Mikey Shulman: Suno and the Sound of AI Music
Lightspeed Venture Partners2024-03-07
645 views|2 months ago
💫 Short Summary
Mikey Schulman, CEO of Sunno AI, shares his transition from music to AI-generated music, highlighting the joy of music creation. Kentro's acquisition by S&P Global led to audio AI projects, emphasizing the value of audio content. The founders left finance to focus on audio, developing the Bark text-to-speech model. Generative AI enables interactive music creation, impacting music format and consumption. The future involves personalized music experiences and dynamic music creation. Collaboration with Microsoft expands creative outlets, emphasizing user-centric product design. Ethical considerations and AI's potential to push creative boundaries in music are discussed. AI's role in music production and artist collaborations is evolving, with a focus on unique sounds and legal considerations. OpenAI's licensing deals and potential collaborations with artists like Taylor Swift for generative music creation are explored. Cambridge's innovation hub fosters talent in software, machine learning, and music, emphasizing a passion for music and building companies.
✨ Highlights
📊 Transcript
✦
Mikey Schulman discusses his background in music and transition to working on AI-generated music.
00:41Schulman talks about playing piano and bass in bands and the challenges of recording in a studio.
Despite studying physics at Harvard, he ended up at Keno through a series of fortunate events.
Schulman emphasizes the joy and satisfaction of creating music that brings joy to others.
✦
Introduction to Kentro and Acquisition by S&P Global.
04:17The speaker was introduced to co-founders at Kentro during grad school, leading to a job offer in a company specializing in machine learning for financial services.
Kentro was acquired by S&P Global in 2018, providing access to valuable training data and inspiring the team to explore audio AI projects.
The team found audio data to be more beautiful and complex than images and text, motivating them to focus on advancing audio technology.
The increasing importance of audio content consumption influenced their decision to pursue audio AI development.
✦
Importance of audio content in comparison to video content.
07:59Models for audio tasks are similar to text models, limited by available labeled data.
Training large self-supervised models on unlabeled text and audio data is increasing to address lack of training data.
Working with audio data is more challenging than text due to difficulties in inspecting and organizing the data.
Audio transcription was initially secondary to text at Keno.
✦
Transition from financial services to audio projects.
11:07Founders developed text-to-speech model called Bark.
Community showed interest in music over text-to-speech.
Shifted focus to music projects due to community support.
Driven by community enthusiasm, founders pursued music-related endeavors.
✦
Development of Bark music model.
16:03Bark was not designed for music creation.
Transformer technology inspired by academic work was utilized for audio modeling.
Bark incorporated elements from GPT for text-to-speech conversion.
Progress in generative music opportunities has advanced since Bark's release.
✦
Generative AI enables active music creation without advanced skills.
17:20Users can easily create personalized music by inputting prompts.
The technology opens up new social dynamics and collaborative opportunities in music-making, akin to gaming experiences.
The focus is on enjoying the creative process and final product, with potential for generative music to shape the future of music creation at scale.
✦
Evolution of Music Format with AI
21:53AI-generated music is leading to shorter songs to capture attention quickly.
Artists are using AI tools to optimize music for streaming platforms like Spotify.
Changes in music format are influenced by platforms like TikTok and stream payouts.
Future of music format is uncertain, but expected to keep evolving with AI's influence.
✦
Impact of Generative AI on Personalized Music Content
24:21Emphasis on smaller niche music tailored to individual tastes, facilitated by AI in real-time.
Evolving creative process influenced by faster models for music creation.
Envisioning a future where music is dynamically generated to match personal preferences.
Openness to diverse uses of created music, from sharing on YouTube to intimate group settings, highlighting the changing landscape of music consumption and creation.
✦
Future of music delivery and consumption.
28:30Current unidirectional nature of streaming music is discussed, emphasizing the lack of social connections between artists and listeners.
Sharing dynamics, social interactions, and remixing are seen as key factors in shaping the future distribution of music.
Microsoft partnership and integration of a free tier into co-pilot for creative experiences is highlighted as a significant development.
✦
Importance of aesthetics in product design for music creation.
31:18Listening to the output of models rather than relying solely on quantitative metrics is emphasized.
Creating intuitive workflows for users with diverse music backgrounds is a challenge highlighted.
Catering to users with specific music tastes who may not be experienced in music production is the goal.
Caution against relying solely on academic reasoning for product design, stressing the importance of considering user experience.
✦
Importance of exploring various ways to enable people to create music beyond text-to-music models.
33:51Emphasize the need to think from a user-centric perspective and focus on what inspires individuals to make music.
Introduction of the concept of 'soundtracking your life' to encourage drawing inspiration from everyday experiences.
Highlight of ethical considerations regarding existing artist rights and commitment to creating an artist-friendly and legally compliant platform.
Envisioning this approach as the future of music creation.
✦
Limitations of AI models in creative tasks such as music composition.
38:35AI models like GPT are compared to artists who push boundaries and create innovative work.
Potential for AI models to exceed existing limits and inspire new forms of creativity is explored.
Examples of using technology like autotune and effects pedals to enhance artistic expression are provided.
Importance of drawing inspiration from various sources and the role of AI in pushing creative boundaries are emphasized.
✦
Potential of AI in Music Production
40:05AI can enhance music by focusing on unique sounds over chord changes.
Advancements in AI technology will affect music production processes and relationships with rights holders.
Geographic variations could pose challenges for AI models and raise concerns about hacking risks.
The future of AI in the music industry is uncertain, with legal and ethical issues taking precedence for professionals in the field.
✦
AI licensing deals with media publishers for generative music creation.
43:56Collaboration opportunities between AI models and artists like Taylor Swift are emerging.
Google's Project Lia already involves big artists in AI music creation.
Structured licensing for AI-generated music may allow users to prompt models with specific songs for a fee.
Despite concerns, there is optimism about AI uncovering new music possibilities with advancements in audio fidelity and song quality.
✦
Innovation hub in Cambridge, Massachusetts for building companies and attracting top talent in various fields.
46:37Passion for building things and music highlighted as a good fit for individuals.
Gratitude and enthusiasm expressed for the conversation.
Encouragement for listeners to rate, review, and subscribe to the podcast for more content.
