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Generating substantial portions of a codebase with AI could make it harder to protect that software, legal experts say.
You Let AI Help Build Your Product. Can You Still Own It?
By: Nate Bek
Startup developers should treat their AI coding assistants as copilots, not autopilots.
That’s my biggest takeaway from CenterForce’s Governance & Strategy Summit, held Wednesday at Bell Harbor International Conference Center on a sunny day in Seattle’s Belltown neighborhood. In a room full of legal minds from across the country, my goal was to take away what matters most to startup founders working with AI.
Two high-profile AI legal battles framed most of the discussions on stage: one centered on patents, the other on copyright.
In the patent case, computer scientist Stephen Thaler challenged the U.S. Patent and Trademark Office after it refused to grant patents for inventions created by his AI system. In the copyright case, artist Jason M. Allen sued the U.S. Copyright Office after it denied protection for an award-winning image he generated using AI.
“We have some information from cases that are in different contexts, where multiple humans are debating who is an author of a work,” said Eric Tuttle, a partner at Wilson Sonsini, speaking on stage in a session titled “Copyright in the Age of AI: Navigating New Frontiers.”
Courts are likely to use that standard as a starting point, but how it applies to AI-generated content is still unclear.
Brian McMahon, senior copyright counsel at Microsoft, emphasized a practical takeaway from the latest copyright guidance. While prompts alone are not eligible for copyright, there may still be a path to protection if human input shapes the final result.
“As a company, if you're using the AI tool in your business, so long as you’re not just cranking out the output, throwing that into the stream of commerce… and you're instead taking that extra step and modifying the output in some way, I think that's a good opportunity,” McMahon said.
He explained that if a human’s expressive contribution can be detected in the AI-assisted output, that work may qualify for copyright protection.
Tuttle then raised a key concern for software companies. If AI tools are generating a significant portion of your code, can that code and the software built from it be protected under copyright law? That’s still an open question that has not yet been tested in court, he says.
For startup founders building with AI, I believe this is the crux. If your AI system is generating code, how much of it can you actually claim? How much human input is required before that code becomes yours?
At this moment, I asked the panelists to double-click on this point: If you're a startup, the whole ethos is to move fast, break things. So, then, what is your pro bono advice with AI coding tools? Is it to not move so fast using Cursor or GitHub Copilot that you put yourself at legal risk? Or is it to just be aware of these cases making their way through the legal system and continue building fast with AI?
Here’s what they told me:
Tuttle said AI is being widely adopted and it is unrealistic to expect developers to avoid tools that improve efficiency when writing code. He emphasized the importance of understanding the risks that come with relying on AI, especially in the context of intellectual property.
When it comes to copyright, Tuttle warned that generating substantial portions of a codebase with AI could make it harder to protect that software. He encouraged founders to consider other ways to safeguard their work, such as intellectual property protections, technological measures, and contracts.
Tuttle pointed to the idea of “autopilot” versus “copilot.” The more humans are involved in revising, editing, integrating, and making decisions about the code, the stronger the case for authorship. He added that being able to document that human input could be important if the code ever becomes the subject of a legal dispute.
Glory Francke, head of privacy and data protection officer at GitHub, advised founders to pay close attention to the tools their developers are using.
She recommended sticking with commercially offered versions of AI tools rather than free ones. Commercial tools are more likely to protect user privacy by not retaining prompts or using them for training.
Francke also urged teams to review and configure their settings carefully. For instance, some tools allow you to block code suggestions that match publicly available code, preventing them from being used in completions. Others offer code referencing features that show the origin of suggested code and its license.
Francke encouraged teams to “get to know your admin” and make use of those settings to reduce risk and stay compliant.
While much about AI is still unknown and outside of our control, Francke said companies can take practical steps now by choosing the right tools, using the right settings, and making sure prompts are not being used for training if that is not the company’s intent.
“Use the tools that are there to help protect against the harms that you're concerned about,” Francke said.
Jonathan Talcott, shareholder at Buchalter, stressed the importance of understanding open-source licensing when using AI coding tools. He advised companies to put systems in place to manage open-source compliance, whether through features built into tools like GitHub Copilot or external scanners like FOSSA.
“You want to be making sure that you have some type of tool in place,” he said, to track and address licensing issues before they become legal problems.
On the topic of IP and copyright ownership for AI-generated code, Talcott said much is still uncertain and will depend on how current legal cases are decided.
“That’s going to be impacted by how these cases play out,” he said.
Still, Talcott echoed what others on the panel made clear. Avoiding these tools entirely is not a realistic option for software startups.
“Unless you want to be left behind,” he said.
Disclosures: I’m no lawyer, and this isn’t legal advice. The speakers quoted here also made clear they were speaking for themselves, not their respective companies. Ascend is a client of Wilson Sonsini.
Investing in Avante
By: Kirby Winfield & Nate Bek
Today, we’re excited to announce our investment in Avante, the infrastructure layer for modern employee healthcare benefits. Fuse led the $10 million Seed round, joined by HighSage Ventures.
The employer-sponsored benefits market is under strain. HR teams are overwhelmed by administrative work, forced to navigate legacy tools that were never built for today’s demands, while employees are making high-stakes healthcare decisions without the information they need. The current ecosystem relies on a patchwork of point solutions that fail to integrate or scale.
U.S. employers spent more than $1.3 trillion on healthcare last year, making it the second-largest expense after payroll. Despite its size, this market remains under-innovated and inefficient. Benefits are just too important — too expensive — to be run on spreadsheets and outdated portals.
Rohan D’Souza, Avante’s CEO and co-founder, came to us with a clear read on the market. He had spent months in the field talking to customers, brokers, and benefit administrators. The feedback was consistent. Teams want better insights, more automation, and a way to deliver a high-quality benefits experience without adding headcount.
Rohan’s findings align with what we’ve seen. In a survey of VC-backed startups with ties to Seattle, nearly all reported offering health benefits as soon as they raise a priced round. Coverage is table stakes, even for teams under 10. At larger enterprises, the problem only compounds. Dozens of vendors, thousands of employees, and increasing compliance demands create an environment where small inefficiencies turn into massive operational and financial drag.
Company leaders know it matters — for recruiting, retention, and employee well-being — but the infrastructure to support that coverage is often shallow or nonexistent.
