How Apple Influenced The Labels To Shut Down My Music Streaming Startup

Vectorspace AI
May 6, 2015 · 16 min read

We had 50M monthly active users and 250M searches every month. We powered Spotify and Steve Jobs told Warner Music, EMI and other labels to sue us. So they did and for $40B. How do I know? I know because Steve Jobs gave them a list of music streaming sites that were competitive threats to iTunes, and told them to take care of it. This was the answer I was given by the CTO of Warner after I asked why he thought they were suing us, as I sat in his office in NY while our attorneys hashed a few things out.

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The iPhone was released in July of 2007 and SeeqPod was one of the first web apps to be available and popularized within that ecosystem. In time, we were known as the “iTunes Killer” — SeeqPod was a ‘Playable Search, Discovery & Recommendation Engine’.

Every month, 50 million consumers and hundreds of API ecosystem companies and individuals including Spotify and Grooveshark, would execute a total of 250 million searches on the SeeqPod platform for the purpose of streaming playable content.

Our first contact with Apple was via litigation initiated on their part regarding our company name, SeeqPod. Apple claimed trademark infringement based on the characters ‘p’, ‘o’ and ‘d’, making up ‘pod’ in our name SeeqPod. This lasted for about 6 months after which we both decided to walk away from any further litigation in this area. We then received a DMCA take down request for about 3000 URLs in our index from the RIAA. At the time we had a targeted crawling system bringing in millions per day.

It was January of 2008 and I was in a taxi with one of my co-founders and investors in New York on the way to meet a group of rainmaker attorneys who were interested in discussing a strategic partnership and M&A event worth north of $100 million for a large part of SeeqPod. While on my way, I looked down at my iPhone to scan my custom built news filters, as I often did, and found that the New York Post published a headline which read: “SeeqPod sued by Warner Music”. This happened minutes before our meeting. At that point I realized the context surrounding our company had shifted, the rug had been pulled from under us and was replaced with a different kind of not-so-magic carpet.

We entered the meeting and stood around the table with these deal makers like it was Bill Carson’s grave at the Sad Hill Cemetery in the movie The Good, the Bad and the Ugly.

We did not ask if they heard the news but they immediately brought it up. They said: “You've finally arrived!” This was true. We were a formidable threat to iTunes and now the major record labels. Needless to say, the negotiation of this deal was placed in a completely different light with different measurable outcomes. No one was prepared for this but this brand of uncertain opportunity is what running a startup is all about. There was no time to pivot or adapt. We simply and collectively made a discovery that needed analysis to determine its value. After some futile banter and greedy disagreements on valuation we headed back to the Bay Area.

A few weeks later the head of Sony M&A called our office and asked to speak with me. He told me Sony had lost the war in the portable music player space with a device called the Walkman. I said I got wind of this. He mentioned they’d like to compete against iTunes using the SeeqPod platform. I said, deal. As the talks progressed, we both knew the current litigation would need to take a direction toward a resolution. So they waited and then in April of 2008, Bear Stearns fell. My mom had spent quite a bit of time in the banking and investment banking industries while at Citi and Montgomery Securities. I also spent time through the years developing algorithms and predictive analytics associated to uncovering patterns in historical stock market data. Even with this knowledge base I failed to pay attention to what I knew was a deep systematic fissure in the financial markets signaled by the fall of Bear Stearns. I was focused on user acquisition, growth and resolving litigation as opposed to revenue, a mistake when the Black Swan appears.

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SeeqPod was burning $650k per month, $150k per month on litigation alone. During this time I was back and forth between Silicon Valley and New York 2 or 3 times per month. Most of the travel involved meetings with partners and Warner Music. This is where I learned where the root of our litigation and contention with music labels stemmed. One day I found myself outlining a proposal to Warner Music executives along with their CTO, Howie Singer, that entailed surrounding streaming music with transactions that would generate revenue for artists, labels and technology companies, namely ours. We took few breaks. During one of these breaks I was by myself in the office of the CTO. He came in and we chatted about the state of technology and music. I then thought to ask a question of which the answer would shed light on the real reason behind the litigation and contention technology companies share with old media companies. I asked Howie, why is it that Warner chose to sue SeeqPod? He said that they maintained a short list of companies and we were on top of that list. I then asked how we wound up on the list and at the top. This is when he mentioned that Steve Jobs had held meetings with several labels including Warner and disagreed with streaming music services like SeeqPod and that companies like ours would inhibit a deal between Apple and the labels, so they collectively decided to keep a running list of “problem” companies that need to be “addressed” by Warner. It was at this point I realized we were not in a fight with the labels but in a proxy war waged by Apple against innovative music technology companies.

