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Max Junestrand: General AI models fall short for legal applications, tailored solutions are essential, and the legal sector’s AI adoption is reshaping competition

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Max Junestrand: General AI models fall short for legal applications, tailored solutions are essential, and the legal sector’s AI adoption is reshaping competition

Key takeaways

  • General AI models are often inadequate for legal data applications due to the complexity of legal workflows.
  • Fine-tuning general models for legal applications is typically ineffective, necessitating tailored solutions.
  • Building specific applications on top of AI models is crucial for their utility in legal environments.
  • The legal market has rapidly embraced AI technology, altering competitive dynamics.
  • Law firms are adopting AI to differentiate services in a traditionally low-differentiation market.
  • The legal sector’s historical lack of software solutions has created opportunities for AI-driven innovations.
  • Legal AI products must surpass foundational models to gain acceptance from tech-savvy lawyers.
  • AI software companies differ structurally from traditional software firms due to evolving model capabilities.
  • Rapid advancements in AI models can quickly render specific features obsolete.
  • Investing in product and engineering is vital for success in the competitive legal tech market.
  • A focus on product readiness can delay sales, ensuring quality and reliability.
  • AI adoption in law firms is driven by the need to offer better services at competitive prices.
  • The legal sector’s underserved status in software has led to pent-up demand for AI solutions.
  • AI companies must deeply understand model capabilities to offer differentiated products.
  • The fast-paced nature of AI development impacts product strategy and feature relevance.

Guest intro

Max Junestrand is the CEO and co-founder of Legora, the AI platform transforming how lawyers work across 800 customers in more than 50 markets. At 23 with no legal background, he co-founded the company in Stockholm, growing it from 40 to 400 team members worldwide. Legora recently raised $550 million at a $5.55 billion valuation in a Series D round to accelerate US expansion.

The limitations of general AI models in legal applications

  • General models are not sufficient for legal data applications, necessitating tailored solutions.

  • I think part of the paradigm was you should train your own models and like the general models aren’t great and fine tuning is gonna be really important for two reasons… fine tuning doesn’t really seem to work at least on the scale that we were operating.

    — Max Junestrand

  • Fine-tuning general models is often ineffective in the legal sector.
  • The complexity of legal workflows requires specific AI applications on top of models.

  • There was so much application that you had to build on top of the models to make them useful in your environment.

    — Max Junestrand

  • Tailored AI solutions are crucial for addressing legal data challenges.
  • Understanding the limitations of general AI models is essential for effective legal tech solutions.
  • The need for tailored AI applications highlights the unique demands of the legal industry.

Rapid AI adoption in the legal market

  • The legal market has rapidly adopted AI technology, surprising many observers.

  • Yes, it’s been like vivid but but second and maybe more importantly the law firm market is very interesting because it’s it’s like this perfect equilibrium with frankly like pretty low differentiation.

    — Max Junestrand

  • Law firms are incentivized to adopt AI to differentiate their services.

  • If one of them starts leveraging legora to offer a better service at a better price point.

    — Max Junestrand

  • AI adoption is driven by the need to stand out in a low-differentiation market.
  • The competitive landscape of law firms is evolving due to AI technology.
  • Law firms leverage AI to offer better services at competitive prices.
  • AI adoption is altering the dynamics of legal service offerings.

The gap in legal software solutions

  • The legal sector was underserved with software, creating demand for AI solutions.

  • I just think the legal sector was so underserved with great software for such a long time that there was this like a lot of built up problems that we could easily solve with llms but they were really hard to solve like pre llms.

    — Max Junestrand

  • Large language models (LLMs) address longstanding issues in the legal sector.
  • The historical lack of software solutions in law has created opportunities for AI.
  • AI-driven innovations are filling the gap in legal software solutions.
  • The emergence of LLMs has transformed the legal tech landscape.
  • Legal professionals are increasingly relying on AI to solve complex problems.
  • The underserved status of legal software highlights the potential for AI advancements.