Avante is building the operating layer companies will need to respond to these pressures. The platform unifies critical data across claims, vendors, costs, and employee engagement. It gives employers a complete, real-time view of their benefits program and helps them make smarter decisions. From plan design to vendor selection to wellness initiatives, everything is surfaced through a single interface with AI-powered insights at the core.
The result is a more scalable benefits operation that improves outcomes for employees and drives efficiency for employers. Avante automates support, integrates cleanly into existing systems, and captures employee sentiment to identify gaps and inform strategy.
We invested, in part, because we believe this team is uniquely positioned to build this product and go after this market.
Rohan previously served as Chief Product Officer at Olive AI, where he played a key role as the healthcare tech company scaled to a $4 billion valuation.
He’s joined by Co-founder Carly Eckert, MD, Ph.D, an epidemiologist and former EVP at Olive, who now leads innovation and impact at Avante.
Kabir Shahani, executive chairman, founder of Amperity, one of Seattle’s most successful enterprise startups.
Nick Cecil, founding head of engineering, brings deep technical expertise from his time as director of software engineering at Salesforce
This is a rare combination of healthcare fluency, product rigor, and enterprise software execution.
With the funding, the team is looking to expand across key functions. Avante is recruiting for five positions: Senior Product Manager, Head of Revenue, Software Engineer, Junior Software Engineer, and Analytics Engineer. All roles are live on the Ascend Job Board.
We believe Avante will define a new standard for how companies manage and deliver employee benefits, and we’re proud to be a supporter from day one.
Photos via Human[X]
Dispatch from the Desert: Human[X], Robotics, and the AI Power Shuffle
By: Nate Bek
Vegas felt like an odd place for a conference about intelligence. The artificial kind, or otherwise. But maybe that made it the right setting.
The slot machines never stopped blinking. The escalators kept funneling us between velvet-curtained ballrooms and DJs and mimosa towers to the floor of a city built to overwhelm. There’s something about putting thousands of people in a place designed to keep you distracted — and asking them to think about the future.
Human[X] was the first AI conference of its kind, but it didn’t really feel like a debut. It felt more like a checkpoint. I came in with a few themes top of mind: robotics, AI labs turning into real institutions, and how venture is reacting to the next wave. Those threads ended up tying most of the sessions I attended together.
From left: Hugging Face, Co-founder; Rachel Metz, AI Reporter at Bloomberg; Arthur Mensch, Co-founder & CEO at Mistral AI; and Metz.
The most grounded — and maybe most striking — observation came from Thomas Wolf of Hugging Face.
"I'm pretty sure 2025 is going to be the year where robots start to work."
Not in theory. In kitchens. In warehouses. On wheels. Reinforcement learning breakthroughs are beginning to land outside the lab, and there’s a new confidence about it. He also flagged what’s missing: openness.
“They're all closed-source… In the future, where robots are everywhere, I would love a part of them to be open so that we know exactly what's inside. Maybe we even build them ourselves."
That tension came up in other corners, too. Brad Porter, founder of Collaborative Robotics, was clear about the practical challenges of humanoid robots. Drawing on his time at Amazon:
“We needed robots that could work collaboratively in and around humans. It was not clear how you were going to safety rate these 300-pound mechanical things that can fall over really easily."
What stood out to me was how rarely this kind of specificity comes up in general AI discourse. These weren’t hypotheticals. They were design constraints from people who’ve already tried and course-corrected.
Alfred Lin of Sequoia framed it well — almost offhandedly:
“The desert is not designed for humans. Just like highways and space.”
Form should follow function. The future of robotics may feel novel, but its success will come down to how it adapts to spaces we already occupy.
From left: Mike Krieger, CPO at Anthropic; Moderator: Alex Heath, Deputy Editor at The Verge; and Kevin Weil, CPO at OpenAI.
Mistral’s session with Arthur Mensch made this shift clear. He spoke about deploying vision models on the edge.
“When the robots [are] in the outer world, it won’t have a very strong connection… you want to have low-latency decision making.”
It’s a reminder that not every AI breakthrough lives in the cloud. Edge environments are where robotics will have to operate, and some of the more forward-looking labs are already planning around that.
He also spoke about Mistral’s recent partnership with Helsing, and the demand for sovereign infrastructure in Europe:
“There's a lot of demand to have a regional cloud because that brings more sovereignty and leverage from an economic perspective… As it turns out, we're pretty good at operating GPUs.”
Labs are becoming more than R&D centers. They’re shaping policy, forming defense partnerships, and influencing national strategies.
Anthropic’s Mike Krieger talked through another facet of this transformation — the delicate balance between building products and staying aligned with partners:
“I called basically all of our leading coding customers to give them a heads up that we're launching Claude Code… We're hearing from people using both.”
He was transparent. It was a small window into what it takes to navigate product expansion when your customers are also your ecosystem.
There was a lot of quiet confidence in the VC session with Navin Chaddha, Lauren Kolodny, and Martin Mignot.
“We're still not at the peak of the hype cycle,” said Chaddha. “VCs have enough money.”
He cited $307 billion in dry powder. Valuations, in his view, still have room to move. LPs remain focused on DPI. “It’s a financial services business,” he said toward the end — a reminder that beneath the storytelling, this is all still portfolio math.
Martin Mignot pointed to the upside:
“We can have trillion dollar VC-backed companies when they go public… A lot of value is being captured by VCs.”
The liquidity window may be narrow, but the bets are only getting larger. And many of them are moving into real-world infrastructure — robotics, autonomy, defense.
In between sessions, I kept thinking about how far we’ve moved from the “chatbot moment.” Human[X] was filled with serious people talking about serious problems. Model architectures were mentioned less than operational realities.
Vegas was a strange backdrop. Too loud. Too bright. Too artificial. But also kind of perfect. A place built on simulation, where beneath the surface you start to feel what’s real.
Early-Stage Hiring, Decoded: What 60 Seattle Startups Told Us
Startups are getting smaller. The average headcount for Series A startups dropped to 15.6 in 2024, down from 18.7 in 2019, according to Carta. The shift is most visible in AI startups, which can scale faster with fewer people thanks to new tooling and automation.
Founders are rethinking team composition from day one. When to hire, who to hire, and what roles to prioritize have all changed.