I came back to the Bay Area a bit more enlightened knowing that with the army of attorneys we had, who were top notch and heroic in the fight, we would likely be spending another $1.8 million on winning a judgement in our favor based on Free Speech and the DMCA. All things considered, it would be a large Pyrrhic victory.

I halted most of our partnership talks with the likes of InfoSpace, Spotify’s early team, the new kid on the block, Grooveshark, Songza, Baidu and host of others as it would not have been a good idea to drag them onto the current battlefield at such an early stage.

We continued our talks with Sony while the economy continued to collapse. Then Lehman Brothers fell. As soon as this happened I knew we had to prepare for the worst. Advertising fell off a cliff by 80–90% and we had to support 50 million monthly active users along with streaming bandwidth costs associated to our targeted crawling systems, content caching techniques, 35 employees and about 20 attorneys. Eventually, being overexposed in the Markets, our lead investment firm had to walk. Private companies around us began suffering downrounds, clawbacks and were being excised from VC portfolios left and right. Banks began to freeze their lending, large funds began to suffer losses, Limited Partners were freezing VC access to capital and the Founders and CEOs had private companies that were turning to dust. Interestingly, although many public companies lost significant value, their exposure to the public markets allowed them to recover. At any rate, there wasn’t any way we were going to continue to operate like we were before.

It was then that EMI came forward and attempted to sue me personally as founder and CEO of SeeqPod to the tune of about $40 Billion. That is, $120k per search. We had 250 million searches happening per month and they considered a single search by a user as a copyright infringement with the maximum infringement fine being $120k per alleged infringement. They included my co-founders and then included a lone software engineer that built an app powered by our API. This is when we retained additional legal counsel in New York, utilized our grit and wit, and got it thrown out of court.

We decided to wind SeeqPod down at this point and begin negotiations with Sony and a few other suitors. Engaging in M&A discussions like these in the middle of the worst economic downturn in US history since the great depression turned out to be an experience like no other. We had a team which had proven its ability to work and grow together, a team that proved its ability to create a hard-to-duplicate, formidable algorithmic search and discovery technology platform loved by consumers, with the prospect of changing the way transactions associated to streaming content would benefit the artists. Despite all of this, we knew we had to wind it down. This came in the form of Sony introducing us to InterTrust, a former joint venture between Sony and Philips, for the purpose of acquiring the patents and other IP.

Other companies included in this swath of litigation against startups that enabled the streaming of content included imeem, project playlist and eventually Grooveshark near the tail end of the SeeqPod battle.

We called it day and lived to tell about it. As a team, we are taking our experience and working on algorithms, art and technology that is even harder to duplicate and with an even larger impact in terms of advancing innovation and business in the area of Artificial Intelligence, Data Science, Hidden Relationship Engines and Recommendation Systems.

Methodical excision of competition is ok until it begins to stifle innovation made by startups and individuals along with forcing creatives and consumers into a single funnel of high costs, limited returns and limited access to competing products. Being forced to conform to a synthetic garden of ideology controlled by a single organization will never be the way forward.

Our interests will always remain in the area of advancing the art and science of living.

SeeqPod had its beginnings as a genomic search and discovery engine that would help researchers in Lawrence Berkeley National Laboratory’s Life Sciences division find hidden connections between genes, genomic pathways, diseases and therapeutics. We were analyzing data related to genes that would extend the lifespan of nematodes ( and genes and pathways associated to breast cancer. Our algorithm sets and technology were rooted in Artificial Intelligence and based on mimicking portions of human cognition. We based a lot of our work on Computational Theory of the Mind (CTM). We were fans of Steven Pinker, Jeff Hawkins and of course those we worked with at Berkeley Lab. It was quite effective and we worked with Berkeley Lab to patent the technology —System and method for generating a relationship network — K Franks, CA Myers, RM Podowski — US Patent 7,987,191, 2011 — — In 2008 we won the R&D 100 award for this approach — and asked by Dr. Steven Chu, winner of the Nobel Prize in Physics, Director of Berkeley Lab and former US Energy Secretary, to advise on what it takes to move technology from academia to industry.

One day we decided to apply the system to finding hidden connections between public companies. When we had this part of the technology ready we tested it. In September of 2004 Merck dropped by about 21% due to the Vioxx debacle. We asked the system to give us public companies that had hidden connections related to the term “vioxx”. The top result was Pfizer (NYSE:PFE). 8 weeks later Pfizer dropped by about 14% based on hidden connections related to Vioxx such as COX-2 and Coxib inhibitors among many other things in their pharmaceutical pipeline.

