from $2M to $20M ARR."

The Product Leader who inherited a feature with 15% adoption, rebuilt the feedback loop, redesigned onboarding, and hit 82% adoption.

Pattern: Makes products people actually use.

Professional brand: "I'm the Product Leader who took feature adoption from 15% to 82%."

See the pattern in these patterns?

Each one has three elements:

  1. Your role - CFO, CRO, Product Leader
  2. Specific numbers - Seed to IPO, $2M to $20M, 15% to 82%
  3. The outcome - What actually happened

Not activities. Not responsibilities. Outcomes.

How to Test Your Professional Brand Statement

Now you need to know if your brand actually resonates.

Think of it like a doctor testing a diagnosis. You have a hypothesis. You run tests. You see if you're right.

Create three variations of your brand statement. Test them.

Update your LinkedIn headline with Version A. Give it two weeks. Track profile views, connection requests, InMail messages.

Switch to Version B. Another two weeks. Compare the numbers.

Test in real conversations. When someone asks what you do, use your brand statement. Watch their reaction.

Do they lean in and ask questions? That's resonance.

Do they nod politely and change the subject? That's not working.

After 4-6 weeks, you'll have data. One version will clearly outperform. That's your signal. That's what the market wants.

Finding Companies That Need Your Exact Capability

Your professional brand tells you exactly who to target.

"I'm the CFO who takes companies from seed to IPO" → Target companies that just raised Series B or C. They'll need IPO prep in 18-24 months.

"I'm the CRO who scales revenue from $2M to $20M ARR" → Target companies currently at $2M to $5M ARR who just raised Series A.

"I'm the Compliance Head who gets startups their license" → Target pre-license companies that just raised funding.

You're not searching "fintech jobs."

You're searching for companies at the exact stage where they need your exact capability.

Building Your Problem Portfolio: 10-15 Target Companies

Create a hit list of 10-15 companies you've researched deeply. For each one, track:

  1. Evidence they need you - Funding stage, LinkedIn posts, job listings
  2. Specific pain points - What they're struggling with right now
  3. Your solution - Straight from your Career Balance Sheet
  4. Your entry point - Who you know, how to reach them

Not 200 random applications. 10-15 companies where you've done your homework.

This is precision targeting, not spray and pray.

What Quantified Professional Brands Actually Look Like

Before: "Hi, I'm applying for your CFO role. I have 15 years of finance experience. I'm detail-oriented and a strong communicator."

After: "Hi, I noticed you just closed your $40M Series C with Sequoia. Based on their portfolio pattern, you're likely 18-24 months from IPO conversations. I'm the CFO who's taken three companies through that exact journey. The biggest challenge is always audit readiness 12 months before filing. I'd like to discuss what you're seeing."

Which one gets a response?

The second one shows you understand their business. You've done your homework. You're not applying - you're offering to solve a specific problem they have right now.

That's the difference between 1% response rates and 60% response rates.

Your Next Step: From Balance Sheet to Professional Brand

You have your Career Balance Sheet. Now turn it into your professional brand.

Extract the pattern. Write it as one quantified sentence. Test it. Find companies who need exactly what you do.

Then reach out with precision, not desperation.

Listen to the full episode of Finding Your Next Role in Fintech for the complete framework, testing methodology, and research strategies.

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Revolut Australia CEO Matt Baxby Interview: 1M Customers

Revolut Australia CEO Matt Baxby: From 3 Employees to 1 Million Customers and Profitability.

Revolut Australia CEO Matt Baxby has done what most Australian neobanks couldn't: reach 1 million customers and profitability. Six years after soft-launching with three employees during a global pandemic, the Revolut Australia CEO sits down with Dexter Cousins on Fintech Chatter to discuss the journey from travel FX startup to 30-product super app, the $250 million saved for Australians, and the ambition to become the country's number one finance app. More importantly, Matt Baxby reveals the hiring philosophy and culture that made it all possible.

This interview was recorded on 4 February 2026.

How Revolut Australia Reached 1 Million Customers and saved them $250M

Dexter Cousins: Matt, congratulations on a massive milestone. Let's start with the big news: Revolut Australia has just hit 1 million customers. Take us through what that means.

Matt Baxby: Thanks, Dexter. Yeah, we crossed 1 million customers at the end of January, which is a really proud moment for the team. But what's more meaningful to me is that we've saved Australians close to $250 million in FX fees compared to what the major banks charge. That demonstrates there's a real need for what we're offering in this market. When you can put that kind of money back in people's pockets, you know you're solving genuine problems.

