Nabin Banskota

The through-line

Nabin Banskota's recurring argument is that the hard part of company building has moved. Product is no longer the bottleneck, and neither, in his experience, is money. What separates a business that survives is distribution, honest self-assessment about how big the thing can actually get, and total commitment to it. "Building product is easy but how do we go to market?" [1] sits alongside a blunt corrective to founders reading their own press: "Just because you raised so much money it doesn't mean you will succeed" [1]. He has learned the same lesson from the other direction too, having tried a business part-time in 2007-8 and failed at it, which produced the rule he now applies to everything: "part-time does not work. You have to like, you know, go in full-time, 110%" [1].

The ambition has shifted in scale over time. His first venture was sold when he concluded it would not become the company he had imagined; with Niural the framing is explicitly generational and long-dated. "Our goal is like why not make it a hundred billion dollar business?" [1], with the qualifier that this needs patience: "let's give it ten years, at least ten years. We are in a big space. There's a lot of changes coming and let's dream big" [1].

On raising money before you have a product

He describes raising $12 million in a week [1], and treats fundraising as a trust problem rather than a numbers problem. Capital signals credibility to customers and partners as much as it funds the work: "If you don't have a good funding, they won't even trust you" [1]. What made speed possible was track record, not traction. "Investors are like you know okay I can actually invest in these founders because they have gone through that journey" [1]. He has laid out the mechanics of pre-product fundraising as an exercise in building investor confidence, crafting a narrative, and securing money on trust and potential, with speed, strategy and storytelling as the levers for attracting top-tier investors [3][6]. The counterweight is his warning that funding is not an outcome [1].

On distribution being the real constraint

Go-to-market is where he says the difficulty concentrates, and he is unsentimental about the tactics that work. "The cold call works really well in our business, right?" [1]. AI tooling makes the build cheaper without solving the harder problem: "Use AI tools but that's still a challenge because you need a way to distribute your product to the sales sales marketing like you know how do you do that?" [1]. He also accepts that go-to-market progress is not linear, and that regrouping is part of the job: "Sometimes you have to take like you know one step back before you take two steps forward" [1].

On AI and the shape of small companies

He expects team size and revenue to decouple. Within three to five years he predicts companies with a fraction of today's headcount "doing hundreds of millions of dollars revenue and it's only possible you know because of AI" [1]. Niural uses AI internally in that spirit, including generating employment contracts [1], and its platform applies AI to global workforce management from onboarding through compliance and real-time payroll [3][6]. He has already seen the leverage first-hand: his earlier company reached roughly $8 to 9 million in ARR in 24 months with a team of twelve or thirteen [1].

On building the team in Nepal

Niural's engineering base is almost entirely Nepal-based: "99% of our tech and product team is Nepal" [1]. He points to a pool of 60,000 to 70,000 contractors already working for foreign companies from Nepal [1], and argues the barrier to entry has collapsed because anyone can learn to code from YouTube and online courses [1]. His stated intent for Niural is to help companies hire Nepali talent, but with a condition attached to the talent side: "they also need to do their job and make sure that they are actually skilled enough to do that" [1]. Hiring is otherwise specialist and domain-driven; for Niural he brought in someone with a payment background and someone with a security background, "because security is also very very key in this business" [1].

On global employment, payroll and crypto rails

Niural sells U.S. payroll, employer of record, contractor management and PEO services across more than 150 countries [3][6]. He frames the EOR model as the answer to a structural problem COVID made urgent: without a legal entity abroad, companies could only engage foreign workers as contractors, which meant no health insurance, pension or stock options, and EOR lets them employ people full time with benefits [4]. The differentiation he claims is crypto. Where Deel, Oyster and Remote.com offer limited global crypto payroll, Niural was designed as an EOR platform for crypto while remaining a single HR tech platform covering payroll, contractor management, employee benefits and bill pay, with web3 users including Alliance DAO, Tensor, Flare, Hadron Labs, Omni and Glo Dollar [4]. On the payments side he has described using stablecoins to make cross-border payments faster, cheaper and easier, with employees and contractors paid in either stablecoins or fiat, and the platform built to bridge Web 2 and Web 3 customers under one user experience [5]. He is candid about the friction: regulatory hurdles in the US and banks' discomfort in working with crypto companies are the roadblocks to adoption, and he has argued for common sense regulation and expressed hope for bills that support the industry [5]. The origin of this thesis predates Niural. He and his co-founder nearly started a blockchain payments business in 2017, and abandoned it partly because blockchain engineers were essentially unavailable in the US at the time [1].

