AI, Investment, Legal Challenges and Music Tech with Valeska Pederson Hinz
- Eric Doades
- May 8
- 28 min read
Updated: May 12
Today we are talking with Valeska Pederson Hinz, partner at Perkins Coie, about what’s happening in the world of music tech investment. Valeska has extensive experience in guiding companies and investors from Series A to IPO and has an indispensable vantage point. Our conversation includes the current state of venture capital funding, the impact of generative AI on the industry and the ongoing legal debates surrounding fair use versus licensing in AI training data. She also has practical advice for startup founders from the legal standpoint of someone who guides growth stage companies.
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Episode Transcript
Machine transcribed
Dmitri: [00:00:00] As a partner at Perkins Coie, Valeska Pederson Hintz helps close the deals and navigate the challenges that are part of the journey from Series A to IPO for companies and investors. Having worked on multiple unicorn deals and over a dozen IPOs, Valeska brings unparalleled expertise to her clients. She acts as the first point of contact for growth stage companies helping management teams establish corporate and governance structures that enable their long-term success For investors, Valeska helps shepherd their investment process and provide strategic advice on acquisitions, IPOs, tender offers, and other liquidity events.
Veka, thanks so much for coming on to the Music Tectonics podcast.
Valeska: Thanks so much for having me. I know this is boring, but just a quick note before we dive in. I am a lawyer. But nothing I say here should be taken as legal advice, just information, hopefully useful, maybe even interesting. But if you're facing an actual legal decision or anything involving real money, you should really talk to an [00:01:00] attorney who's licensed in your jurisdiction and understand your specific situation.
Also, I'm expressing my own opinion here and not those of my firm. And with that outta the way, a huge thanks to
Dmitri: You would say all of that,
Valeska: I know.
Dmitri: and you have to, and it's good that you do because people should know what's going on here. So thank you. I'm excited to talk to you. I saw you do a presentation in Austin where you're based, during South by Southwest, and I learned so much about the investment landscape and even you guys dove deep into what's happening with music tech.
I really appreciate, you doing that presentation. And I hope to share some of that on this episode. How is the VC landscape looking overall in this first half of 2025?
Valeska: not as great as I had hoped.
Dmitri: Oh.
Valeska: According to TechCrunch, startups have raised about 91.5 billion in venture funding in Q1 of this year. So that's an 18.5 percent increase over Q4 2024, and the second highest quarterly total in the last [00:02:00] decade. On the surface, that sounds like a rebound, so it sounds like better than where we were when we last talked.
like maybe the vibes are back, right? nearly half of that money. 44%. Came from a single deal, open AI's, $40 billion raise. That's the company behind Chat, GBT, and a lot of investor fomo. So not exactly a sign of a broad-based enthusiasm. And according to PitchBook, another 27% came from just nine other companies, including Anthropic, another AI research company building large language models like Claude, which raised 4.5 billion.
So if you strip out those mega deals. The rest of the market, literally every other startup shared about 26.5 billion. That's not nothing, but it's a long way from a market wide recovery. I was hoping for.
Dmitri: Whoa. That's really interesting. See, already I'm like getting insights about stuff that I wouldn't catch in the news myself. I mean, I knew a lot of these, big [00:03:00] AI gen AI companies were attracting a lot of money. But to think of it that way, that there's like a handful of whales that are really,soaking up all the oxygen in the
Room from from investment does paint a different picture.
So historically, let's focus in on the music tectonic side of things. How have investors done with music tech investment? 'cause I think you have some context there that's really useful for our wider audience.
Valeska: Yeah, and what we'd been talking about before. Historically music tech has been a tough category for investors in the data back setup, right? According to, tracks as earlier, as of earlier this year, there were over 10,000 music tech startups globally. I. Of those, about 1800 were funded, raising a total of 15.5 billion.
But when you look at the exit data, it's only 2,239 acquisitions and just. 20 IPOs. So you start to see the challenge there as of last month. You can add one more to that list. Infinite reality acquired Napster for 207 [00:04:00] million, so that's great. but what really jumps out when you drill into the numbers here is it the difficulty of music techs, startups face and reaching scale only.