DC: The awareness is certainly building. People are starting to understand there are alternatives to those airport FX desks, and Revolut is at the front of that pack. But you've evolved well beyond just travel money, haven't you? You're now offering 30 plus products.

MB: Absolutely. When we launched six years ago, the proposition was simple: bring together disparate financial solutions into one app. Things like overseas money transfer, bill splitting, peer-to-peer transfers. One of the key features from those early days that's still incredibly popular is the ability to transfer any currency directly to another Revolut customer in a different market. No friction, no cost, no waiting around for three days. That was the hook.

But you're right: we've expanded significantly. Today we're a modular platform. There's no set use case for our customers. Some people use us primarily for travel, others for everyday spending, some for investing in crypto or US shares. We build based on what customers tell us they need solved.

DC: That's interesting because as a Gen Xer maybe it's my eyes going, but the app is getting more complex to navigate with all those features. Is product proliferation becoming a challenge?

MB: [Laughs] Fair observation. Look, we're very aware of that, and it's something we're constantly working on. But I'd rather have that problem than the alternative: being too narrow in what we offer. The development continues to be driven by customer feedback. If enough customers are telling us they need something, we'll build it. That customer-first approach has been core to our success.

And here's the thing that keeps me confident we're on the right track: word-of-mouth referrals still represent a large proportion of our new customer acquisition. That's the highest compliment we can receive. When customers are actively recommending us to friends and family despite the complexity, it tells me we're delivering real value.

Revolut Business Australia: 235% Growth in Transaction Volumes

DC: Let's talk about Revolut Business. Small businesses are the backbone of the Australian economy, but they often feel overlooked by the major banks and even by many fintechs. What's happening there?

MB: Revolut Business has been incredible since we launched it in 2023. We've seen 235% growth in transaction volumes over just the last 12 months. The opportunity is massive because you're right: small businesses have been underserved for years.

The really exciting development is our new merchant acquiring product. We've just launched physical terminals and payment gateways through "Revolut Pay." What makes this powerful is we have a double-sided marketplace: a large consumer base who already have Revolut on their phones, and a rapidly growing small business base. When you can connect both sides, you create real network effects.

How Revolut Australia Succeeded Where Other Neobanks Failed

DC: That's a significant expansion beyond your core FX and payments business. Speaking of expansion, where are you with the APRA banking licence?

MB: The process is ongoing, and it remains very important to us. A banking licence enables services like interest-bearing savings accounts and broader credit products. It also provides government guarantees on deposits, which builds customer trust and gives us access to more sustainable long-term funding.

But here's what's critical: the lack of a licence hasn't constrained our product delivery or business growth. We've been very deliberate about that. We've continued shipping products, growing customers, and most importantly, we reached profitability in 2024. That's a very different path from other neobanks in Australia.

DC: Indeed. Most of the local neobanks either failed or were acquired before reaching profitability. What did Revolut do differently?

MB: Our strategy was fundamentally different from day one. We established a strong foothold in payments and foreign exchange first: areas where we could demonstrate clear value and actually make money. Then we expanded the product offering from that profitable base.

A lot of other neobanks tried to be full-service banks from the start, which meant massive infrastructure investment before they had meaningful revenue. They were burning capital trying to replicate everything the Big Four do, just with a better app interface. That's incredibly capital intensive and the unit economics don't work until you have massive scale.

We took a different approach. Build what customers need, prove the economics work, then expand. Stay lean, stay focused, stay profitable.

COVID-19 Pivot: How the Revolut Australia CEO Adapted in 2020

DC: Let's go back to the beginning. You joined Revolut in February 2020 as the first Australia CEO. You started with three people, then literally one month later, COVID hit and the world went into lockdown. What was going through your mind?

MB: [Laughs] Honestly? It was a significant inflection point, to put it mildly. Here we were with a travel-oriented FX proposition, and borders just… closed. Completely. For what ended up being over a year in Australia.

But looking back now, I'd say it was the best thing that could have happened to us. It forced us to think much more broadly and pivot into new opportunities immediately. We accelerated our plans for US share trading, we introduced cryptocurrency exposure, we focused on international e-commerce. All the things that didn't require getting on a plane.

That agility, that bias to action, is core to Revolut's culture. Our founders backed us to make those pivots quickly. We didn't spend six months doing market research and business cases. We identified the opportunity, built the product, shipped it, learned from it. That's how we survived and then thrived despite the pandemic.