00:01[Music]
00:05hey everyone and welcome back to
00:06generative now this is the show where we
00:09talk to the builders who are creating
00:10the world's most exciting AI products
00:12and companies I am Michael mcnano I am a
00:14partner at Lightspeed and today we've
00:16got an awesome conversation I spoke with
00:18Mikey Schulman CEO co-founder of sunno
00:22AI about the future of AI generated
00:24music we talked about why audio's been a
00:27bit behind the curve behind other
00:28modalities like text and video
00:30and how AI generated music could change
00:32how we experience music moving forward
00:35so take a listen to this conversation
00:36with Mikey
00:38Schulman hey Mikey hey it's great to be
00:41here I am so excited to talk about sunno
00:45uh it is definitely one of the most fun
00:48AI products I've played with and it
00:50seems like others that have have used it
00:52all have uh similar feelings and so I'm
00:55excited to get into it excited to get
00:56into the story uh before we do that I
00:59think we have have to go back a little
01:00bit because what I understand about you
01:03and your interests and passions growing
01:05up they're very aligned with what you're
01:07working on right now yeah definitely uh
01:09I think i' I've definitely been doing
01:11music for much longer than I've been
01:12doing AI uh played a lot of piano as a
01:15kid um taught myself a few other
01:17instruments uh played bass in a in a lot
01:20of bands in in high school and college
01:22um in New York uh some some very small
01:26clubs that maybe you've heard of uh and
01:29um really really fell in love with it I
01:31think
01:33um you know I I remember very distinctly
01:35the first show um I played ever uh was
01:39uh with a a four-piece band and I
01:42remember at the end of the night getting
01:43a very small stack of bills uh from the
01:46promoter and I remember just thinking
01:47like wow this is Criminal uh I know that
01:50I had more fun than the audience did
01:52tonight it wasn't a very good show but
01:53it's just so fun to make music uh with
01:56people and um you know it I guess it
02:00comes full circle having having done a
02:02lot of stuff in between then and now
02:03being able to let people make music uh
02:06is a lot of fun and they enjoy it and it
02:09definitely feels good that we make
02:11something that makes people smile well
02:14it it sounds like you were entertaining
02:15people you were getting paid for it so
02:17um what about what about did you did you
02:19and I guess your your bandmates did you
02:21try to to you ever spend any time in the
02:23studio did you try to record music or
02:25only ever really playing out live we did
02:27we we recorded uh one EP I'll say like
02:30I'm certainly no seasoned uh Studio
02:32musician so uh take this all with a with
02:35a grain of salt but like a lot of takes
02:37it's like laborious and and at least for
02:40me in many ways less fun than just
02:42playing for fun um I I actually remember
02:46uh one track I um I it was actually like
02:50a really good take and I actually kind
02:52of like slipped off my chair a little
02:55bit like at the very very end um and we
02:58end up having to basically redo the
03:00whole thing it was like really really
03:01sad yeah and and and just thinking like
03:04wow that would never have happened if we
03:05weren't recording so so it it seems like
03:07you were very focused or at least for
03:09part of your life really focused on
03:10music but uh you studied physics at
03:12Harvard which was a very very different
03:14thing than music and then you end up you
03:18end up at Keno right tell us tell us a
03:20little maybe how did that happen and
03:22tell us a little bit about Keno maybe
03:23for people that that don't know about it
03:25yeah total total accident honestly
03:27really a nice right place right time
03:29type of a thing for me um I was in the
03:31last year of grad school and I was
03:33introduced to some people at Kento one
03:35of whom is is one of my co-founders now
03:37Martin um and we went to lunch uh
03:42because I was local and um at lunch they
03:44said when do you want to come interview
03:46and um I said I don't know I'm a grad
03:49student I can do whatever I want and
03:50they said how about right now and so we
03:51went upstairs and I and I interviewed
03:53and I did very poorly um but they they
03:56decided to to give me a chance and yeah
03:59like I said right right place right time
04:00a lot of a lot of stuff kind of uh
04:02luckily went right at kentro so kro was
04:05a company that did um machine learning
04:07for financial services um we did a lot
04:10of NOP you can think there's a lot of
04:12documents that are really financially
04:14relevant and had you kind of in an
04:16automated way make sense out of a lot of
04:17those um uh acquired by S&P Global in
04:212018 in some sense this was amazing uh
04:24you know we we got acquired by a company
04:26with the kind of the one of the biggest
04:28if not the biggest Pro of financial
04:30documents that you might want to play
04:31with so you know you're kind of like a
04:32kid in a candy store um got lots of good
04:35data to train on we had a lot of fun
04:37learned a lot met a lot of great people
04:39um and sort of was the the impetus for
04:43for starting sunno is we uh we did one
04:47audio project which was uh learning to
04:49transcribe earning calls so do speech to
04:51text um on public company earning skulls
04:54so if I don't know if you've ever read
04:55one Mike uh in in your SE the transcript
04:58I have I have I you know when I was at
05:01Spotify I would uh I would try to listen
05:03to most of them but whenever I couldn't
05:05listen I would I would read the
05:06transcripts so Prett pretty dense L
05:09dense information yeah not not real page
05:11Turners but um there's a good chance
05:13then that you read an S&P transcript um
05:16oh okay uh and uh a lot of people resell
05:18the S&P ones and they're perfect uh
05:21those transcripts and um pre 20109 they
05:25really couldn't be done with any um
05:27automation or machine learning and so
05:28you know people would ask like why don't
05:30you just go to insert your favorite
05:32cloud provider here and get speech to
05:34text from them the answer is that it
05:36will come back with so many errors that
05:38it will take longer to fix than to start
05:40from scratch um and of course if you
05:44have 20 years of perfectly transcribed
05:46training data sitting in the basement
05:48and you're a machine Learning Company
05:50like this you know your eyes kind of
05:51light up and that was that was our first
05:53foray into um audio AI um and like the
05:58rest is history we felt fell in love
05:59with it it is like it's so beautiful
06:02it's so much more human it's messy
06:04there's so much interesting stuff in it
06:07um and uh it is so far behind images and
06:11text and I think that was definitely
06:13true in 2019 when we started on that
06:15project and you know in some sense it's
06:17more true now if you just think about
06:19everything that you've seen happen in
06:21images and text and um there's nothing
06:24fundamental that says audio has to be
06:26behind forever um and that was I guess
06:29the kick in the pants that we needed to
06:30say okay let's go and and let's do this
06:33right we we love we love audio we love
06:35AI um let's let's do this yeah I mean
06:39that's an observation we we had at
06:41Spotify often uh and Daniel e the CEO
06:43has talked about this um in that audio
06:47is something that everyone consumes we
06:49all listen to music we all you know many
06:52of us listen to podcast audio books we
06:54the radio I mean it is massive massive
06:57in terms of the amount of attention that