And remember we had that chart that showed like here he's funded companies, based on their round. And when you looked at it. Only about 20% of the funding companies actually make it to their series A and just 4% reach a series C and only 2% reach. the later stage rounds like a d or beyond, that's fewer than 50 companies where, and that's where your billion dollar unicorn valuations typically start showing up.
Now those stats really matter because venture capital is built on what's often called venture math. Investors aren't just looking for good businesses. They're looking for the one outta 10 that can return the entire fund. So for VCs, that means aiming for 10 x returns. Growth equity investors may target five x returns, and private equity firms often look for about three x.
But they have lower risk and [00:05:00] more predict predictability and the growth and private equity. That's because they're dealing with later stage companies. So to even get in the door for a Series A, your business has to show the potential to reach that kind of multiple. And historically, compared to enterprise SaaS or FinTech, music tech just hasn't produced many unicorns.
That makes it harder to raise institutional capital in this space unless you can clearly demonstrate scale potential. And a credible path to liquidity. Those later stage rounds are typically where you're getting the billion dollar valuations or unicorn status, like Pandora going public at 3.5 billion.
Venture investors need to believe that type of return is possible before they wanna invest in your company. it's not that investors haven't made money in music tech, they have, but the bar is higher from what I see, and the exit paths are narrower. So the expectations are steeper. So when you have a music tech company breakthrough, unfortunately it's exception and not the
Dmitri: Wow. It is, sobering information when you present the data that clearly, and [00:06:00] probably there's some music tech founders out there listening to the podcast who just felt pain in their side or in their stomach or some something from just hearing the data just put out so clearly. I guess I could ask this question.
I don't know where we'll go. I'll be curious to see what you say. What are the pros and cons of music tech as an investment category?
Valeska: From my perspective, music tech, like any tech category, is most compelling when it's undergoing a disruption cycle, right? That was the case in the early days of streaming, starting with Napster evolving into your platforms like Spotify and Pandora. While streaming services may still be innovating, it's now more of a mature market segment.
So today the disruption is in generative audio, voice modeling, AI powered production tools. I keep hitting ai. This whole thing is gonna be ai. one clear pro though, I think just for the sector in general is that music tech has cultural gravity. And in a way that other sectors don't, outside of, like media tech, like film tech.
people form emotional connections to artists and [00:07:00] music brands in a way that just. It doesn't happen with enterprise software, no one's forming a fan club for invoice automation. but millions of people, including my 6-year-old follow Taylor Swift and lots of people like the Rolling Stones, it's fun, it's public facing, and when it's scales it's ga fast and globally.
one of the drawbacks is that, like we're talking about the exit funnels narrow, as we discussed, there's a lot of early stage bets. Not as many acquirers and only a few of the startups reach the kind of skill that attracts real m and a attention. And there can be some regulatory risks, which are often overlooked.
so I guess when you have an attorney on, they're gonna, they're gonna bring this up. but one example is, some music tech startups have been trying to innovate around artist financings by offering investors a cut of future revenue. Think streaming, touring, merch, whatever. the problem is that these models can raise serious red flags down the road.
If the arrangement starts [00:08:00] looking like credits extended to a musician, it may trigger lending laws and nobody would normally think of that. it could also look like pooling investor money into a common enterprise. And if you think back on all the Web3. Blockchain securities, Howie problems, you're getting into that space 'cause you're tying all of this pulled money into a common enterprise that's tied to future revenue.
And that startup starts becoming to look like an un unregistered investment company under the Investment Company Act of 1940. That's something nobody's really thinking about. So those are issues that often don't surface until it's too late, and when the company is scaling and they're trying to borrow from a bank or prepping for an acquisition or an IPO at that point.
The damage can be really hard to unwind, so banks won't lend buyers walk even your contract rights can be void if you're found to be an illegal investment company. So the takeaway though, is that these startups in these situations, they need to pause when they're initially structuring the [00:09:00] investment arrangement and engage regulatory counsel, which isn't me and I, there's lots of 'em on my team.
But if you wait until you've grown and scaled the business it, it's maybe too late to fix the issue.