The necessity for superior legal AI products

  • Legal AI products must outperform foundational models to gain acceptance.

  • If you showed up with a legal ai product it had to be better than the foundation models yeah otherwise they were just gonna say why are you deserving of my dollars.

    — Max Junestrand

  • Tech-savvy lawyers demand superior AI solutions.
  • Legal AI products need to demonstrate superior value to be adopted.
  • The competitive landscape in legal tech requires high-quality AI products.
  • Lawyers expect legal AI solutions to offer clear advantages over existing models.
  • The necessity for superior products drives innovation in legal AI.
  • Legal AI solutions must meet the high expectations of informed users.

Structural differences in AI software companies

  • AI software companies differ structurally from traditional software firms.

  • One of the unique things about an ai software company is that it’s tactically built differently than a traditional software company… we need to deeply understand model capabilities and then we need to bring that to our customers in a way that’s deeply differentiated.

    — Max Junestrand

  • Rapid evolution of model capabilities impacts AI company operations.
  • AI companies must navigate unique structural and strategic considerations.
  • Understanding model capabilities is crucial for AI software companies.
  • AI firms need to offer differentiated products to succeed.
  • The operational dynamics of AI companies differ from traditional firms.
  • AI software companies must adapt to the fast-paced nature of technology evolution.

The impact of rapid AI advancements on product strategy

  • As AI models improve, the relevance of specific features can diminish rapidly.

  • As models got better your features may not matter in six months.

    — Max Junestrand

  • Rapid advancements in AI impact product development strategies.
  • AI development is fast-paced, affecting feature relevance and strategy.
  • Product strategy must adapt to the evolving capabilities of AI models.
  • The speed of AI evolution necessitates agile product development.
  • Stakeholders must understand the implications of rapid AI advancements.
  • AI product strategies need to be flexible to accommodate technological changes.

The importance of investing in product and engineering

  • Investing in product and engineering is essential for success in a competitive market.

  • If you wanna be best well then you need to invest in product you need to invest in engineering and I think you need to build that culture of like reliability first.

    — Max Junestrand

  • A culture of reliability is crucial for market leadership in legal tech.
  • Product development investment is vital for achieving competitive advantage.
  • Engineering excellence is a key factor in legal tech success.
  • Companies must prioritize product readiness to succeed in legal tech.
  • Investment in product and engineering drives innovation and market success.
  • A focus on quality and reliability is essential for long-term success.

Balancing product readiness with market entry

  • A focus on product readiness can delay sales to ensure quality and reliability.

  • We actually had a time period in the company for six months where we didn’t sell basically because we weren’t ready to like hit the gas on onboarding a thousand lawyers a day.

    — Max Junestrand

  • Prioritizing product quality can impact immediate sales strategies.
  • Startups face challenges in balancing product development with market entry.
  • Ensuring product readiness is crucial for successful market entry.
  • Delaying sales to focus on quality can lead to long-term success.
  • Strategic decisions to prioritize product readiness can impact growth.
  • Companies must balance product development with market demands.

Disclosure: This article was edited by Editorial Team. For more information on how we create and review content, see our Editorial Policy.

How India’s $26.58 billion fintech market in 2026 reflects emerging market growth

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How India’s $26.58 billion fintech market in 2026 reflects emerging market growth

A vegetable seller on the streets of Mumbai taps her phone to a customer’s smartwatch, and payment settles instantly through UPI. No cash changes hands. No merchant fee bites into her margin. Five years ago, this moment would have been exceptional. Today it is routine. This everyday interaction, multiplied across hundreds of millions of transactions daily, is why India’s fintech market is projected to reach $26.58 billion in 2026 according to Fortune Business Insights.

The UPI revolution as economic foundation

Unified Payments Interface wasn’t invented by a fintech startup. It was designed by the National Payments Corporation of India, a quasi-government body, and launched in 2016. Yet UPI is the most important fintech infrastructure ever built by an emerging market. It handles over $230 billion in transaction value annually and processes billions of transactions daily, all with near-zero friction.