At this point we knew we had an interesting and competitive technology that could wrangle vertical search and recommendation markets away from some of the larger players.

After some 80’s-style phracking while in high school, I worked as a DB admin for the Symantec ‘Q & A’ natural language query database system that my mom ran for her startup in 1987 when she took a break from the investment banking industry. Then I began as a systems administrator on SCO UNIX running 4GL Informix in 1991 mentored by Gil Lee. After that I started writing search engines, parsers, language preprocessors, modified code snippets from 2600 and building Linux Slackware and Yggdrasil kernels from the root and boot 1.44”’s in 1992. It was eventually easier to pick up CD’s if I needed extra libs or install packages and if I was up for a drive to Sonoma from Berkeley from time to time. If I could compile and debug kernel issues along with X11 X86Config files with assistance from a good friend at the QNX-based Caprica Internet in LA and the 2-person Slackware “helpdesk” on the East Coast every few days then I’d be able to continue to test NCSA webserver over SLIP and PPP to deliver electronic faxes with a web interface for the Mosaic Browser for a sugar company in Marin County to save them a few thousand a month in fax fees to China. I then began putting webservers online in 1993 when there were only a few thousand webservers in existence. I was doing this while building pattern matchers and preprocessors on the QNX OS at Genentech for data related to the new breast cancer drug HER2 at the time. I worked and was mentored under Joe Seiwert, an extraordinary engineer and scientist who was deeply in love with his NeXT boxes at the time.

I moved to Sun Microsystems within their Menlo Park MPK campus and began working in their SysAdmin and Network Security Group building tools to analyze data among other things. I then moved to Sun Micro-Electronics (SME) and that’s where I learned one of my most valuable lessons. I began to build and ship the first HTTP-based (yes, this was a thing back then) stock ticker and called it DigitalTrader in 1996 ( It was built in Tcl/Tk and Java/Tk. It auto-updated itself and crawled and Yahoo for data. Six months later a competitor duplicated it and Yahoo bought them and put us out of business overnight. Lesson learned: realize when you are doing something that is easily duplicated. After that, I made the decision to stay in the camp of only doing things that are not easily duplicated, the camp of invention and true algorithmic, scientific or technological innovation. Business innovation is different and it’s good to recognize this as well.

After that I spent some time at Oracle, Cisco, General Instrument, AirTouch/Vodafone, TiVo, mPower(Morningstar), X-Mine (genomics, bioinformatics) and consulted for Life Sciences companies like Astra Zeneca before moving to Berkeley Lab. Most of what I worked on fell under the umbrella of Data Mining and Knowledge Discovery which is what we’re calling Data Science today.

I wanted to apply the approach to something outside of Genomics and publicly traded companies so decided to acquire an exclusive license to the patent. I thought because I was the lead inventor it would just be given to me. Not so. I had to purchase a licensing agreement for what could be up to a few hundred thousand dollars. This is when I knew I needed to start a company to raise funding from angels or VC’s to do so. I also knew that for the first time in my life I would have to apply everything I knew and learned in Silicon Valley as an algorithm, product and software engineer over the years.

It was 2005 and my term at Berkeley Lab was up and I was doing a pitch per week for fundraising. I had an executive summary, a few decks and business plan which seemed to change with the pace of lava flowing out of a volcano in creating a new island.

The Day I found Investors

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I had a few firms and angels show interest but most were lukewarm when it came to understanding how we wanted to compete in the Search space based on mimicking the process of human cognition to help form new hypotheses and make discoveries in Life Sciences.

I was then introduced to an angel investor that got it. We met semi-informally and I consider these the best kind of meetings. We met at a friend of a friend’s house. We had 2 degrees of separation. I described the vision and the opportunity. I then described the problem and solution and then the team and our history together. He asked if he could call me tomorrow to discuss further. Early the next morning I got a call and proceeded to answer a few more questions and then the deal was done. He offered to write the first in a series of checks to get us started. We received the first check a few weeks later and then a check every month. We began to seek a large syndicated angel raise after that. This resulted in a year-long pitch per week in which we raised $7.4 million from smart angels to execute on the world’s first playable search, discovery and recommendation engine.