06:58gets uh allocated to consuming audio
07:01content but for some reason it hasn't
07:03necessarily been valued in the same way
07:05as as say video I think things are just
07:08beginning to change now but you know
07:09even rewind just six months or something
07:11like that and audio looks a lot like
07:13text did in 2019 which is to say um
07:18there's a lot of very uni purpose models
07:22like very task specific models and so I
07:24you know we we we we look at text a lot
07:26to kind of understand the landscape and
07:28so imagine uh I I know this we did this
07:30at kencho things like named entity
07:32recognition it's like here's a piece of
07:34text let me pull out all of the people
07:36and places and companies in this piece
07:38of text and you'll train a model to do
07:40only this one thing and you will always
07:42be extremely data limited you're
07:44basically always just limited by how
07:45much labeled data you can get your hands
07:47on and I think audio in those two tasks
07:51looks a lot like that it's like how much
07:53label data can I get on speech to text
07:55or how much label data can I get on text
07:57to speech and
07:59these models end up small and uh
08:02somewhat brittle because of the lack of
08:04all that data and now go back to text no
08:07one would ever think about training a
08:08named entity recognizer model you would
08:10just paste some text into GPT and you
08:12would say give me the named entities in
08:14this piece of text and it's because
08:16there was some sort of Paradigm Shift
08:17there of I'm going to forget about a
08:20single purpose model I'm going to train
08:21a very large self-supervised model on as
08:23much unlabeled text as I can get my
08:25hands on and okay then let's just do the
08:28same thing for audio train a large
08:30self-supervised model on as much audio
08:32as we can get our hands on and then we
08:33can figure out how to make it do the
08:35things that we need to do um and I think
08:37you're just beginning to see that happen
08:39in
08:40audio so if I could um maybe my my
08:44understanding of what you're saying
08:45probably in a much more uh simple simple
08:48way because I'm obviously not nearly as
08:50close to the research as you are it
08:51sounds like lack of training data lack
08:54of data has been a big limitation G GA
08:56gathering up a bunch of text doesn't
08:59seem that hard there's you know a
09:01treasure Trove of it sitting on the
09:03internet audio maybe not so much is is
09:06that kind of what you're saying big time
09:08Big Time so there's no common crawl or
09:10pile for audio um and even if you had it
09:14which you don't it's really hard to work
09:16with audio um you can't easily inspect
09:19your data you can't easily search over
09:20your data you know in text if you just
09:22have two data points and you want to
09:24know are these two data points the same
09:26are these two sentences the same that's
09:28really really easy to do in audio that's
09:29really really hard to do and so you just
09:32have to be much more careful um with how
09:36you arrange an organize your data um in
09:38a way that you just don't have to in
09:40text and so yeah it makes people not
09:42want to do it got it so how much of the
09:46focus of Keno was on this work that you
09:48were doing around audio and
09:49transcriptions of earnings call was that
09:51just sort of a sliver of the work and
09:53and where did that happen along sort of
09:54your journey at
09:56Keno um it was like a it was a part but
10:00not not the majority at all like most of
10:02what we were doing was text and it
10:04happened um maybe started a year after
10:07the acquisition you know after we kind
10:09of leared uh uh where all the data lived
10:13and What all the interesting projects
10:14are and I think um don't get me wrong
10:18there are a lot of future interesting
10:20things to do in financial services with
10:22audio but I think ultimately there's way
10:25more stuff with audio outside of
10:27financial services uh that you can do
10:30and that's like really what Drew us to
10:31want to go do that and I think um
10:35Financial Services is a for good reason
10:38a somewhat more risk averse uh domain
10:42and so I think there's so many
10:44interesting things to do with text there
10:46that it will be hard to look away from
10:47those text based projects got it so so
10:51you're you're you're running machine
10:52learning at Ken show then obviously you
10:54go through the acquisition you're
10:56inspired by this this challenge around
10:58audio did did you and your co-founders
11:00actually I believe all your co-founders
11:02were at Keno did you leave specifically
11:04to go work on this audio project that
11:06became
11:07soono uh pretty much we knew that um as
11:11much as we loved our jobs like pursuing
11:13audio in a Financial Service Company
11:15doesn't necessarily make sense and so um
11:18I think we didn't know exactly the form
11:20uh that our product would take but we
11:22knew that there was big opportunities
11:24yeah so tell us a little bit about that
11:27that sort of founding Journey you know
11:29I'd love to hear a little bit about the
11:30the early days of sunno you know it's
11:31funny we we looked around for a while
11:34and we talked to a lot of people doing
11:36things in audio um and in some sense one
11:39of the biggest learnings there was what
11:40I said before is like people really
11:41don't like working with audio um and I
11:44think maybe that's what made us special
11:45is we really liked working with audio
11:47but like most people don't like working
11:48with audio and we we knew that um the
11:53quote unquote right way to do things
11:55would be to to try to train a a big
11:57self-supervised model of Foundation
11:59model is is the buzz word and we always
12:01said we we wanted to do this right so
12:03that um things are kind of future proof
12:05and it gives us capabilities um very
12:08easily compared to training things in
12:10sort of the old style way and honestly
12:12we weren't sure the instant we left Keno
12:15that we would do music uh as opposed to
12:17speech you know we knew speech very well
12:19a lot of people um told us like speech
12:21is this really really big Market don't
12:23focus on music like it that would be a
12:25really really bad idea and I think a
12:27couple of things happened one is you
12:29know by virtue of all four of us being
12:31musicians and audio files like we kind
12:33of couldn't help ourselves in some sense
12:35of of of starting to do music um and
12:38then there was another really big
12:39realization which is that we put out um
12:42an openin source project called bark
12:44which is a text to speech model um and
12:46it got good update by the community got
12:49a lot of GitHub stars and what we found
12:52is that um the we would ask people uh
12:56like what they were interested in um on
12:58the the there there was a little sign up
13:00form on the read me from bark and the
13:03biggest thing people wanted was music
13:05and this was like a big aha moment for
13:06us it's like here's this text to speech
13:08model it got a lot of popularity people
13:11like it and the biggest thing that
13:13everybody here wants is not text to
13:14speech it's music and so um if you kind
13:17of can't help yourself from trying to do
13:19music and then everybody in the
13:20community is telling you to go do music
13:22it's that's like a pretty strong signal
13:24that was a little over 6 months ago so
13:26we're like just past 6 months from
13:27releasing our first music model
13:29um we've released a few more since then
13:31and yeah haven't haven't really looked
13:33back so so you you come out with bark
13:37and you're saying developers in the open