Dmitri: You do sound like a lawyer. I mean a nice lawyer, but you, you're giving us he helpful tips in advance, but yeah, there's a lot of categories you just came up with there that do complicate,just thinking through some of the legal, speed bumps or worse along the way in the future that if you don't think about 'em now they can get a lot worse.
Okay. I'm gonna keep going with these questions, what investment categories within music tech have done better than others?
Valeska: so based on data from PitchBook, the music tech categories that have attracted the most capital since 2020, are those that interest, that have, that intersect with a broader proven business model. So the top three are movies, music, entertainment tech. It's probably not a surprise to you. and that's with over.
350 million in invested capital. then you go to entertainment software, that's close to 300 million. Then the third is [00:10:00] business and productivity software. A lot of software, right? And that's with over 250 million. A distant fourth is gonna be B2B. That's business to business. Media and information services, but that drew only about 75 million.
Beyond that, the numbers drop off like really sharply. So from an investor lens, the categories that do best tend to either support content creation and distribution models at scale, or they offer tools with enterprise or cross sector utility. what's also notable is how the themes have shifted in 2021 to 2022.
A lot of attention went into. Metaverse concerts, blockchain powered royalty platforms, avatar based artist models. but in 2024 it's more boring stuff. we've seen a pivot towards more traditional. And arguably more scalable businesses. So for example, Heller and, Hellman and Friedman, which is also called h and f, easier to say, had a $3 billion investment in global music rights.
And [00:11:00] Universal Music Group did a $775 million acquisition of downtown's distribution infrastructure that reflects significant interest in rights, infrastructure and distribution. So not the like flashy. Metaverse concerts, but a little more stable. And then at the same time, I think the biggest story kind of across tech generally, like I've been saying, is ai.
So AI's clearly driving the next wave of excitement. Two of the most talked about deals this year were lights speeds, series B investment, and suno. And Connect Ventures, $40 million Series A in music.ai. And music.ai is a foreign company. Kind of interesting. Both are AI native platforms focused on music creation and production tools.
One is more consumer facing and the other has a strong B2B offering, and both were backed by major global firms, both with billions of dollars under management. So Lightspeed. [00:12:00] Has 28.2 billion under management and NEA, which is a partner in Connect Ventures, has $25 billion under management. So these are huge venture firms that are big players in tech, really well known globally.
And what these deals have in common is a bet on scalable technology, not just cool features. It's not just a product, it's an actual scalable business. So the platforms offer real utility for creators. They're paired with commercial models that can expand across industries and geographies. And in the case of music ai, the emphasis on ethical training data also addresses kind of one of those key risk factors and.
Ai, which is IP compliance. So while the exit funnel and music tech has historically been narrow, the categories gaining traction now are those that solve real problems with business models that grow and adapt, particularly where AI and music creation intersect.
Dmitri: Again, that data really, I think gets us quickly educated on the [00:13:00] different categories,some of the considerations, both in terms of historical, interest as well as current interest as well. So thank you so much for that. I was super helpful. Maybe like moving, I don't know if it's more anecdotally, but what are you hearing from the investment field about investing in music tech right now?
I know that's you're not necessarily a specialist in that particular vertical, but I'm curious,with your knowledge, with your lens, now that we've looked at some of the data, what are you hearing?
Valeska: Yeah, right now most of the buzz I'm hearing around AI powered music tech,last week. I served as a judge at the Rice Business Pitch Competition. to give you a bit of a background on what that is, it's the largest, best funded collegiate startup competition in the country. So think of your PhD students at Stanford, Harvard, Berkeley.
All across the world and your college students. And they got 550 domestic and international applications that they whittled down to 42 teams. And those 42 teams competed and two of them were in music tech. [00:14:00] And that's something I haven't seen before and I think it's a sign of where things were headed.
one of the startups was this company called Song Description out of Stanford. They use AI to. Transcribe audio and music sheets and automatically arrange songs for different instruments and musicians. They do some other things. another one was watermark.ai from UI uc. They embed imperceptible digital watermarks and songs, podcasts, and other audio files to protect them from deep fakes and unauthorized use.