To bring more clarity to the hiring journey, we surveyed more than 60 VC-backed startups with ties to Seattle. The companies range from pre-seed to Series B and span industries, though many are AI-native.
We asked when key team members typically join — from founders to the first sales, marketing, ops, and support hires. The results offer a snapshot of how early-stage teams are actually built in this market.
Here’s what we found.
No surprise — every company starts here. In our survey, every startup had a Founder/CEO at formation, and the role remains the anchor through every stage.
Dilution occurs as a startup grows, but the Founder/CEO most often retains at least 15% equity in their startups in the early stages.
Most startups add at least one cofounder early. At most startups, cofounders are in place by the time a company raises its pre-seed round. After that, it's rare to see new cofounders join.
This is often hire No. 2 or 3. Most often, founding engineers join before the seed round, typically on the heels of a pre-seed raise or right before one.
This role tends to come later. Most startups wait until early post-seed or Series A to bring on their first dedicated sales hire. Founders tend to lead early sales themselves.
Marketing is typically a Series A hire. A few outliers bring someone in earlier, especially if they’re selling to consumers or developers, but most wait until they’ve found some product-market fit.
This hire follows revenue. Startups usually add a customer support or success role around the same time as their first reps — often in the Series A window, occasionally just after.
Ops shows up across the board. Some companies bring someone in early to help the team run smoother; others wait until they hit complexity. Most Series A companies in our survey had at least one ops-focused hire.
Early-stage startups in our survey start offering benefits as soon as they raise a priced round. Seed is the most common trigger. Health coverage is table stakes, but perks like wellness stipends and 401(k)s often wait until Series A or later.
*METHODOLOGY: We surveyed 60 verified, VC-backed startups with Seattle ties. Responses were collected in Q1 2025 and included startups from pre-seed to Series B. While this sample is directional and not comprehensive, it offers a useful glimpse into how early teams form. Take the data with a grain of salt, but we hope it brings more transparency to startup hiring in the region.
Booming: An Inside Look at Seattle's AI Startup Scene
By: Nate Bek
Seattle’s AI startup boom isn’t loud, but it’s reshaping the city’s future in real time.
Startups are multiplying, talent is clustering, and investors are taking notice. “We are, by any measure, the second-most important city in the United States for AI,” says Kirby Winfield, founding general partner at Ascend.
While Silicon Valley still dominates, Seattle has quietly built an AI battleground — powered by a deep well of technical talent, the cloud giants that fuel modern AI, and world-class research institutions.
On KUOW’s Booming podcast, Joshua McNichols sits down with Kirby to break down the forces driving this surge, the hidden network of startups shaping the industry, and whether Seattle’s AI moment is built to last.
(Join us and consider donating to support local journalism.)
We’ve lightly edited this transcript for brevity, added references, and clarified a fact. Enjoy! — Nate 👾
Joshua McNichols: There's an invisible startup boom happening in Seattle right now.
Kirby Winfield: I'd say, gun to my head, how many startups are operating today in Seattle with AI at the core of their technology? 1000, at least.
Tons of artificial intelligence companies have cropped up in the area, and they're rivaling sectors like health tech and computer software, but the AI startup scene looks a little different than other industries… Seattle's secret AI startup boom could transform our economy. But are we in a bubble, or is it here to stay? I sat down with an insider to find out.
His name is Kirby Winfield, and he's a venture capitalist for this company called Ascend. I started out by asking him to explain to me exactly what he does.
I take money from investors, and I give that money to startup founders to help them build the future. I think the best analogy I've been able to make for folks who don't know about venture capital is that we're sort of like betters at a race track. There are lots of horses and lots of jockeys, and there are lots of ways to try to handicap the race. You might look at the horse's training speeds, or you might look at the horse's provenance, but in venture capital, it's much more about the jockey than the horse. So my job is to find the right jockey, the right startup founder, and then they take that technology horse and ride it to victory. That's the idea.
And when you're looking at jockeys, you're focusing on AI?
Our fund is entirely focused on artificial intelligence applied to enterprise problems. So basically, businesses selling to businesses technology solutions using AI.
So you're just the guy to talk to.
I hope so.
Okay, so can you rank Seattle's place in the AI world? I mean, where does it sit in relation to other cities?
So we typically say that Seattle is HQ2, or the second headquarters of the US for AI. Obviously, Silicon Valley is HQ1. There's just such a density of technology there. But we are, by any measure, the second most important city in the United States for AI. We have one in four of the AI software engineers in the US living in Seattle. We have the second largest population of software engineers in the US period. And about 65% of AI compute usage runs through the cloud. The three largest providers of which are based in Seattle.
But the cloud can be anywhere.
But the talent that runs the cloud and builds the software that allows the cloud to work for AI lives in Seattle.
OpenAI has opened an office here. We've got the Allen Institute for Artificial Intelligence. We've got Microsoft. These are companies I've heard of, but I've also heard that there's a hidden startup scene here. Can you give a couple examples of AI startups rooted in and around Seattle that could really change people's lives?
Absolutely. Probably the most popular one is read.ai. And this is just a comprehensive AI assistant that sits in your email inbox and in your Word documents and on your Zoom calls and coordinates information for you, so that you're always aware of the context of what you're doing and why you're doing it and what may have been related to it before. And this can work for personal situations as well. It keeps track of conversations and historical data that's associated with a given relationship. So this can be useful for, let's say, you're like me, and you have a child with a disability, and you're coordinating across dozens of service providers and caregivers and schools and doctors. That can be challenging to manage on your own and read.ai has software that puts that all at your fingertips. That can be really life changing for people.
That one speaks to me because it's so complicated even to do a search in my email inbox for the right correspondence. If I had like a little personal assistant tying all those threads together and bringing together other parts of my life outside of email, that could be useful.
Yeah, it really is like having almost a precognition of what the needs of a certain conversation or experience are going to be, and then populating your dashboard, if you will, with all the information you will need before you even know you need it. It's pretty magical.
I've heard that there's not a lot of investor money flowing to startups in Seattle. So is AI an exception to that?
I'd say AI is an exception to that in almost every market in the world right now, because there really is this kind of Cambrian explosion happening, and so there's been a lot of money coming into AI. It helps that we have Microsoft, Amazon, Google Cloud, Apple's HQ2 for technology. We have, I think, 200 now technical headquarters for large tech companies based here. What happens when you have that kind of technical talent base is that those people want to leave those big companies and start their own little companies, and that does attract investment capital. Not only from people like me and other firms in Seattle, but we did some research, and 90% of the investment money in technology companies in Seattle comes from outside of Seattle.