13:39source Community are saying hey we want
13:41to use this for music We want to build
13:43music applications with this model
13:45that's right and in fact you know we had
13:47a we had a little Discord server um and
13:50we would see people trying to pull music
13:53out of bark albeit poorly and this is
13:56like a this is like a real clue of like
13:57here's somebody really abusing your
13:59model to try to do something it's not
14:00meant to do because they really want to
14:02do it yeah what what was that like like
14:04did it work was it actually spitting out
14:06music or what what did that sound I I
14:09will decline to say what is and is not
14:10music um no it it spit out like little
14:14bits of Music sometimes not terribly
14:15reliably obviously um you know and
14:19sometimes it would sing and sometimes it
14:20would be background music and and it was
14:22a total crapshoot but um this was a
14:24model meant to do something very very
14:26different got it and and and maybe talk
14:29a little bit about the model and and and
14:31how it worked and you know give us sort
14:34of the simplified version of of how this
14:35model Works maybe compare to for some of
14:38the other modalities we talked about
14:39text and video it's no secret we're
14:41we're big fans of transformers this is
14:43probably our text backgrounds um not all
14:46audio is done with Transformers there's
14:48a lot of audio that's done with
14:50diffusion and these two um these two
14:53methods have pros and cons depending on
14:56the modality also but we we are big fans
14:58of Transformers
14:59um some of it was inspired by some
15:00academic work but there really was very
15:02little out there in the open source
15:04around doing things in audio with
15:07Transformers and so there was a lot of
15:08kind of basic research or or technology
15:11that we had to build um we had the Good
15:14Fortune of by picking something like
15:16Transformers where there's a lot of work
15:18being done in text there's a lot of
15:19stuff from the open source that we were
15:21able to B to borrow so if you go and you
15:23look at the source code for um bark
15:25you'll see big capital letters we we we
15:27thank and and attribute a lot of the
15:29code to for example Andre kpa's nanog
15:31GPT which is like a really really easy
15:33strip down implementation of GPT and
15:36it's just like having resources like
15:38that available to us really let us focus
15:40on the bits that we love to do which is
15:43really understanding audio and really um
15:45trying to model audio correctly and so
15:48um bark was a series of a few
15:51Transformer models that you need to turn
15:54uh text into ultimately nice sounding
15:57speech or sometimes uh in some abusive
15:59use cases nice sounding music but um
16:03yeah you know it's it's it's crazy to
16:04think that that was a year ago uh I
16:07think we've Advanced a lot in the
16:08community uh and the open source
16:09community and the research Community has
16:11also Advanced a lot since then so so
16:14developers Wanted music you guys as
16:17Founders were very passionate about
16:18music sounds like you were all musicians
16:20but maybe beyond the passion for
16:23music why music from a product Direction
16:26like what's what's the opportunity for
16:29generative music I think I think there's
16:31a lot uh and I think we only focus on on
16:34some of it I'll tell you uh the corner
16:36that we focus on which I think is uh the
16:39most interesting and the most fun um is
16:44making music that doesn't exist and so
16:48um I think you know the way we think
16:50about this is let's just look at the
16:52music landscape right now most people uh
16:57have a streaming service like Spotify or
16:59apple music and they kind of sit there
17:01passively listening to music much of the
17:03time maybe not even paying attention to
17:05it and um there is so much more to
17:08experiencing music than just that and
17:12one very big thing is creating music and
17:14I I guess I'm I'm lucky to have
17:16experienced that Joy creating music with
17:18people but there's usually a pretty big
17:20barrier to doing that which is like
17:22getting pretty good at an instrument or
17:24getting pretty good at some complicated
17:26piece of software like Ableton or log or
17:28even Garage Band and you can think
17:31generative AI is a tool or means to an
17:33end to letting everybody kind of take
17:35the sounds that are in their head and
17:37and make really nice music out of it you
17:39know one thing one thing that like we
17:41often think about is you look at like
17:43the gaming industry is 30 to 50 times
17:47bigger than the music industry and you
17:49know if you wanted to be really
17:50reductive about why that is it's because
17:52like you are a very active participant
17:54in gaming and so you know one one thing
17:58to think about is like what are the
18:00other 49 50ths of music that we've just
18:04yet to build a compelling product
18:05experience around and it's not only like
18:07I sit in front of my computer and I make
18:09music and then I share it with someone
18:11although that is really really big but
18:12there's lots of things from doing it
18:15with people like you often play games
18:16with people and that's can be some of
18:18the most fun gaming experiences uh you
18:21know we we talk a lot about having
18:23collaborative concerts and stuff like
18:24that um or little Jam sessions there are
18:28I think totally unexplored social
18:30dynamics around what happens when you
18:31let people make music for their friends
18:34and share it the same way you might do
18:35with an image so there's just so much
18:38that is I think unexplored and we're
18:40just beginning to explore the creation
18:42bit now but um there's a lot more to
18:44come after that maybe to set the context
18:46for I'm I'm sure a lot that we're going
18:48to be talking about with this what is
18:50the product today just talk about whatso
18:52does specifically today so today we let
18:55you um in a couple of ways make songs
18:58that don't exist right
18:59[Music]
19:05now so there is a song that's in your
19:07head there's something that's going on
19:09in your life right now and you can turn
19:11that into a song uh the same way that
19:13you might write in a journal or take a
19:15picture of something you can turn it
19:16into a song and music is a really human
19:20uh form of expression that I think we're
19:22all really hardwired to want to do uh
19:24you can do something as simple as type
19:26in uh make me a reggae song about
19:30podcasting um or you can go a little bit
19:33deeper and you can bring your own lyrics
19:34and you can really understand the
19:36different ways you can prompt these
19:37models to do things whether it's with uh
19:40playing with line breaks or saying this
19:42is a verse and this is a chorus or uh
19:45describing the music with adjectives
19:47that most people would not think to use
19:48to describe the music and out will pop a
19:50song um and hopefully you enjoy it um
19:53and I think most importantly hopefully
19:55you've enjoyed the process of of
19:57arriving at the the final product now at
19:59scale like when people are doing this at
20:01scale like what do you what do you think
20:03the role of generative music will be in
20:06relation to non- AI generated music
20:10that's you know fast forward five years
20:12from now like are my kids instead of
20:14listening to Taylor Swift are they going
20:15to be listening to AI generated music is
20:17there a hard line or how how do how will
20:20these things coexist I'm not necessarily
20:22sure there's a hard line because I think
20:24AI is a set of tools and those tools
20:26will come to artists as well and while