So cool things. And these are really, they're. College students and PhD students. So these are early stage companies, but often the most exciting innovations come out of universities. And in my opinion, it's really encouraging to see music techs holding their own in competitions, usually dominated by energy, healthcare and sas.
on the investor side. Yamaha recently launched a $50 million music tech investment fund. It's the first through the [00:15:00] Yamaha Music Innovation in Initiative. the funds focused on backing innovation across the entire music value chain from production and recordings to streaming and fan engagement, that kind of corporate CBC or corporate venture capital commitment.
Signals a growing interest in the investment sector. I think, I mean, we see a lot of CBCs in tech, but it also has broader strategic view of like where music tech is gonna fit in the future of content creation and consumption. So I see those two things to me stand out as like really interesting.
Dmitri: Those are great stories. Thank you for sharing all that. as I listened to this first, half of this conversation, Valeska, I'm thinking a little bit about some conversations we've heard from a lot of people in the investment side of things. One that. A lot of investors don't think of music tech as a category and their advice is think more transmedia, think across larger verticals, bigger markets than just music [00:16:00] tech.
So some of the things, some of the statistics that you shared with us, I think kind of point to that. when you talk about entertainment. As one of the biggest categories,and film related things that are also music related as one of the biggest categories of investment in the past.
that points to how a music tech company could widen out the possibilities, not only by looking for investors who are like changing your mission because there's investors in it, but also widening out what the potential customer and the potential market is for that company as well.
So that's. That's an interesting insight that I think resonates with other, things we've heard on the podcast. But I'm excited to hear you, talking about some new categories that are emerging as a result of some shifts. Certainly the AI stuff around, music creation, regardless of whether it's gen AI or non, whether it's ethical or not, but just the fact that, there's this infusion of.
Interest a around, music creation with these tools. but also, like some of the startups you mentioned too seem to be in response to some of the challenges that were in the past. Things like watermarking, music in itself is an [00:17:00] interesting, one too to protect against deep fakes.
Like you can have AI as the solution in music. You can also have AI as the problem. And then another form of AI that solves the problem of AI in music as well as such
Valeska: it, it's interesting the AI and DeepFakes, because I was playing around with the Suno app and, we have this cat that we call the asshole cat. I love this cat, but he opens doors and he's crazy. And so I, I asked. Suno to make me a song about this cat. And I put his features in there and I wanted it in jazz.
And I swear it sounds like Michael Bule, and I love that Michael Bule made a song about my asshole cat, but I'm sure he doesn't.
No, I know. And did he, maybe he did. We don't know, Maybe he did, maybe he licensed it to them. I don't really know. But, is fascinating what the technology can do. it, that's just fun for me. But, imagine you're a cartoonist. And you're like a college student or somebody getting into the field and you're like, I don't write music.
I, I don't know how to do it. I'd like to just use one of these things for my [00:18:00] theme song, and you can do that. And that to me is kind of amazing. So it just opens the world of creation and innovation up for more people, and it makes it more accessible. And I think there's gonna be more and not less content created because of this stuff.
Dmitri: This whole conversation about fair use and licensing training data is one that I want to dive into. We have to take a quick break and when we come back, I have some more for you. We'll be right back.
Okay, we're back. And Veka, I do wanna ask you about gen AI and this fair use versus licensing training, data debate, and the impact on investment. But before we get there, I wanna ask you a little bit, something more broad for our startup founders who are thinking about investment, what KPIs are most important for startups to demonstrate to investors?
Valeska: So that's a great question. And I, there's so much debate between like CFOs and other people at the company as to what's an important KPI? And the reason that is, is because [00:19:00] KPIs are really specific to each company. They depend on your business model, the dynamics of your market. a B2B SaaS startup is gonna track different metrics than a consumer-facing marketplace.
And music tech has both. that said, across the board, investors are prioritizing revenue. Durability is something I've heard a lot about, and the past few years, the focus has shifted in tech generally. From top line growth at all costs to long-term resilience. And the KPIs that matter most are the ones that show your business can scale sustainably and withstand market volatility.
So some important KPIs I'd like to highlight are, number one, turn and retention. So that is, are your customers sticking around? Churn is the rate at which customers stop using your product. So that's an important one. It signals. Fragility, strong retention points to lasting value and product market fit.