UPI’s success created the conditions for India’s fintech explosion. Because moving money is frictionless and free, fintech companies can build on top of this infrastructure without fighting payment rails. Digital lending platforms emerged because they could disburse loans instantly to customers. Investment apps exploded because moving funds to trading accounts cost nothing. Insurance-tech platforms found distribution channels through UPI-enabled payment flows.

This is why India’s fintech market can grow rapidly despite lower average per-capita income than developed markets. The unit economics favor accessibility over profitability per transaction. A lender in India might earn a smaller fee than a US lender, but serves ten times more customers at lower cost per acquisition.

Digital lending outpacing traditional finance

India’s fintech market growth is driven largely by digital lending. Platforms like Cibil, NIRA, and dozens of others are formalizing credit for users who never qualified for traditional bank loans. These borrowers might be gig workers, street vendors, or small shopkeepers. Traditional banks deemed them uncreditworthy because they lacked collateral or credit history. Fintech lenders use alternative data: phone payment history, e-commerce activity, UPI transaction patterns.

This market segment has minimal competition from traditional banks, which explains the explosive growth rate. Traditional banks serve the wealthy and salaried employees. Fintech serves everyone else. The $26.58 billion projection for 2026 assumes continued expansion in this lending segment as digital platforms refine their risk models and reach deeper into underserved populations.

Investor confidence and venture capital flows

India received $3.4 billion in fintech funding in 2025, according to Innovate Finance. This trails the US ($25.1 billion) but places India third globally after the UK. Venture capital flows reflect where investors see the largest addressable markets and lowest competitive barriers. India’s massive population, rising smartphone penetration, and underserved credit market check all these boxes.

Investors also recognize that India’s regulatory environment, while sometimes unpredictable, is generally pro-innovation. The Reserve Bank of India has created sandboxes for fintech experimentation. The government actively promotes digital payments through schemes like Pradhan Mantri Jan Dhan Yojana, which opened bank accounts for 400 million previously unbanked Indians. This policy alignment is rare among emerging markets and gives investors confidence in long-term market stability.

From domestic to regional dominance

India’s fintech market isn’t just growing domestically. Indian fintech companies are expanding into Southeast Asia and Africa, regions where they have cultural and linguistic advantages over US or European competitors. Companies like Razorpay and Pine Labs are building payment infrastructure for other emerging markets, effectively exporting the UPI model to Nigeria, Kenya, and Bangladesh.

This regional expansion adds a multiplier effect to India’s fintech ecosystem. Venture capital returns aren’t determined by India’s $26.58 billion market alone, but by the total addressable market across India, Southeast Asia, and Africa, which could exceed $100 billion. This reality attracts more investment capital and talent to Indian fintech hubs, creating a virtuous cycle.

Profitability and sustainability questions

India’s fintech market is growing, but many companies aren’t profitable. Digital lending platforms operate on razor-thin margins and face default rates that eat into returns. Investment apps compete on commission rates, compressing revenue per user. Payment processors have already been commoditized by UPI’s free infrastructure.

The $26.58 billion market projection assumes that this profitability challenge will be resolved through consolidation, regulatory clarity, and new revenue streams. Buy-now-pay-later platforms might capture more market share than expected. Wealth management fintech could become a high-margin segment. Insurance-tech might drive profitable underwriting. These bets are built into the forecast, but they’re not guaranteed.

Why emerging markets matter for global fintech

India’s fintech trajectory teaches that market size and growth rates depend less on GDP per capita than on addressable population and regulatory openness. Digital banks are transforming consumer banking globally, but in India this transformation is happening faster and reaching broader populations than anywhere else. The $26.58 billion projection for 2026 reflects this reality.