20:28we are not focused on that other people
20:30are and I'm sure there's going to be a
20:31lot of work um where artists do things
20:34with AI but um I think the answer is
20:37definitely both and um you like
20:41listening to Taylor Swift because you
20:42have some connection with her and her
20:44music and what she's writing about and
20:46you will also like listening to the song
20:49that your best friend made for you
20:51because you have a very different kind
20:52of connection with your best friend and
20:55um I think you know when I said
20:59uh we're trying to figure out what the
21:00other 49 50ths of Music really is that
21:03is to say I don't think that first 50th
21:04is going away it's like really really
21:06important um
21:09so I yeah I I think I think there is a a
21:12pretty big Continuum here that we're
21:14trying to explore how much do you all as
21:16a team think about the format of music
21:18as it exists today right now music is
21:20delivered obviously digitally via
21:23streaming service Spotify um it's
21:25organized into these algorithmic
21:27playlists
21:28that could deliver to people as a result
21:31the you know the actual creative format
21:33of the music is often optimized by the
21:35artist by The Producers to play into
21:37that you know it's something like two
21:39and a half minutes to three minutes gets
21:41right to the you know gets right to the
21:42hook to catch you as quickly as possible
21:44how much do you think about the format
21:46of music as it exists today and how do
21:48you think the format will evolve in a
21:51world where much of it is AI generated
21:53it's a really good question we think
21:54about this a lot and I think the the
21:56short answer is we don't know but we
21:58know it's going to change and we know
21:59that it is changing and I think uh you
22:03know like you mentioned uh songs are now
22:05you know two and a half to three minutes
22:07and maybe five years ago they used to be
22:09longer and 10 years ago they used to be
22:10longer still and they're trying to get
22:12to the hook faster you know for for
22:14various reasons having to do with how
22:15streams are paid out um you have things
22:17like Tik Tok now where you're taking 60
22:19seconds of an existing song and you're
22:21doing something with it and I think AI
22:22will certainly accelerate some of the
22:24changes in format but we don't exactly
22:26know what they are and we are playing
22:28around with them a lot you know one
22:29thing that I I think pretty firmly is
22:31you know right now we make songs that
22:32are uh you know one to two minutes and
22:35but a one minute song is very different
22:37from a one minute clip from an existing
22:40song and how do you condense the Journey
22:43of a whole song that used to be 3
22:44minutes into one minute is something
22:47that I don't think it's for us
22:49necessarily to Define I think it's for
22:50us to make the tools to let people make
22:52the music and then that new art form
22:55will kind of emerge um from people
22:58making stuff you know and then kind of
23:00as a Cary when you have a lot of one
23:03minute songs not one minute clips of
23:04songs what is a playlist mean playlist
23:07Maybe now takes like different forms
23:08like maybe actually I make an album and
23:11my album doesn't have 11 songs on it
23:13maybe my album had 25 songs on it
23:15because it's still that listening
23:17experience and it is a new way to tell
23:20stories um in kind of smaller smaller
23:23chunks you know it's like a a book with
23:25three-page chapters yeah it seems like
23:27the delivery mechanism of music has so
23:29much to do with the with the the actual
23:32creative format of music right and so it
23:35strikes me that however we're all
23:37listening to music will probably inform
23:40what people end up wanting to do with
23:42sunno as a tool and so do you imagine a
23:45world in which you know a sunog
23:47generated song is delivered in the same
23:50way that say a Taylor Swift generated
23:52song is and we're sort of listening to
23:54them back to back or they happening in
23:56like completely different context and
23:58experiences I I think it is certainly
24:00possible that you're listening to those
24:01things back to back but there will
24:04certainly be new experiences from this
24:06new art form that uh we are trying to
24:09figure out what they are and I think it
24:12would be maybe a little bit reductive of
24:13us to think it has to be all one or all
24:15the other um you know like one thing we
24:19say is
24:21um we are almost indifferent as to what
24:25you do with the songs that you create
24:27you you the songs that you create witho
24:29if you want to put them um on YouTube
24:32that's good we're not going to we're not
24:33going to try to prevent you from doing
24:35that and we're not going to try to take
24:36a cut if you make a lot of money but
24:38we're basically just as happy if you
24:40shared that song with your three College
24:42roommates and you all got a good laugh
24:44out of it and then that's where the song
24:46lives forever and ever so um and again I
24:50think there's a lot in between Spotify
24:53and that three-person group text message
24:55it's interesting to think about where
24:58this all could be going I mean maybe
25:00just taking like image models as a
25:02comparison um it feels like the
25:05creativity and almost the art form of
25:07making an image will naturally change
25:10like the experience of making that will
25:11change as the models get faster and
25:14faster right now I go into mid journey I
25:17type in a prompt I get four of them at
25:19low resolution um you know I think that
25:23experience has then inspires the way
25:26that I create for that medium and I
25:28imagine as it gets faster and faster I
25:30probably start to create differently
25:33right and maybe in a more Dynamic way
25:35now going back toso and applying it toso
25:37I imagine as your models and your your
25:39experience gets faster it will also
25:42impact the creative experience but it
25:44might impact the consumption experience
25:45as well right I mean can we imagine a
25:47world where music is almost dynamically
25:50being created on the Fly for our own
25:52personal
25:54tastes 100% And I think um there's you
25:59know right now uh music gets streamed to
26:02you on the Fly and there's a really
26:04interesting Discovery problem of
26:06figuring out what music out there is
26:08going to be most in line with my weirdo
26:12tastes in music in other modalities like
26:14basically user generated content makes
26:16for increasingly small smaller and
26:18smaller niches that really appeal to
26:20people and ultimately what generative AI
26:22will let you do is let you create that
26:25really really Micro Niche that has only
26:27one person who really really likes it uh
26:29and you can't support a whole industry
26:32just for that one person but generative
26:33AI can make you stuff on the Fly
26:35tailored to your exact tastes and can
26:38keep learning from the things that you
26:39tell it to make that music better and
26:41better so I think that is yet another
26:43experience in music that um doesn't
26:46exist yet today that we are excited
26:48about eventually being able to tackle so
26:51we we talked about how the delivery
26:53mechanism inspires the creative process
26:56but it almost feels like like based on
26:58what you're saying that the creative
27:00process could Inspire the delivery
27:02mechanism in the future if music is so
27:04fundamentally easy to create in this
27:06near near future and dynamic and
27:09personalized maybe we don't listen to
27:11songs the way we do now right where we
27:12just listen to the three minute or even