I'm [00:20:00] sure you've heard a lot about product market fit from startups. number two is what people call LTV and cac. So LTV, that's lifetime value. So total expected spend per customer and CAC is customer acquisition cost. And this is really getting at is your growth cost effective? And sometimes what you'll see is people will look at the ratio of lifetime value or LTV by customer acquisition, cap cost, or C, and what that tells investors is whether you're building a scalable business or just burning their money to grow.
if it, if you earn a dollar, but it takes you $2 to get that customer not so hot. The next third or the third thing is revenue mix. So that is how balanced and reliable is your income or revenue. So investors are gonna look at how much your revenue is recurring. And that's things like subscriptions versus transactional, whether it comes from a single segment or more multiple segments.
So think of [00:21:00] customer types, geographies, product lines, and how much of it is tied to consistent behavior. I. reoccurring things versus one-time events. So at the end of the day, investors are backing companies with predictable, repeatable, and resilient revenue, not just those that are fast growing right now.
And to build that kind of company, you need to be able to track the right KPIs from the start. So I love your question because KPIs aren't just for fundraising. They're really a part of how you build a staying power in your business.
Dmitri: I know a lot of our listeners were taking notes and are rewinding this and catching notes on this as well. That was great. I lesky really good at crystallizing some of these answers into like very condensed but useful information that I'm sure people are gonna be studying for weeks and months to come.
So thanks for
you and I talked about this when we were in Austin, but also just through this conversations, it's clear that you're aware of what's going on in the Gen a, gen ai, and the music field closely.
specifically, [00:22:00] there's a debate about fair use versus licensing training data, which fair use sounds so fair. but, um, I, I mean you mentioned some of the legal challenges of having some type of company that is skirting some laws around ip. How does this debate impact investment in AI and music?
Is this a, oh, if you follow the fair use doctrine or per perspective, then that means your expenses are much lower because you don't have to license things and so you can grow like crazy or. Are you potentially facing the type of lawsuits that could put you out of business with multi-billion dollar,fees and,just losing all that money.
And in legal cases, because you haven't followed the kind of the regular practices around using other people's ip,how is investment influenced by AI in music?
Valeska: Yeah. so just to start off with, here's my second disclaimer. I'm not an IP attorney. so this topic isn't my specialty. That said, the area is unsettled. [00:23:00] It's really complex and it's quickly evolving. So I'd just like to back up for a second and give a little bit of a picture of the landscape. So generative AI models.
I'm sure most people know, but they need massive data sets. So think text, audio, images, and they use that to learn patterns. But if those data sets include copyrighted material, like songs or lyrics, the question you're really asking is, do AI developers need permission to use them? And can they rely on fair use?
Like you were saying, there's a separate question of, these companies that have websites also have terms of use, and those are contracts that govern how you can use things. And if you're using in violation of that. You are in violation of contract law and that's a breach, but we're gonna put that aside 'cause we could talk forever about this stuff.
but the question is now at the heart of multiple lawsuits with respect to ai. and is it fair use? The most recent case that I feel like has gotten all, or there's two of them really. [00:24:00] There's the Thomas Reuters versus Ross Intelligence, and there's Concord Music Group versus Anthropic. And both of those highlight a growing legal conflict between, what we were saying is the fair use doctrine and the emerging practice of licensing data for AI training, the outcome of these cases is still unfolding.
these are still ongoing, the. So it's creating a lot of uncertainty for startups, clearly, especially in music tech. and it's influencing how investors evaluate those companies and how they value them and how they diligence 'em. So these are like very important topics. we'll start with the Reuters case. So the Reuters case was the first major US Court decision on AI training and fair use Ross built. Basically, an AI legal research tool and trained it on summaries of case law derived from Westlaw's database without a license. And for attorneys, we are really familiar with Westlaw. [00:25:00] They use these like key sites and, we learn how to use 'em in law school.
So this kind of rings a bell for us. in February, 2025, the court rejected Ross's fair use defense and ruled that using Westlaw's copyright headnotes to train its AI was infringement. So for context, the doctrine of fair use provides that use of another's work is not infringing. When it's used for certain purposes and there's not really a BlackLine rule for what that is, one of those facts and circumstances tests that lawyers love to talk about.