So why is Seattle a good incubator for AI? Are there reasons besides the labor pool?
I think there are. We have probably the greatest independent center for excellence for AI in the United States, if not the world, with Paul Allen's Institute for Artificial Intelligence. That is a one-two punch when combined with the Allen School for Computer Science at the University of Washington.
We've backed a number of PhDs out of the UW to start their own companies, and those people have huge influence on hundreds and hundreds and hundreds of very talented PhDs and masters students and computer scientists who then go on and leave this market and go to do their masters at Stanford, or go to start a company in Silicon Valley. And when their mentors start a company here, they come back. And so all of these centers for excellence become magnets for talent. That's one really important aspect.
Certainly the hyperscalers — which is tech slang for Amazon and Microsoft and Google — also create a real bed of support. It's not just about the talent pool. If you're a software engineer who's an expert in AI, you want to apply that talent to big problems. And some of the biggest problems are ones that are experienced by these big companies that are located in Seattle. And so the problem space, if you're experimenting with AI technology, is really rich and dense in Seattle in a way that it's not really anywhere else in the country, maybe outside of Silicon Valley.
We've seen here in Seattle in the past, when an industry is booming, the benefits extend far beyond that particular industry. What kind of impact could AI have on the economy in Seattle, beyond just the companies themselves and making somebody like you money?
I think AI is poised to have an impact that maybe we don't understand on Seattle. An example that I always use is my daughter, who has Down syndrome. She's 15, and she will not be able to drive a car. Seattle is notoriously not a great place for public transit. For her to be able to take an autonomous vehicle, where I don't have to worry about her safety with regards to a driver, that's life changing for her and it'll be life changing for a lot of other people in this market in particular.
You think about drug discovery. You think about energy. You think about the impact on maritime such as autonomous maritime vehicles. We've been talking about ferries here for 25 years, and the lack of skilled labor, aging of the ferry workforce, and the lack of persistently serviceable vessels. There's no reason we shouldn't have autonomous ferries in the next 10 to 15 years.
I think a lot of what's going to change in Seattle is going to change a lot of other places, too. But there are those areas where I think we're probably going to see a little bit more impact just because of how our geography is positioned
Quick side note: I know for public transit nerds, they probably would take issue with the idea that public transit is bad here. We got Light Rail opening all over the place, all that kind of stuff. But I take your point. It's a car-oriented city. That's its legacy that it's trying to overcome.
We don't have the time to debate this here. The ridership numbers might suggest otherwise.
A topic for another time… So China rattled the markets recently with news that it's developing new, cheaper AI models like DeepSeek, for example. How could competition from China and other places affect our startup scene here in Seattle?
My perspective on China and DeepSeek, in particular, which is a new large language model, is that it was very disruptive because it actually outperforms some of the big closed-source models, like Open AI and Anthropic. So when it comes to that, I actually get excited, because if these state-of-the-art open source, low cost to free models become ubiquitous, that actually means that there will be a Cambrian explosion of AI applications and agents developed by companies that could not otherwise have existed because it's cost prohibitive to conduct the training of these models for an individual company. There's actually this paradox, Jevon's paradox, when the resource becomes freely available, there's an initial decrease in the volume of use, but then it should exponentially explode, just because it's so broadly available.
So DeepSeek and other cheaper, large language models could actually result in a startup explosion here, whether or not this is good or bad for Microsoft or other big AI companies here .You see this as being good for startups?
Absolutely.
One thing AI is making it easier to do is to start companies with very few employees, because you can use generative AI to do a lot of your engineering. So what does the low number of employees needed to start a company today mean for jobs in this region? Could we have an AI startup boom in Seattle that we don't really feel?
I think that's entirely possible. We're already seeing companies that we're backing hiring fewer people to do the same work. *Instagram, when it was bought by Facebook, had 13 employees — and it was bought for $1 billion. And that was before generative AI. So I absolutely think it's not a matter of if, but when, there's a billion-dollar company built by one person.
Wow.
That said, I do think one of the only things that's not going to be replaced are functions within a company that are inherently human. I was talking with someone today about the radical increase in AI-generated inbound sales emails. And there are all these startups that have raised hundreds of millions of dollars to build an AI sales representative. And what happens is, as a potential buyer, you realize it's all AI generated, and you delete everything. Before, if I got a cold sales email, I had to in the back of my mind, at least think, "Well, someone took the time to write this email and to notice something about my company or me that made them think that maybe I would buy their product," because they wouldn't do it if they didn't think I would, because their job is to sell their product. So there was this qualification that you could tell it happened by a human and it made you lean in a little bit and say, "Well, maybe not today." But if they email me again in six months or whatever, maybe I'll reply and say, "No, thank you," because I'm a nice person. And if I had their job, which I have, I would want someone to reply. That has been wiped off the face of the earth now.
So actually what becomes valuable is warm, human introductions and personal networks. Which then begs the question of the return to office in this virtual world we live in and relationship building. And I think human relationships become the capital that's probably the most valuable in the next five years, at least, and I do worry a little bit about what that means for folks who are in the first decade of their career and may not have accumulated that may not know how to.
I've often thought that as AI progresses and becomes indistinguishable from humans, we will just move the bar — redefine what it means to be human, or we'll just keep moving the goal posts and cherish those things, or even fetishize those things that are sort of unique to human. Even if that means being chaotic.
I totally agree.
We seem to go through these waves where everyone is investing in something, and then there's too much of it, and then the bubble pops. Can we just be in another one of those cycles?
We, without a doubt, are. There's nothing new under the sun, as Shakespeare said. I lived through the commercial Internet boom and bust, and operated startups through it. I lived through the mobile era of boom and bust and the cloud era. And now we're in the AI era, and just like those others, what you get is a very big rush of capital and excitement at the very beginning, when the technology is new. And the challenge is that that capital is chasing ideas that are not very well formed about what the future is going to look like.