27:14the one minute song and then skip to the
27:15next one right it could be something
27:17else completely
27:19entirely 100% I think the B one of the
27:21big unknowns here I we don't we I'm I'm
27:24100% sure you're right and I'm also 100%
27:26sure I don't know the answer to exactly
27:28what it looks like too um I think one of
27:31the biggest question marks for us is
27:33ultimately what do the sharing Dynamics
27:35and the social dynamics look like here
27:38and so just to just to be clear about
27:39that you know right now when we stream
27:41music there's a bunch of artists who
27:43make music the rest of us just passively
27:46listen to it we stream it it's like very
27:48unidirectional um and there is some
27:50connection between artists and fans but
27:52it's it's it's somewhat cursory and then
27:55there are and I would say that kind of
27:57resembles let's say the the highend of
28:00Instagram where you have people spending
28:02six figures making posts and they have a
28:05lot of followers and they make their
28:06livelihoods like that but there's lots
28:08of things that are missing in music that
28:10exist in other modalities whether it's
28:11the social graph that exists um on
28:14Facebook or whether it's like that tail
28:16end of Instagram where it's like someone
28:19with a locked account with 17 followers
28:22posting pictures of their kids and that
28:24doesn't exist in music yet today and
28:26that can exist in music
28:28and the exact nature of how these things
28:30get shared and liked and remixed and
28:33used for uh additional creative
28:35inspiration we don't know those details
28:37but they're going to matter a lot in how
28:39these things ultimately get distributed
28:41yeah it's fascinating um so what's gone
28:44on so so far since you shifted to music
28:47there's a Discord there's a website
28:49believe you also had a really exciting
28:50partnership with Microsoft that you
28:52announced recently maybe talk us through
28:54some of the highlights over the past
28:55couple of months yeah um so we started
28:58on Discord and it was an amazing way you
29:00know we had a Discord from our open
29:01source uh work and it was an amazing way
29:04to get our stuff out to the community
29:06and and and see how people liked it um
29:09in November we um released our first
29:12non- Discord experience uh uh web app
29:15and and people like that even more and I
29:17think since then yeah it's been it's
29:18been super exciting so the the
29:20partnership with Microsoft is is is
29:22great they um they've integrated our
29:25kind of free tier into co-pilot which is
29:27a set of lots of different creative
29:29experiences and and just like you may
29:31want to make an image uh to uh as an
29:35outlet or for any other creative reason
29:37you may want to do the same with a song
29:39how did that come together I mean that's
29:40that's huge that's huge it's it's funny
29:43I I think um I I've heard at least
29:45rumors that somebody had shown a the
29:48very thin web app to Satya over
29:50Thanksgiving and he played with it and
29:52he really loved it so obviously um we
29:55were we were pretty stoked to have the
29:57opport Unity
29:58to get uh our songs into Microsoft
30:01co-pilot eventually that is a an amazing
30:05way to spread the word about how
30:06enjoyable making music really is uh you
30:09know we've just uh announced a the
30:11little Valentine's Day experience um I
30:14think music has is so important when we
30:17think about let's say soundtracking our
30:19lives and these different experiences
30:21that we can bring to people that are
30:22tailored to to for example Valentine's
30:25Day and how how do we get people making
30:27songs for one another and spreading love
30:29and spreading Joy um really really
30:31exciting people people seem to like it a
30:33lot um so yeah I don't know it's it's
30:36been been a a a really fun a couple
30:39months um and we build a product that
30:41makes people
30:42smile speaking of product you know so
30:45much of your background has been in um
30:47machine learning engineering research
30:49now in this role leading sunno uh I
30:52imagine you're spending so much of your
30:53time on product designing product
30:56thinking about the product experience
30:58how how do how do you approach Building
31:00Product atso um to connect users with
31:03with this model that you've developed
31:05yeah I'll caveat this with I don't think
31:07I'm terribly good at product but there's
31:08certainly people here who who are I
31:10think there there's a few important
31:12principles that that we that we think
31:14about um I'd say the first is uh one
31:18thing we say is like Aesthetics matter
31:20uh which is to say it's very easy with a
31:22machine learning background to just be
31:24super obsessed with whatever
31:25quantitative metrics you have about how
31:27good your models are and ultimately all
31:29of these quantitative metrics are are
31:31flawed and they're especially flawed in
31:33in music just because they're not very
31:35mature and ultimately like these are the
31:38best judges of what's going on and so
31:39you need to listen to the stuff that is
31:42coming out of your model and that is
31:43like a big uh driving ethos both for the
31:46product and both for and and the whole
31:48company um another big thing that that
31:51we think about is just like what are the
31:53workflows that people want to do that
31:56are intuitive for for people to make
31:58music and it can be tough actually with
32:00a lot of um people at the company who
32:03really love music to
32:06um it it to to tend to make things that
32:09resemble the workflows that
32:11professionals like to use to make music
32:13and you know one thing like we always
32:15tell ourselves like my mom does not want
32:17to make music the same way a
32:18professional producer wants to make
32:20music and for example I think once
32:22you've once you've talked about
32:24something like I want to stem this out
32:25and I want to take that kick drum and I
32:27want to take out the low end like I
32:29promise you that's not how my mom thinks
32:31gone too far yeah it's gone too far and
32:32so um thinking about it very much from
32:36the perspective of here's somebody with
32:39very specific tastes in music who's
32:41never produced a bit of music in their
32:42lives how do they intuitively think
32:45about this process and um either
32:48fortunately or unfortunately it means
32:50that we end up building things that are
32:52often not suited for professional
32:54musicians and it's just kind of not the
32:56goal right now the last thing I'll say
32:58is that
33:00uh it is also very easy uh with like a
33:04lot of academics to think that you can
33:07just first principles reason your way
33:09toward what is the best experience here
33:11and um I think this is a this is an
33:14empirical science where um let's say
33:17because we have backgrounds in music we
33:18actually can't intuitive the way novices
33:21want to use it and so we need to run a
33:24lot of experiments and we need to ship a
33:26lot of features and we need to see how
33:28people like them and then we can kind of
33:30iterate our way
33:32there speaking of product and the
33:34product experience you talked earlier
33:36about how now it's you know you can you
33:38can bring your own lyrics you can just
33:40describe it but it sounds like it's
33:41mostly coming from you know Tech a text
33:44to song approach as you think about the
33:47products Evolution what are the other
33:48ways in which you might enable people to
33:51create music outside of just text yeah
33:54it's a really good question I'll say
33:55before I answer it I'll just say like uh
33:57I'll be unkind to machine learning