But when you're determining whether a particular use is fair, the courts are gonna consider for non-exclusive factors. So one is the use is purpose and character, including whether it's commercial or nonprofit. Two is, the copyrighted works nature. Three is how much of the work was used and how substantial a part of it was relative to the copyrighted work as a whole.
And four is how the defendant's use [00:26:00] affected the copyrighted work's value or potential market. Now, the judge in the Reuters case, dismissed the fair use defense as a matter of law. That means it doesn't even have to go to trial, it's just settled. And the reason that was is because Reuters. Prevail, Reuters had problems with factor one and factor four.
The judge noted that Ross's, Ross Intel's tool wasn't generative. it's helpful in this case, They returned existing text rather than creating new text and it directly competed with Westlaw. So the use wasn't transformative and it was direct competition.
The decision mark, the first time a court squarely held that feeding copyrighted material into an AI without permission was not their use. and the lawsuit was actually such a financial burden that the company went under. So that's like the first example. The second one is that anthropic suit.
And here what we had was music publishers, universal Concord, et [00:27:00] cetera. They Sue Anthropic, which is the maker of the Claude AI chatbot, and what they alleged was that Claude was using song lyrics in training data without permission. So in March of this year. The publishers sought a preliminary injunction to stop anthropic from using their lyrics.
The judge denied that, which, some people said is an early victory for AI companies, but really it was a limited win. The court didn't rule on the ultimate issue of fair use, so it just found that the publishers hadn't shown immediate irreparable harm. And that's what you need for an injunction.
So the judges ruling emphasize the existence of a market for AI training, data licensing. And I think that's more important than just saying there wasn't irreparable harm. So what she did was point out that music publishers can license their catalog to AI companies for training. So that's like an emergent market for licensing.
And to me that's not, if you're pointing that out, you're not saying,clearly [00:28:00] you can just use this as fair use. I think it's part of why you are starting to see startups like music.ai tout their ethical training data, which is, licensing the copyrighted data that they're using. Also in the Anthropic Clay case, explicitly noticed that she wasn't deciding fair use at that stage. But her comments just, I just don't think they bode well for the future of, using the fair use defense in such cases. So the case is ongoing, but to me it signals that music, AI developers face serious questions about using unlicensed content.
In these cases, what they really show is that the legal landscape is in flux. So on one hand, you know you have these tech companies that are arguing that ingesting copyrighted works. To teach an AI is transformative and should be fair use. On the other side, you have content owners insisting AI training is just like any other use that requires a license, especially.
If it threatens their licensing revenue. So battle's not over. lines have been drawn. [00:29:00] You're gonna see more lawsuits by authors, artists, media companies, they're queued up. but these aren't Supreme Court rulings, so it's not a settled issue.
And the discussion doesn't even touch on, what I was mentioning before about terms of use, more like contract breaches, from pulling data and how you're using it. So you know, there's the question of ingestion. can you use these copyrighted materials? And then there's also, once you create the content, who owns it.
So there's so much that is a moving target right now. But it's a really, it's a really interesting field. So before, again, to my point that I was raising, before you really start diving in and creating all this stuff and training your model and get to the point where you're ready to scale, you really should be talking to an IP attorney and figuring out, how do we work in a way that.
Makes us a sustainable long-term company because you need to be able to sell your company. You need to be able to take it public. You need to be able to borrow, so you don't wanna get to a place [00:30:00] where you've scaled and you can't do those things if you're not in it to win it. I don't know why you're here.
If you're in the venture space, I guess I would say.
Dmitri: That was super helpful to, to get a very well expressed summary of some of the things that are going on in the courts right now around gen AI and this fair use versus licensing training, data debate. even though it's still in flux and we don't know what will happen next or if and when it gets to the Supreme Court, what the fi, some of the final decisions will be there, but interestingly.
It is in contrast to the data you presented at the beginning around where money's actually getting invested in tech and in music tech specifically. and I guess I might just ask a little more how the, I mean, obviously
Valeska: really. Right, because you had those big investments in content libraries. So that means that the big player, your boring, stable, private equity guys that make smart, informed decisions are buying content libraries. So to me, along with [00:31:00] that Anthropic case, I think that the market is making big bets on content, having more value and having more way, to your durability of revenue.