The future gets clearer. As it gets clearer, the costs for the technology tend to come down, which, combining that future being clearer and the cost coming down, results in a completely different landscape than what existed when the wave started to build. So that wave crests, and then the next wave comes. And I think we are just now at the cresting of the first wave, and the beginning of the next wave. And the next wave, I think, resembles the wave that came after the internet bust, where 80% plus of the commercial value on the internet today was created. And so that's what's exciting about investing in AI, at this moment in time is that I do think we're at the beginning of the real growth.
Stash Your Cash: Stifel Bank’s Minh Le on Startup Banking
By: Nate Bek
Startup founders flush with fresh cash face a whirlwind of priorities — milestones to hit, engineers to hire, and a product to build and polish. But a practical, often overlooked question looms: where should that cash live, and who can be trusted to manage it?
To get answers, we spoke with Minh Le, Managing Director at Stifel Bank and a veteran startup banker in Seattle. With two decades of tech banking experience, he has guided startups from their earliest funding rounds to successful IPOs. Along the way, he’s been a trusted advisor, securing lines of credit to help companies navigate turbulent times.
(Ascend banks with Stifel, part of a longterm relationship and trust of Minh.)
We’ve put together a detailed FAQ for founders seeking guidance on banking best practices. This resource offers practical insights for founders making financial decisions or seeking a trusted banking advisor.
*We've edited this conversation for brevity. Enjoy! — Nate 👾
Nate: Thanks for chatting with us, Minh. If I am a founder, how many banks should I have?
Minh: We recommend having two banks — one to serve as your primary bank and is a good strategic fit (has products and expertise that are tailored to your business), great service and can also go beyond banking to provide relevant business advice and connections to customers, talent, and investors; the other would service as a back-up that you have a couple of payrolls of cash at just in case your primary bank fails. A key component in this is making sure that your cash is also safe where you have it.
Cash is critical for any company, so founders should know where its deposits are held, how secure the funds are, how accessible the funds are, and what they’re earning on the funds.
At Stifel, our clients benefit from our Insured Cash Sweep product, which allows them to keep their cash completely liquid (they can access their cash same day) and FDIC insured up to $250 million. The rate we pay is also very competitive for fully safe and fully liquid.
As VCs, we are constantly pitching our platform services and value add to founders. Do startup bankers offer similar support for founders?
Different banks and bankers have different skill sets and approaches to working with their clients, some of which are mostly focused on their banking products and loans. At Stifel, we provide value to founders in two ways: focus and platform.
Focus: We largely work with founders and companies that are disruptive and often are venture backed (or are planning to raise venture capital), so our products and services are tailored to this profile. We have very experienced venture bankers that are focused on being great, strategic business partners for our clients. If a client needs banking services, we’re there to provide it, but often it’s other things like specific business advice or connections to investors, talent, customers, strategic partners, etc. that is a pressing need and that we can provide them. Also, the advice and the creative debt solutions are important as well. You can’t get those things from most banks.
Platform: We would argue we’re the best and provide the full platform, which includes all of those things mentioned above, but also extends to investment banking and wealth advisory (optimizing liquidity events for founders). Venture Bank + Investment Bank + Wealth Advisory can support a founder at all stages both personally and professionally, we believe that’s very compelling and unique to Stifel.
What are the most common banks startups are working with these days?
Stifel Bank, Mercury, Brex, JP Morgan, Silicon Valley Bank
What are the pros/cons across various banks (big strategically important vs. regional)?
You’ll get widely differing perspectives here depending on who you ask. I’ll try to be objective:
• Stifel – I’ll start with my naturally biased beliefs on Stifel, which I honestly believe:
Pros:
Best Team - Our team has decades of experience supporting entrepreneurs. The person that ran the Tech practice nationally is with us as is the person that ran enterprise software nationally, the person that ran frontier tech national, the person that ran Corporate Banking nationally (the group that worked with late stage and public companies), I ran Washington and Western Canada, my counterpart that ran LA, etc.
Broad Platform – Having the robustness of the expertise of our wealth advisory business and investment bank to pair with what we do on in the commercial bank for companies is a big differentiator, and quite frankly a vision we were trying to realize at SVB the last 5yr+ before the bank collapsed (we were building an investment and bought Boston Private to build out our private banking capabilities). Stifel is the 7th largest wealth advisory business in the country by number of advisors, and half a trillion of assets under management and the leading middle market investment bank, very active in technology, healthcare, and related sectors, Stifel Investment Banking is #1 in equity deals under $1 billion market cap*. *Source: Dealogics M&A Analytics and 451 Research as of 8/19/24. Other company data available on www.stifel.com/investor-relations
Focused on Early to Growth - Having an affiliated investment bank focused on middle market companies is important as it shows Stifel has expertise more applicable to a broader swath of our clients. The larger banks can have a hard time getting the attention of their investment bankers that are incentivized to only work with the multi-billion dollar transactions, of which most tech outcomes are not.
The Right Size – We’re a $31B+ bank, which makes us large enough to work with large growth stage companies, while being very nimble in how we operate. Larger banks (banks that are $200B+ like JPM, HSBC, and SVB) are considered LFIs(Large Financial Institutions) and have a substantially higher level of regulatory oversight that can limit what they can do and how they do things.
Cons:
Limited Brand Awareness – We’ve come a long way since our west coast team was formed nearly 2 years ago (21 months to be exact). When we lose to banks like JPM, it’s largely due to their brand and the feeling that they’re too big to fail.
UI/UX – We’re making huge strides here as well and are actually launching a new banking platform in 2025, but our UI/UX (nor will any other banks be) what folks experience with fintech players like Mercury.
Other Pros and Cons:
Pros:
Of the really big banks, HSBC, MUFG, and JPM have hired a lot of tech-focused bankers to try and capture the opportunity in tech.
HSBC, MUFG, and JPM are also viewed as too big to fail.
Mercury and Brex are software companies. They tend to have the best UI and have digital account opening that tends to be the quickest. They offer a good self-service offering.
Cons:
Big banks tend to be more rigid and less nimble given their size and regulatory burden.
Mercury and Brex aren’t banks and can often mask the bank companies are actually working with. Underlying banks and their respective safety profile often aren’t evaluated, when they should be. As an example, one of the leading banking as a service providers and has recently been in the news with meaningful regulatory issues that have emerged. Mercury and Brex are also both private, venture backed companies, so if you’re looking for transparent stability as a key factor in your bank, working directly with a regulated bank that’s public would be better at achieving that.