33:59people because I am a machine learning
34:00person like you know people will think
34:02like oh this is a text to music model
34:04and I think that is an extremely
34:06non-user Centric way of thinking about
34:08what we're doing and if all you can do
34:10is think about A to B text to music or
34:13tapping on the table to music or it's
34:15like um we will never build something
34:18people want to use and so we really try
34:19to think hard about it from the other
34:21end and think about like what inspires
34:23people to want to make music and how can
34:25we get them to express that and so yes
34:28describing stuff is great but not
34:30everybody is in the habit of writing
34:31lyrics maybe there are other ways of
34:34describing things whether that's tapping
34:36your pencil on the table and recording
34:39that um or maybe you sang into your
34:42microphone and for some inspiration um I
34:46think maybe you can come with your own
34:47samples of music and kind of describe
34:49why you like them and and start that as
34:51a process kind of like you might do mood
34:53boarding in Pinterest um there's lots of
34:56stuff there I think I think um one of
34:58the workflows that we are really into is
35:01what we call soundtracking your life and
35:02it's like what are all of the random
35:04things that are happening to you today
35:06and how are you going to kind of show
35:07those to a model as inspiration for the
35:10sounds that are in your head um and
35:14maybe it's a really loud car horn you
35:16know like whatever it is um so we try to
35:19keep a pretty open mind there um we've
35:21got a lot of stuff coming that I I don't
35:23want to that I that I don't want to
35:25tease out just yet but I think
35:27um I think we are very cognizant of the
35:31fact that text to music is is going to
35:33be
35:34limiting it's really really cool and
35:36then on the output side you know you've
35:38you've clearly made some very
35:39intentional choices um obviously in
35:42terms of the quality which is phenomenal
35:45um but also on the limitations right I I
35:48believe uh you know you make it very
35:50difficult if not impossible for anyone
35:52to make anything that may be infringing
35:54of of existing artist rights maybe talk
35:56a little a little bit about that we
35:58always said we were going to do audio
36:00the right way and techn technically that
36:02meant um kind of the foundation model
36:04approach and then you know ethically and
36:06and legally that means um not uh trying
36:11to infringe on um existing artists and
36:14trying to do this in a way that is
36:16artist friendly and this is not only
36:18because it's ethical and legal and moral
36:20it's because it's also what we think is
36:22the future of how people want to do
36:24music it's that other 4950 that we were
36:27talking about before of people aren't
36:29going to lose their connections to
36:30artists um but there's just a lot of
36:33other experiences that we can do and so
36:35um we don't let you say make me a Taylor
36:38Swift song um actually our models don't
36:41even know who Taylor Swift is but
36:43instead of actually making something up
36:46that's just not um we try to gently
36:50nudge users into the behavior that we
36:52think is the long lasting one which is
36:54making the original music that's in your
36:55head um and the same for other people's
36:58existing lyrics
37:00and um one thing that you know is
37:02actually nice for us is there's a lot of
37:04these cover type things where you know
37:06maybe you could have uh Taylor Swift
37:08doing Enter Sandman by Metallica And I
37:12think those tend to go very viral and I
37:14think that is really not the future uh
37:18of how people want to do music and so
37:19we're very happy to kind of let those
37:21things go viral and then kind of um
37:24evaporate somewhat quickly and I think
37:26the analogy that I have not with your
37:28platform exactly exactly so you can't do
37:30that with our platform you can do that
37:31with someone else's it is almost
37:32certainly illegal um it is very viral
37:35and I don't think that is the long
37:36lasting use case I think the the analogy
37:38that I have in my head for for making
37:40covers for Taylor Swift covering
37:42interent Sandman is uh the first time
37:45you played with GPT and you made a
37:46Shakespeare sonnet about drinking coffee
37:49and then you made another one and then
37:50the third time you were like this is
37:52cute but this is not what I want to do
37:54and that doesn't mean that GPT isn't
37:56extremely cool and useful because it is
37:59it's just not for writing parody
38:01Shakespeare sonnets the comparison to
38:03GPT makes me think you know one of the
38:05one of the things that people knock it
38:06about is that you know it it it sort of
38:10when you try to get it to do things that
38:11are really creative it sort of hits this
38:14kind of upper bounds and maybe that's
38:16because it's you know it's a it's train
38:19you know it's training on the world's
38:20knowledge so it sort of it it bumps up
38:23against the limits of that knowledge and
38:25that information um thinking about music
38:28though music is obviously so creative
38:30and if you think about like the greatest
38:31artists ever so many of them became the
38:35greatest artist ever because they did
38:37something that no one had ever done
38:38before right um and so do you imagine a
38:42world in which sunno and and maybe just
38:44music models in general can do things
38:46that have never been done before or does
38:50it is it always going to kind of hit up
38:51against the upper limits of of
38:53everything that came before yeah I I
38:55think we can make make music that is
38:57kind of above the the limits of what
38:59we've seen right now um and I think that
39:01for a few reasons one is you know humans
39:03keep making music that is above the the
39:05limits that humans continue to see and
39:07so they are you know standing on the
39:10shoulders of giants you get inspiration
39:12from everything not just from other
39:13musicians but you get inspiration from
39:14the car horn that that honked outside of
39:17your window um and uh there's a lot of
39:22like uh new ways that humans can be
39:24inspired by these models as well so am I
39:27using autotune when I record my album
39:29like that did something that was
39:31impossible for a human to do to sing
39:33that perfectly and then the tool let me
39:35do it or that I use an effects pedal on
39:37my guitar that was impossible to pull
39:39out of my amp but that thing did it and
39:41so I think this is kind of a natural
39:44progression and I don't know where the
39:45line of which constituted Ai and which
39:47was not but um I I think that's kind of
39:50always been a part of this and I'll tell
39:53you one thing that I'm actually pretty
39:54excited about um if if you think about
39:57the progression of Music um more
40:02recently progress in music looks like in
40:04like things that are sonically
40:05interesting so like interesting sounds
40:07but not necessarily more interesting
40:09chord changes um and I think uh AI has
40:14the potential to kind of bring that back
40:16where you do more interesting stuff
40:18melodically and harmonically it also
40:21obviously sonically but um I think uh it
40:24is a way to like
40:26it is a way for humans to express the
40:29sounds that are in their heads and
40:31sometimes uh that there's a block um
40:34that kind of artificially sets the bar
40:35for humans to love you know one of the
40:37things that's come up on on this podcast
40:39a couple of times is this notion of of
40:42training and training on the entirety of
40:44the internet um definitely feels like
40:46it's a little bit of a wild west right