On pivoting and where ideas actually come from

The idea that built his previous company was not the founding idea. Harvest began as an investment management and broker dealer business aimed at retail women investors, close to what he had been doing at Citi, and pivoted within three months [1]. He generalises the lesson: "you can't be stuck to your idea all along. You have to look for opportunities and you know, if you find if you find something you know, and you know, if the pivoting of the idea makes sense, you have to do it" [1]. The new idea came from reading disclosures rather than brainstorming. They found the average customer pays about $786 a year in bank fees unrelated to any service taken, that banks book billions annually on fees, and that after acquisitions their systems are unintegrated and error-prone, with the Wells Fargo account-opening scandal as the extreme case [1]. All of it, he stresses, was public: "Everybody it's it's a publicly traded company SEC.gov when it's all available" [1]. They tested it manually by phoning banks on their own and friends' accounts before automating anything [1].

On the fee-negotiation bot and why domain knowledge mattered

The product became what he calls an AI super agent: a customer connects their account through Plaid, an algorithm identifies negotiable fees across a year of transaction history, and a chat bot negotiates with the bank, with the company taking 25% of whatever it recovered [1]. This predated the foundation-model era, so they built a mini LLM themselves, seeded by his co-founder Nami's exposure to transformer models at Twitter, and refined accuracy from 60 or 70% up to 99% [1]. The behavioural insight was that customers simply will not chase $15 or $40 charges: "people have inertia get to call banks... nobody has time to call the bank and ask for that" [1]. He includes himself, with an old Wells Fargo account from 2002 quietly accruing charges [1]. The pairing of skills is something he presents as decisive: "with her product manager background, she's more technical. I'm more finance. And it was a perfect recipe" [1], and his own domain knowledge of how to start an investment management business gave them a starting point even though the final product was different [1].

On knowing when the company you built is not the company you imagined

He is unusually direct about the gap between founder ambition and reality. Every entrepreneur wants a unicorn and an IPO, "But you know, like you have to be a little bit realistic as well" [1], and the honest question is whether the thing you have built over three or four years is genuinely IPO material. Acorns approached four or five times before he sold, and he accepted on the reasoning that this company "may not be the IPO company that we thought about" while Acorns could be [1]. Structuring the deal followed the same logic: taking equity in the acquirer was the whole negotiation, because they believed in the buyer's upside and wanted to be part of it [1]. He treats the outcome as vindication of the product rather than a retreat, noting Acorns raised its tier from $3 to $9 and realised roughly $20 to 30 million in ARR from it [1]. Timing mattered too: they had a term sheet in hand for a Series A and a neo bank in the pipeline, and sold anyway because the offer and the fit made sense [1].

On why he left a secure career, and what he tells founders

Twelve years across KPMG, JP Morgan and Citi Private Bank, where he built a finance team from one person to sixty and helped grow assets from $1 billion to about $30 billion, ended in boredom and in a compliance-heavy role he had not wanted [1]. He also names a constraint rarely discussed openly: being on an H1B visa restricted him from starting a company, so getting a green card came first [1]. The decision to leave was made against advice, "people called me like stupid" [1], and on an explicit calculation about age and regret: "I'm like 33 uh you know, and freshly married, right? If we don't do it now, this is not going to be time for me to do it" [1], and "I just don't want to be, you know, like when I'm in 60s and 70s, I don't want to regret that decision that I didn't take that leap of faith" [1]. He attributes the instinct to family, with seven siblings and a father who never held a job in Nepal, none of them employed by anyone [1]. That entrepreneurial pull also produced Nepal Tea Collective in 2017, which his brother runs while he helps from behind the scenes [1], and it grew out of the same earlier idea about selling Nepali tea directly and cutting out the middleman that he had failed at part-time [1]. His general advice to founders is consistent with all of this: solve real problems, build a support system around yourself, and be genuinely passionate about the mission [5]. On Niural specifically he says the motive is not the money but building something lasting on Nepali talent [1].

Takeaways

• Capital buys credibility, not survival: he raised $12 million in a week and still insists "Just because you raised so much money it doesn't mean you will succeed" [1].
• Pre-product fundraising is a trust and storytelling exercise, and prior founder track record is what makes investors move quickly [1][3][6].
• The bottleneck has shifted from building to selling: "Building product is easy but how do we go to market?" and cold calling remains one of the tactics that works [1].
• Expect AI to decouple headcount from revenue, with very small teams reaching hundreds of millions in revenue within three to five years [1].
• Nepal is a viable engineering base at scale, with 99% of Niural's tech and product team there and 60,000 to 70,000 contractors already serving foreign companies [1].
• Test the idea manually before automating it: they phoned banks on their own accounts before building the bot that eventually reached 99% accuracy [1].
• Sell when the company you have is not the company you imagined, and negotiate for equity in the buyer so you keep the upside [1].
• Never start a company part-time; the 2007-8 failure taught him it requires "full-time, 110%" [1].
• Crypto payroll's obstacles are regulatory and banking-related rather than technical, which is why he argues for common sense regulation [5].