Fintechs are focused on UI and self-service. Those tend to be low value needs they can make good. They don’t have the relationship aspect of banking that drives real strategic value.
What about the newer entrants like Brex and Ramp if founders use them for other things?
We actually partner with Ramp. They have a great credit card and expense management platform. Ramp and Brex are both really good at the credit card/expense management side of things. I think they should be leveraged for that. For banking, for reasons outlined above, I think choosing a stable bank to directly work with makes more sense, in particular one that understands your space and needs and can deliver value in those areas.
Addressing the elephant in the room, how can founders make sure they are FDIC-insured on all their cash? Or should they not worry about that?
Founders should absolutely understand where their cash is and whether it’s liquid and safe. FDIC insurance is one way to achieve safety of your cash. Insured Cash Sweep is a way to allow you to get FDIC insurance on potentially all of your cash. You can also have cash invested in treasuries as another option for safety.
How can founders make sure they are getting decent interest on their cash?
Taking a look at a few different options to compare is the way to ensure you’re getting a market rate. Also, look at what government treasuries are offering both short and near term as a good proxy. It’s worth nothing that Bank interest rates tend to move in line with interest rate changes made by the Federal Reserve. If the Federal Reserve decreases rates, you should expect the return on your cash to decrease as well.
Should founders have business checking, savings, a sweep account, etc.?
The most common way to do this is to have a checking account and sweep account. The sweep account will have some minimum peg balance and will sweep everything above that level. Just remember that what’s not swept is then typically subject to the $250,000 FDIC limit, so if the peg balance is $500,000, then $250,000 of that cash in there will not be FDIC insured. That's a small exposure, so maybe it’s okay. For instance, our peg balance is set at $250,000 and then the rest is swept into ICS.
Should founders buy treasuries/bonds/CDs with any of their cash?
I’d generally say no for cash-burning tech companies. Keeping cash liquid tends to be important and there’s not a lot of incremental value for locking up cash and having to manage it vs. having an automated solution like insured cash sweep. That recommendation might change if you have multiple years of runway and meaningful cash ($100M+) and if the yield curve shifts and there’s a more meaningful return for locking up some portion of cash.
How much debt should I take on and when?
This varies on the business type and stage. For early and early growth companies, you typically are doing a third of the last equity round and you’re doing it within six months of raising the round.
What are good standard terms?
This varies on the type of facility you are getting. For venture debt, good standard terms tend to be a third of the last round in size, four years overall term, no financial covenants or maybe a topline covenant, rate of Prime +0.50%; warrants in the 20bps fully diluted ownership range.
What are the gotchas?
Know your lender and what kind of partner they are. If they don’t have a ton of experience, it might be risky to work with them as you don’t know how they will behave when it matters most. Otherwise, I don’t think about it as gotchas. I think you just need to make sure the structure works well for the business.
If there are covenants, do they provide founders sufficient flexibility and are they the right ones to manage the business with. How should founders pay it down?
Use it when you need it. Pay it down per the terms of the debt deal unless you’ve raised a new round, in which case you should consider refinancing what you had with your lender.
When should founders consider working capital facilities vs. standard venture debt?
This varies depending on the type of business and business model. Generally speaking, when revenues start getting to the $10 million range, you have enough working capital that can support working capital debt. Before then, you typically will have more availability through venture debt. Venture debt doesn’t scale well like working capital debt.
What about equipment financing/leasing? When should founders consider this?
Typically when you have hard assets to finance AND it’s in excess of what a venture bank might be able to provide. Bank loans tend to be more attractive in how they are structured (cost and repayment).
I’m glad to connect with folks directly if they have questions or are looking for a better banking partner. You can reach me through email: mle@stifelbank.com.
Nate Bek is an associate at Ascend, where he screens new deal opportunities, conducts due diligence, and publishes research. Prior to that he was a startups and venture capital reporter at GeekWire.
Disclaimer: The information provided here is for educational and informational purposes only. It does not constitute financial advice, and you should always consult with a qualified financial professional before making any investment decisions. Past performance is not indicative of future results.
Token Talk: Open source won the AI race
By: Thomas Stahura
If it wasn’t clear already, open source won the AI race.
To recap: Deepseek R1 is an open-source reasoning model that was quietly launched during the 14 hours TikTok was banned. The reasoning version of Deepseek V3, Deepseek R1 performs at o1 levels on most benchmarks. Very impressive and was reportedly trained for just $6 million, though many are skeptical on those numbers.
By Monday, a week after R1 launched, the model caused a massive market selloff. Nvidia lost $500 billion in value (-17%), the biggest one-day selloff in US history, as the market adjusts to our new open-source reality.
So, what does this mean?
For starters, models have been commoditized. Well-performing open-source models at every scale are available. But that’s besides the point. Deepseek is trained on synthetic data generated by ChatGPT. Essentially extracting the weights of a closed model and open sourcing them. This eliminates the moats of OpenAI, Anthropic, and the other closed source AI labs.
What perplexes me is why Nvidia got hit the hardest. The takes I’ve heard seem to suggest it’s the lower costs it took to train Deepseek that spooked the market. The thinking goes: LLMs become cheaper to train, so hyperscalers need fewer GPUs.
The bulls, on the other hand, cite Jevons’ paradox. Wherein, the cheaper a valuable commodity becomes, the more it gets used.
I seem to be somewhere in the middle. Lower costs are great for developers! But I have yet to see a useful token-heavy application. Well maybe web agents… I’ll cover those in another edition!
I suspect the simple fact the model came out of China is what caused it to blow up. After all, there seems to be such moral panic over the implications on US AI sovereignty. And for good reasons.
Over the weekend, I attended a hackathon hosted by Menlo where I built a browser agent. I had different LLMs take the pew research center political topology quiz.
Anthropic’s claude-sonnet-3.5, gpt-4o, o1, and llama got outsider left. Deepseek R1 and V3 got establishment liberals. Notably, R1 answered, “It would be acceptable if another country became as militarily powerful as the U.S.”
During my testing, I found that Deepseek’s models would refuse to answer questions about Taiwan or Tiananmen square. In all fairness, most American models won’t answer questions about Palestine. Still, as these models are open and widely used and used by developers, there is fear that these biases will leak into AI products and services.