40:48now um you know the the open AI New York
40:51Times case comes to mind um and
40:53everything you know everything that's
40:54being discussed there like how do you
40:56think this shakes out are there changes
40:58coming and what will that mean for the
41:00future of these large foundational
41:03models yeah I I think there definitely
41:05are uh changes that are going to come I
41:08think some will be across the modalities
41:11uh others may be modality specific I
41:13think um music is particularly
41:15interesting because there are just much
41:17more established uh ways of uh
41:21interacting with rights holders and
41:24um for just for example on the on the um
41:29let's take that New York Times open AI
41:31lawsuit where uh the New York Times is
41:33upset that you can get an entire New
41:35York Times article at a GPT with the
41:37right prompt um there are you know if I
41:41have a restaurant and I stream some song
41:43there are like very obvious ways that
41:46are established that I can um compensate
41:49the owners of that songs the master and
41:51the songwriter and that doesn't exist in
41:53text and so I think it's not going to be
41:55blanket solution that apply across the
41:57whole industry I think some things will
41:59and some things won't um the thing
42:01that's going to be really tricky here I
42:03think is actually uh geographical
42:05distance uh geographical differences
42:07between um let's say different countries
42:10and um I I'm honestly a little bit
42:12worried about how things will shake out
42:14is is open AI going to have to have
42:16different models in the EU and in the US
42:19and stuff like that and just um that
42:22that make it very difficult and kind of
42:24prone to to I don't know hacking and
42:27people are going to be using like vpns
42:28to access different countries models and
42:30stuff like that
42:33um I think it's really really early I
42:36think people think this is going to get
42:38sorted out in the next six months and I
42:40don't even think the open AI the open AI
42:42lawsuit won't get sorted out in the next
42:43six months but that won't even sort
42:44everything out for the the whole
42:46industry so you know for us as sunno
42:48especially with music being somewhat far
42:49farther behind images and text we watch
42:52this stuff very closely you know we we
42:54uh try to always do the legal and moral
42:58and ethical thing and these things
42:59aren't always the same something that's
43:01legal may still be not artist friendly
43:03the thing that we stress is like it's
43:04like super early and uh I don't think
43:06there are any scenarios that are amazing
43:09for us or terrible for us that I can
43:12that I can foresee in the next year it
43:15seems like open AI um is is you know
43:17doing licensing deals with uh with media
43:23publishers and you have to imagine a
43:25world in which those Med Publishers
43:26obviously then benefit in some way maybe
43:28you know maybe it helps Drive
43:30subscriptions or I don't know maybe
43:32there's a new version of these models
43:33where you know you can use certain ones
43:35that that Output New York Times and
43:37certain ones that don't do you do you
43:39imagine similar types of deals happening
43:41in music can you can you imagine a world
43:43in which you know I know we keep
43:45mentioning Taylor Swift the Taylor
43:47Swifts of the world are collaborating
43:49directly with music models um to create
43:53you know generative Taylor Swift music
43:56yeah 100% so you know Google is is
43:58starting to do this with their Lia
44:00project and they have a few big artists
44:02um on board there but you know if we
44:05think about it like let's fast forward a
44:07few years and the uh licensing um uh
44:13climate is a little less uncertain and
44:16maybe we can let you prompt the model
44:17with a Taylor Swift song and like this
44:19was the big inspiration and there's
44:21something that is akin to the way people
44:23pay out for sampling now but it's
44:24obviously a little bit different
44:26um and then all you had to do Mike when
44:29you wanted to make a Taylor Swift
44:30inspired song was like pay for that
44:33sample and I don't know how much it will
44:34be and I don't know how much will
44:35actually go to Taylor Swift but like we
44:37will be able to let you do that and we
44:39can actually do that right now we just
44:40can't let you do that because we don't
44:42know how to pay Taylor Swift for it
44:44right right so it's technically possible
44:46it's just not um the infrastructure for
44:50that from a royalty perspective is not
44:51yet exact exactly what's next for sunno
44:54where where where where do you go from
44:56here what what are you looking at over
44:57the next couple of quarters yeah uh a
44:59lot of exciting stuff so um we've got a
45:02new model that we're going to release
45:03soon um it is kind of better in all of
45:06the ways that we think about so we're
45:07really really excited about it just um
45:09can go Tactical for a second when we
45:11think about models qualities we think
45:14about audio Fidelity like does it sound
45:15like it was crisply recorded with uh
45:18beautiful Hardware um we think about
45:20song quality is it catchy does it make
45:22me feel um and we think about
45:24controllability when I asked for
45:25something does that something come out
45:27um and because music is still so early
45:30it's it's we're still making gains
45:31across all of these so really really
45:34exciting um lots of new ways that we
45:37want people to interact with stuff so um
45:40whether that is different ways of
45:41prompting the model whether that's
45:43different interfaces um into our product
45:45we're like really excited about all of
45:48those um uh yeah those are those are the
45:52big ones uh there there's a couple of
45:53other secret things coming down the pike
45:55that that that we're not ready to talk
45:56about just yet but I think you know the
45:58the future the future of music is really
46:00really big and I think you know people
46:02think a lot about how streaming almost
46:04killed the music industry uh and people
46:07think that AI will almost kill the music
46:09industry and it's I think it's really
46:10really the opposite I think we we are
46:12quite confident that there is another
46:134950 it'ss of music that um we among
46:17others are going to try to
46:19uncover uh always got to ask is sunno
46:21hiring we're always hiring uh um so
46:25we're in we're in Cambridge
46:26Massachusetts um kind of a a fun place
46:29uh for for uh building companies it's
46:32something we've done here before um
46:34always looking for um the best talent
46:37across software and machine learning and
46:39product uh and design and music if you
46:42uh really love building stuff and you
46:44really love music this is probably a
46:46good home for you Mikey thank you so
46:48much this has been so much fun we really
46:50appreciate all the time awesome thanks
46:51so much this was this was great happy to
46:53happy to happy to be here let's do it
46:55again sometimes see you thanks
46:57Mike thank you so much for listening to
46:59generative now if you liked what you
47:01heard please rate and review the episode
47:03that really does help and of course
47:05subscribe to the podcast on platforms
47:07like Spotify YouTube and apple podcasts
47:10if you'd like to learn more you can
47:12follow us at Lightspeed VP on YouTube
47:15Twitter LinkedIn Instagram and
47:17generative now is produced by Lightspeed
47:19in partnership with pod people I am
47:21Michael mcnano and we will be back next
47:23week with another awesome conversation
47:24thank you so much
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