More ways to like create different license streams from different customers, different segments, different It, I think that's all great. So I
Dmitri: I guess, the only reason I said maybe it doesn't align is 'cause there's also these multi-billion dollar investments in gen AI companies that are going down the fair use, pathway. Am I wrong?
Valeska: I don't know all of their licensing models. You're not wrong, but I think you do see something, you know, when you have an emerging industry. And now everybody is thinking about fair use, four years ago we weren't. And so they were developing these models. They've developed over time, people have been thinking about it.
So you had the same thing. Totally different industry. But just for an example, blockchain had that problem where they threw out all these tokens or all these offerings and the SEC came after them and said, [00:32:00] everything's a security. and. It was the same problem. You had an industry that was really trying to.
Innovate and it just didn't work with the existing framework for various different reasons. Like these things really did look like securities in a lot of ways. but you were selling them and for you to be able to like create this ecosystem, you needed to be able to send the tokens to various.
Ecosystem participants. And so they did. And so you have the same, a similar thing here where the innovation is really outpaced where the law is. And so you have to figure out, okay, here's where we are today. And this is just a policy argument. this is just me thinking. but I think you have to stop and say, okay, well there's these big companies that have trained these huge models this way and they've created this.
what do we do about what they've done in the past and how do we. Fix that to, and then what do we do going forward? Like how do we build on this? we don't wanna throw the baby out with the bath water. but we need a fair solution that works for all the parties. and that's really hard to do.
And I think that's why you see, competing interests where, you're [00:33:00] right, people are investing in these big generative AI companies. but the fair use doctrine, and this is this year, Dmitri like. Just a couple months ago, or last month in one case. So it's just new. and I think everybody's just trying to sit down and figure out like, what makes sense?
How do we move forward? What are the courts gonna do?
Dmitri: thanks for diving into that conversation. There's a lot more we could say about, this and it's still unfolding, so we'll see. but now that everyone's heard you talking about all this stuff and starting to get a little bit of sense of how you're involved with it, let's get a little more specific there.
How does a law firm like yours get involved with startups and investment, and how far along do companies need to be to enlist your help?
Valeska: So at a large global law firm like mine, we're built to support high growth companies and their investors through every stage of the journey. From formation to IPO and beyond, I work across sectors, so tech, biotech, consumer products. What I'm really focused on is companies that are backed by institutional investors.
generally it's, private equity, [00:34:00] venture capital, and bigger family offices. So whether it's the series C startup laying Its Foundation or a late stage company preparing for a public exit. I'm often the first call when the founders are setting up governance, structuring their next round, or thinking strategically about growth.
And I've worked on multiple unicorn deals and over a dozen IPOs, so I've seen firsthand what it takes to grow and operate at that level. On the investor side, I represent venture firms, private equity funds, and family offices helping them. Evaluate the opportunities. They're looking at structure investments and execute complex transactions.
Because of the scale and reach of my platform, I'm able to tap into deep subject matter expertise. that's things like privacy, generative ai, looking at regulated product counseling, international expansion. So clients get full spectrum support. interestingly, I also partner with an internal team that we have that goes beyond traditional legal services.
So our [00:35:00] startup and investor services group helps our clients refine their pitch decks and connect with investors, and that is without cost. That's just a free service. It's a value add. That team's entire job is to maintain deep relationships with venture capital firms, family offices, and private equity investors, and to really understand exactly who's investing in what, at what stage.
And in what sector? So it's a valuable resource for both founders and investors. as for when to bring us in, firms like mine aren't always the most cost effective solution for every task, especially at early stages. when the stakes are high or the issues are complex, entering the US market, raising capital, conducting a tender offer, navigating delicate board and stockholder situations.
Prepping for your IPO or an acquisition, negotiating major commercial deals undergoing regulatory review of your product or services. That's where big law firms really bring the most value. So no matter the stage, my focus is always the same. Clear business focused advice that helps clients [00:36:00] make smart decisions and keep building.