Revolut Australia's Remote Work Culture: 100 Employees, Work From Anywhere

DC: You mentioned culture, and I want to dig into that because you've built teams at Virgin under Richard Branson, at Bank of Queensland, and now at Revolut. How do those experiences compare?

MB: They're all very different cultures, but there are principles I've carried through. At Virgin, I learned the power of entrepreneurialism and brand: what it means to genuinely put customers first and challenge incumbents. At BOQ, I learned the discipline of running a bank, dealing with regulators, managing risk at scale.

What I've adapted for Revolut is being very specific about what type of people succeed here. We're rigorous about hiring problem solvers: people who can think critically and exhibit a strong bias to action. We assess that through interview scenarios, not just by asking people to talk about their CV.

DC: Your recruitment process has a reputation for being thorough. And you're doing all of this with a "work from anywhere" policy, which is quite different from the banking norm.

MB: The remote working policy works because of the discipline and mindset of the people we hire. We have high expectations for performance, ambitious quarterly KPIs, and structured measurement. There's a misconception that you need people in an office to have performance oversight. What you actually need is clarity on objectives, rigorous measurement, and people who are self-motivated.

If you've hired properly — true problem solvers with a bias to action — it doesn't matter if they're working from a Sydney office or a beach in Byron Bay. They'll deliver. If you haven't hired properly, having them in an office won't fix that.

DC: You now have 100 people in Australia. When you're hiring, what are the absolute non-negotiables?

MB: Problem-solving ability and cultural fit around action. I'd rather have someone who can think critically, move fast, and figure things out than someone with a perfect CV who needs to be told exactly what to do.

We're also looking for people who are comfortable with ambiguity. Revolut is a founder-led organisation. Nick, our founder, sets ambitious goals without caveats. His goal for us is to be the number one app in the finance category in Australia. Not "number one neobank" or "number one among challengers." Number one, full stop. You need people who find that energising, not terrifying.

Revolut Australia CEO on Taking On the Big Four Banks

DC: That's quite an ambition when you're competing against the Big Four banks who control 80% of the market. After six years and 1 million customers, how's that battle going?

MB: We're bringing genuine competition to a market that's needed it for years. The Big Four have had it pretty comfortable: wide margins, suboptimal user experiences, business models built on customer apathy. We're changing that equation.

What's surprised me is how quickly Australians have embraced an alternative once they try it. The word-of-mouth growth I mentioned earlier: that's people voting with their wallets and their recommendations. That doesn't happen if you're just marginally better. It happens when you're delivering something genuinely different.

Are we number one yet? No. But every customer we win, every dollar we save them, every feature we ship: we're getting closer. And unlike some of our competitors who've fallen by the wayside, we're profitable and sustainable. We're in this for the long term.

DC: Looking forward, what's the vision for the next 3 to 5 years?

MB: All our actions, whether it's our F1 sponsorship, our product development, our marketing, are focused on that number one goal. We want to be the app Australians open every day to manage their money. All their money. Spending, saving, investing, borrowing.

We'll continue expanding our product suite based on customer needs. The banking licence, when it comes through, will unlock more capabilities. We'll keep investing in making the experience better, more intuitive, more valuable.

But fundamentally, it's about meeting Aussie consumer needs better than anyone else. That's been our mission from day one, and it won't change.

Revolut Australia Careers: How to Join the Team

DC: For people interested in joining this journey, where should they look?

MB: Head to revolut.com and check out our careers page. We've got live roles across product, engineering, operations, commercial, compliance: pretty much every function you'd expect. If you're someone who loves solving problems, moving fast, and making an impact, we'd love to hear from you.

DC: Matt, congratulations again on the milestone. It's been an incredible journey to watch, and I'm proud that Tier One People could play a part in it six years ago.

MB: Thanks, Dexter. And thanks to you and the Tier One People team. We couldn't have done it without finding the right people, and that partnership has been crucial to our success.

Revolut Australia has 1 million customers and 100 employees nationwide. The company is certified as a Great Place to Work in Australia and is actively hiring. For more information, visit revolut.com.

About Tier One People

Tier One People is Australia's leading fintech executive search firm. Six years ago, Tier One People placed Matt Baxby as Revolut Australia's founding CEO - a placement that has delivered 1 million customers, $250 million in savings for Australians, and a profitable, sustainable fintech business.

That's what happens when you find the 1% who define what's possible.If you're building a fintech team or looking for your next role in fintech, visit tieronepeople.com or connect with Dexter Cousins on LinkedIn.