I’d like to think that this problem is solvable with fine-tuning. I suppose developers are playing with Deepseek’s weights as we speak! We’ll just have to find out in the next few weeks…
Investing in Oumi
By: Nate Bek & Kirby Winfield
Today, we’re excited to announce our investment in Oumi, an open-source AI platform. Venrock led the $10 million Seed round, joined by Plug and Play and Obvious Ventures.
AI is at a critical juncture. Industry leaders are investing heavily in proprietary systems, with OpenAI’s $500 billion Stargate initiative serving as a clear example of the growing shift toward closed ecosystems. These walled gardens centralize innovation and resources, restricting access for much of the AI community.
Even within so-called open-source initiatives, transparency remains incomplete. Models like DeepSeek and Llama provide open weights, but fail to include critical elements such as full training pipelines, preprocessing code, and detailed data provenance. This lack of openness limits developers’ ability to reproduce results, refine models, or push the boundaries of what is possible. Without full access, innovation remains stifled, and the barriers to entry for AI development stay high.
A step-function change is needed to redefine what open-source AI can and should be. Oumi is addressing this challenge by building a fully open-source platform for large foundation models that spans the entire AI development lifecycle: data preprocessing, pretraining, fine-tuning, evaluation, and deployment. By providing access to every component, Oumi empowers researchers and developers to collaborate and innovate without restrictions.
Oumi partnered with more than 13 universities, engaged 36 AI researchers, and built a team of 10 high-profile builders to bring its vision to life.
Our conviction in Oumi was solidified through conversations with CEO Manos Koukoumidis. He has a track record of leadership at hyperscalers like Google, Meta, and Microsoft, paired with academic rigor as an MIT and Princeton PhD. Open collaboration on this scale requires not just technical excellence but strong leadership. Oussama Elachqar and the other seven co-founders were also stellar. It’s rare to find such an exceptional founding team on day one.
“To protect their interests, closed vendors openly brag about how many GPUs they have in their attempt to secure their compute ‘moat,’” Manos says. “But I believe an open community can do at least as well as them. I actually think it can do a lot better.”
This vision aligns with and helps inform our AI Fast Follower thesis. The economics of AI development have shifted significantly. GPU arbitrage, open frameworks, and pre-trained models have slashed barriers for new entrants. Fast followers can achieve frontier-level model performance without relying on the deep pockets of the early movers.
That said, open source isn’t without its challenges. Monetization can be tricky, and aligning a decentralized community takes effort. But the potential payoff is enormous: a truly global effort driving better, safer, and more reliable models across every modality and use case.
Oumi’s platform is already addressing critical needs in industries where trust, reliability, and scalability are non-negotiable. This progress makes a strong case that open-source collaboration can deliver better, faster, and more inclusive innovation than walled gardens.
We’re excited to share the cap table with notable angel investors including Ruslan Salakhutdinov (Carnegie Mellon professor), Ragavan Srinivasan (Meta Product VP), Vipul Ved Prakash (Together.ai CEO), and Clem Delangue (Hugging Face CEO).
Open-source AI has the potential to lift all boats, driving global progress and opportunity. Oumi embodies this belief, and we are proud to support their journey.
Meet Nazuk Thakkar, the Ripple Investor Who Lifts up Young Founders
By: Nate Bek
For young founders, Nazuk (Naz) Thakkar might be your strongest ally — literally.
In college, she took over a powerlifting club and made it more open to women, sparking her passion for lifting and a drive to make traditionally exclusive spaces more inclusive. That experience shaped her mission: make hard-to-crack communities more accessible. It also gave her the grit to advocate for founders taking their first swing.
“It’s founder empathy,” she tells Ascend. “Running the powerlifting club and also being an active member in this traditionally male-dominated sport taught me how to support people building from the ground up.”
Naz also sees massive potential in young founders. “These young founders are fearless and committed,” she says. “They’re willing to take a bet on themselves, and that energy is what’s going to take their companies far.”
As an investor at Ripple Ventures, a Canadian venture firm, Naz focuses on high-conviction pre-seed investments, writing checks between $250,000 and $1 million for core deals and $50,000 - $100,000 through Ripple’s Fellow Fund.
Ripple is currently investing out of its Fund III, anchored by the Royal Bank of Canada and the Government of Canada's renewed Venture Capital Catalyst Initiative ("VCCI") through BDC Capital. The firm focuses on companies tackling outdated industries like healthcare, logistics, and construction.
Naz was kind enough to sit down with Ascend for our VC profile series, where we showcase early-stage investors from across the US. We talked in more depth about her global roots, her views on young builders, and why sauce is the best ingredient. Read to the end for carve-outs.
*We've edited this conversation for brevity. Enjoy! — Nate 👾
Nate: Thanks so much for doing this, Naz. How did you get started on your journey of becoming a professional investor?
Naz: I grew up in Ottawa, Canada’s capital, but it always felt like a small town. I literally grew up in farmland, surrounded by cows and horses. Back then, I didn’t know much about startups. My dad worked in cybersecurity, and when I was in fourth or fifth grade, he started his own company. Watching him scale the business and take that chance on himself was exciting and left a lasting impression on me.
Later, my family moved to Singapore, where I did high school. Living there broadened my perspective—I traveled to nearly 40 countries, which shaped my worldview. After high school, I returned to Canada to study business at Queen’s University. The standard career paths—investment banking, accounting, consulting—didn’t appeal to me, which was unsettling because everyone else seemed so sure of their career trajectory.
Some friends in private equity suggested looking into venture capital. It clicked. My dad’s tech background and my exposure to Singapore’s fast-paced innovation ecosystem made VC feel like a natural fit. At 18, I cold-emailed around 100 VC funds during my first semester. One fund, Trend Forward Capital in New York, took a chance on me. I spent 4–5 months there as an analyst, and it blew my mind. Venture capital was the most exciting and dynamic space I’d ever seen. Where I could be myself and thrive.
You get back from New York and your internship, what do you do from there?
Back at Queen’s, I helped launch the Ontario chapter of Front Row Ventures (Canada’s version of Dorm Room Fund) and created Queen’s first VC course to make this career path more accessible for students.
To gain operating experience, I became the first hire at a DTC brand in New York called Ruby. I worked on scaling their retail and customer experience operations, handling biz dev and sales. But I missed software and investing. That’s when I came across Ripple Ventures through their fellowship program. I loved their grassroots, operator-focused approach to investing. I pitched myself hard, and they brought me on. Four years later, I’m still here.