Dmitri: So one last question, VE Alaska before I let you go, you've covered covered a lot of, a lot of territory here and, would love to see what you're thinking is coming next.
What's the outlook of music tech investment for the rest of 2025? I know it's a tough question, but just wanted to put it out there.
Valeska: So when we talk about the outlook for music tech investments in 2025, we can't isolate it from the broader venture capital landscape, which is facing serious headwinds right now. So macroeconomic uncertainty, tight capital and a frozen exit environment are shaping investor behavior across. All sectors and music tech is no exception.
Looking into the rest of 2025, the outlook is challenging across the board. There's real uncertainty out there. So think geopolitical risk rising, including early stages of a trade war. yes, equity markets are volatile. What really rattled investors recently was the bond markets. So when treasury yields start moving fast, it signals uncertainty about inflation, interest rates, and [00:37:00] basically the cost of money.
And when money gets expensive or unpredictable, venture capital tends to hit the brake. Especially when there's no clear path to liquidity. how do you value a private company in that environment? Especially, like a late stage unicorn. the IP window for now is shut. You had Figma, which makes collaborative design software filed confidentially, but others like Klarna, the Swedish Buy Now, pay later, FinTech and Hinge Health, which offers digital physical therapy have both postponed their offerings.
M and a activity isn't really filling the gap either. Without exits, there's no liquidity for limited partners, those are the folks who invest in the venture capital funds, and that means the general partners, the people running those funds can't raise new capital to deploy. So if you're a founder of building the next AI powered music tech platform, that series A tech might be a little slower and smaller than you'd hope for, or it may not come at all.
unless we're seeing a meaningful shift, like a pickup in IPOs or acquisitions, we're really likely to [00:38:00] see. Unfortunately, more down rounds where startups raise lower valuations than their previous round, more distressed m and a and in some cases just plain shutdowns, which some VCs have been predicting for a couple years now, if the broader economy slips into recession, the risk only gets worse.
So look, the VC market is cyclical, and investors by their nature are optimistic. They have to believe that the future is gonna be better. That's like the whole premise of investing in innovation, right? So some of the most iconic companies were built in downturns. Remember like Airbnb and Dropbox were born during the 2008 financial crisis.
Spotify scaled during that same time period. Uber, Instagram and others emerged from similar environments. So keep in mind that during financial crisis and more recently in the tech post pandemic period layoffs resulted in a bunch of seasoned operators using that as like a reset to launch their companies.
And those were companies they'd probably been thinking about for years. These weren't [00:39:00] your first time. People starting out, they weren't guessing at product market fit. They were veterans solving problems that they knew from the inside. So to me, these moments, demand, focus, and discipline and creativity, which is exactly where founders shine.
And I kind of wanna leave you on a high note with, music tech. There's real opportunity. AI is opening up new frontiers in how music is created, produced, and monetized with tools that can make the industry more accessible and artist centric. So now is the time to be scrappy and build towards those KPIs we were talking about that show durability and value, to stick close to your users.
Don't wait for the market to bounce back and build what defines the next cycle. In some yes, it's a tough moment, but it's also a moment to build and I think the next wave of meaningful innovation is already in motion, and now is the time when the next unicorns are gonna be built.
Dmitri: Wow. Plus Valeska Pederson Hintz says that [00:40:00] music's cooler than the rest of Tag. She said it here on the Music Tectonics podcast and everybody knows it. Those family offices, every time you come across 'em, you gotta remind 'em how cool music is for us. Okay, Velasco.
Valeska: that.
Dmitri: a great interview. Thank you so much for coming on to the Music Tectonics podcast.
Valesquez, a partner at Perkins Coie and really great at articulating so much great information. So clearly. Thanks for being here.
Valeska: Thanks for having me.
Let us know what you think! Tweet @MusicTectonics, find us on LinkedIn, Facebook and Instagram, or connect with podcast host Dmitri Vietze on LinkedIn, Twitter, and Facebook.
The Music Tectonics podcast goes beneath the surface of the music industry to explore how technology is changing the way business gets done. Weekly episodes include interviews with music tech movers & shakers, deep dives into seismic shifts, and more.