For investors and fintech entrepreneurs, India offers both opportunity and lessons. The opportunity is clear: a market with hundreds of millions of potential users and limited competition from traditional finance. The lessons are harder but equally important. Building fintech products for emerging markets requires different assumptions about average transaction size, risk tolerance, and regulatory oversight than building for developed markets. Companies that understand this sell to India successfully. Those that transplant US or European models fail. The $26.58 billion market is growing because Indian fintech companies have internalized this difference and built accordingly. The future of global digital banking includes a substantial Indian component, and that future is arriving faster than most investors anticipated.

India’s fintech growth cannot be separated from the broader transformation happening globally. The role of venture capital in fintech growth across emerging markets is increasingly shaped by what investors have learned from India: infrastructure-first approaches like UPI create more durable ecosystems than app-layer competition alone. The lessons from India’s model have already influenced fintech policy in Brazil, Ghana, and the European Union, each of which has developed real-time payment rails that mirror UPI’s core architecture. India is no longer a market that adapts external fintech models. It is now a primary source of those models, and its $26.58 billion market size reflects the global premium investors place on that proven, replicable approach.







OKX, HashKey Back VPBank’s CAEX in Vietnam Crypto Pilot

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OKX, HashKey Back VPBank’s CAEX in Vietnam Crypto Pilot

CAEX, a crypto platform linked to the Vietnam Prosperity Joint Stock Commercial Bank (VPBank) ecosystem, said OKX Ventures and HashKey Capital are backing the company as it seeks to qualify for Vietnam’s pilot regime for crypto exchanges.

CAEX said Friday that the two offshore companies will join VPBank Securities (VPBankS) and technology partner LynkiD as shareholders.

According to a release shared with Cointelegraph, their investment is intended to help CAEX reach Vietnam’s minimum charter capital threshold of 10 trillion dong (about $380 million), a key condition for participating in the pilot program.

Vietnam pilot sets high bar

The move comes as Vietnam’s Ministry of Finance and State Securities Commission press ahead with a five-year crypto pilot that will admit only a limited number of licensed digital asset service providers. Officials have said no more than five enterprises will be allowed to operate exchanges under the pilot, which opened its licensing window on Jan. 20.

The framework also caps foreign ownership at 49% and requires at least 65% of capital to be held by institutional shareholders, creating high barriers to entry even for bank-backed contenders.

Authorities have also signaled they may block access to unlicensed overseas platforms once the first onshore exchanges are operational, raising the stakes for foreign firms seeking a compliant route into the market.

A spokesperson for OKX told Cointelegraph they could not disclose the size of the investment or the companies’ stakes in CAEX, nor whether the investment confirms the exchange’s selection in the pilot, saying it would “not be appropriate to comment further on the regulatory process.” However, they said the investment would enable CAEX to meet the capital requirements to pursue entry into Vietnam’s regulated crypto pilot program.

CAEX is part of VPBank’s broader financial ecosystem and previously said it was in the final stages of raising its charter capital to 10 trillion dong to qualify for the pilot, while VPBank is one of Vietnam’s largest private lenders.

The OKX spokesperson said that, as a strategic partner, the company would work with the other shareholders “as appropriate” to ensure CAEX has “the financial strength and technical know-how” to meet user expectations and regulatory standards. Potential areas of collaboration include technical infrastructure, security systems, compliance and risk management, they said.

Related: Banks want to run Vietnam’s crypto exchanges, Boyaa’s $70M BTC plan: Asia Express

Vietnam’s crypto market has boomed, but regulation is tightening

Vietnam’s crypto market has boomed in recent years, with Chainalysis ranking the country fourth in global crypto adoption in 2025. However, that growth has been marred by several high-profile scams and fraud investigations, giving regulators additional impetus to tighten control.

Vietnam ranks fourth in global crypto adoption. Source: Chainalysis

In March 2026, Vietnamese authorities detained multiple ONUS-linked suspects after alleging they used false promotions and manipulated token trading to misappropriate billions of dollars of investor funds through the crypto platform.

The spokesperson said Vietnam is an important market for digital asset innovation, and that the “development of a regulated framework” is a “constructive step” for the country’s industry.

Big Questions: Is China hoarding gold so yuan becomes global reserve instead of USD?