You mentioned your operator experience. I worked in retail too — at a fish spot and an ice cream shop. While that doesn’t exactly translate to software, I think there are some shared lessons, like dealing with black swan events and the unpredictability of running a business. As an operator-focused VC, what do you offer founders? Why do they want you on their cap table, both personally and as a fund?
Travel shaped how I work with people. Seeing different cultures and perspectives taught me how humans tick. At university, I accidentally minored in religion because I took so many courses to understand worldviews. That foundation helps build founder empathy and trust, which is critical at the pre-seed stage.
Founders face big risks and uncertainty, and I believe in being a good, kind human first. That’s why they want Ripple on their cap table. We show up—whether it’s answering late-night emails, reviewing decks at midnight, or just listening when things get tough.
It’s about being like a third co-founder, filling gaps and offering support. My own experiences—traveling, being an early operator, and facing challenges—drive that approach. I know how much it matters to have someone in your corner early on.
What are you personally excited about these days? App layer/infra etc.
I spend most of my time around application layer, digging into verticalized applications for cybersecurity and compliance.
I care most about very clear, immediate and costly problems that are ripe for disruption and have budgets to access with unique distribution advantages. I’ve been seeing a lot of these trends in use cases within gov, pharma, healthcare, climate and fintech especially for example where we've done a few deals this year.
You’ve done a few deals in Seattle, like Zealot, the spin-out from the University of Washington, and Climba, the agent company. Being based in Toronto, how do you position yourself in that market? And how do you approach competing in larger U.S. markets and winning allocation there?
Starting with Toronto, it’s about building rapport with founders. There’s a risk aversion here that’s more obvious than in SF or New York. But Toronto is getting to a place where founders are more comfortable. There’s a wealth of resources and a strong sense of community, similar to what I’ve seen in Seattle.
You see people co-investing and collaborating on deals. I’m passionate about building that ecosystem in Toronto. I host monthly VC events and spend time in the community building out our co-investor network. The grassroots approach matters. You need founders to trust you, even if they’re not raising today. You want to be their first call when they are.
Toronto founders often take the time to work closely with investors. But the brain drain is real. You have to win on value-add. That’s why Ripple focuses on pre-seed gaps — both capital and domain expertise. We invest in thought leadership, community work, and founder support. That’s where you win in Toronto.
It’s similar to Seattle. We travel to build our co-investor network and think about strategies to help portfolio companies scale. It’s about setting them up to win — not just in Canada, but globally. That means working with VCs who can back future rounds, building corp dev networks, and thinking strategically about what’s next for the companies to scale.
When I was in Seattle, I saw founders building in a space with gaps similar to Toronto’s pre-seed scene. Ascend and PSL are bridging those gaps, keeping founders from draining to SF. It’s all about post-investment work: graduating companies, co-investor networks, corp dev connections, and helping companies succeed beyond just your capital.
Let’s talk about Seattle. We’re a Seattle VC, and, yes, we pander to our audience — people click for that. You’ve been here, hosted an event, and made a few investments. Coming from across North America, what excites you about the opportunities here? AI talent stands out — that’s obvious. The legacy of juggernauts like Microsoft and Amazon is huge. But beyond that, let’s talk about the personality of the founders.
Our most recent investment in Seattle came from students and dropouts from UW. Making that investment, we realized there are incredibly smart kids here who are just building.
They have this “screw it, I’m just gonna build something” culture. It’s not as common in other universities and we need more of that self-commitment.
In Seattle, it’s different. These young founders are ruthless and fearless. They’re willing to take a bet on themselves, and that energy is what’s going to take their companies far. We saw that firsthand with our investment.
Interesting, I don’t hear that often. I’m friends with some of the most cracked young engineers here, and you’re right: they’re killers. Some that come to mind are Caleb, Jamari, and Parsa at our portfolio company Moondream. You alluded to it, but what’s the bear case?
I think the ecosystem in Seattle, to talk about the bear case, needs to cater more to the young builders. There’s a very strong focus on investing in seasoned founders. We talked a bit about the access gap to capital for early-stage companies, but it absolutely exists for young builders too.
When I was trying to understand what organizations exist within UW and other universities in the PNW, there’s not a lot. But there are these kids who are so inspired. They’re close to SF, so they’re drinking from the firehose, surrounded by all the smart people in Seattle, and they’re willing to build companies.
We’ve spoken to so many who aren’t necessarily looking for capital but for support and value-add investors. The good thing is there’s amazing talent, hustle, and dedication to building here. That’s rare to find outside the traditional tech hubs.
People need to keep an eye on these young builders and pay attention to who’s up and coming, rather than always focusing on three-time exit founders or ex-Amazon execs. The folks coming out of school and building companies are some of the most committed I’ve seen. Turnover and commitment are always challenges for young builders, but what’s coming out of the Seattle schools is very impressive.
I don’t know how much you’ve been following what Kirby has been posting online or some of the things I’ve been involved with, but yeah, there’s a lot happening. There’s DubHacks, CSeed, and YoungTech Seattle. Switching gears, what songs are getting the most run on your headphones?
My partner had never seen Twilight, so I was like, "Okay, it’s officially that time of the year — we have to watch all the movies." And we did. We binged every single one in a weekend. Since then, the soundtracks have been on repeat. I’m obsessed with "Eyes on Fire" by Blue Foundation —it’s such a classic. I also love all the EDM remixes of it. But in the gym, I’m in my punk rock era. And I’m a dubstep girl through and through — dubstep is my go-to for all my lifts.
What’s your go-to shoe?
One, lifting shoes—absolutely. My heeled lifting shoes have changed my life. Seriously, I can’t imagine squatting without them. They give you depth, comfort, mobility and the range of motion you need. They’re 100% my ride-or-dies. Day to day, though, I love Nike Blazers. The color schemes are awesome, they look good with everything, and they’re just such a timeless casual shoe.
Go-to ingredient in the kitchen?
Oh, okay, I love this question. I’m a big believer that food is just a vessel for sauce. Like, literally, the whole side of my fridge is packed with different sauces—especially hot sauce. I really believe in the power of spice. I’ll add spice to anything. As for ingredients, the trifecta is garlic, onion, and chili.
Honestly, you can’t go wrong with those.