There was one afternoon Mike Muldoon, former SeeqPod CTO, and I were driving to one of our meetings and before we had any kind of office. At that time a large portion of the business plan included using the search technology to relevantly match ads to search results in addition to finding hidden connections between genes and drugs. It was on this day that we first came up with the idea of analyzing audio, including music, using frequency and amplitude data, and on the car ride home, Mike came up with the idea to analyze playlists. Caleb Pate, an old friend of mine and musician, decided to map out our media and art strategy.

That one bit, beyond all the truly awesome engineering we did, really captured a magic moment. It is examining the world for connections and patterns, and finding ways to leverage that information for the purpose of discovery that really turned us on and tuned us in.

Some of our advisory board members were investors. One advisory board member, Daiwa Quantum Capital, run by former Sony CEO Nobuyuki Idei, also on the board of Baidu, was responsible for nixing a deal with what could have kept Napster alive. Ref: This relationship also led to China inside Baidu’s offices, but that’s another story.

This was a hard-to-duplicate core algorithm and technology platform designed to crawl the Internet for anything that a consumer might want to play including sounds, audio, games, video, animations related to entertainment, Life Sciences or any other category you could think of.

The founding team members had backgrounds in music. Although I had a background in music as an amateur instrumentalist, I also had a background in the drug delivery business. Music and technology were both therapeutics in my mind. I also knew the inner-workings of search, discovery, recommendation and vector space-based hidden relationship algorithms. Combined with the fact that the top searches happening on the Internet were related to music, SeeqPod became a place where you could stream just about any music you wanted, due to the large targeted crawling system effort coupled with vertical search and recommendation algorithms.

People began to use it to search and stream music more than anything else. We were essentially a drug delivery business that began to disrupt the distribution pipelines of who we thought were Warner and EMI, but turned out to be Apple.

We observed that at the time, MySpace became a successful social network due to its initial focus on music and a place for bands. 30 million email addresses also helped. It’s hard to say if Facebook would even exist today if not for MySpace paving the way there. While Apple was in the dumps, on the verge of bankruptcy and after borrowing $150 million from Bill Gates, the tides really began to shift when Steve Jobs decided to focus on music via the iPod/iTunes model. Moving from RISC to SISC architecture also helped. Interestingly, it was Steve Wozniak that originally held the vision for music and technology via the US Festivals:

“Steve Wozniak, creator of the Apple II, believed that the 1970s were the “Me” generation. He intended the Us Festivals, with Bill Graham’s participation, to encourage the 1980s to be more community-oriented and combine technology with rock music.[1] The first was held Labor Day weekend in September 1982 and the second was Memorial Day weekend in May 1983. Wozniak paid for the bulldozing and construction[2] of a new open-air field venue as well as the construction of an enormous state-of-the-art temporary stage at Glen Helen Regional Park near Devore, San Bernardino, California. (This site was later to become home to Blockbuster Pavilion — now San Manuel Amphitheater — the largest amphitheatre in the United States as of 2007.) The festival stage has resided at Disneyland in Anaheim since 1985, and has operated under various names and functions as the Videopolis dance club, the Videopolis Theatre, and the Fantasyland Theater.” —

While companies were doing quite well using music as a platform to launch new technologies and new revenue models, Google was a company that got left behind in this area. The opportunity for search engine technology combined with music was in clear site. At the time, Google was distracted for good reason and could not understand how to address this opportunity for a few different reasons including the fact that its founders never really listened to music. (The Search, John Battelle 2006 This in hindsight was probably wise as one of the hallmarks of a great product and company is defined by whether or not its founders eat their own dog food or in other words, find their own product supply to be quite useful to them personally and in their everyday lives.

We also knew a little something about the way consumers and listeners use search services. Solving for spelling is a very powerful yet underestimated strategy for almost any company. For example, if users misspell something they are searching for, many users would assume, your engine or platform, simply does not have the result or product and, in turn, is inferior to a competitor. We developed our own in-house vector-space based spellchecker and it worked better than anything else out there at the time so we implemented it. Google was not paying attention to music. We were a core algorithmic vertical search and discovery platform being used to discover and stream content including music. We saw this as an opportunity to take a large bite out of Search via vertical search approaches.

Fast forward to today and our current team is collectively made up of a bunch of founding teams. Many of our original team members, equipped with a unique set of skills and experience, went off to start their own companies or are working on their own hard-to-duplicate projects and strategies.

Advancing innovation involves much more than inventing hard-to-duplicate algorithms and technology. Much of it involves the strategy that you wrap around your innovations to protect them and guide them along the way, enabling them to thrive or spawn new ways of thinking about old ways of doing. This takes a bright and clever team, strong and fabulous, a team I’ll never forget.

—Kasian Franks (


Stories from the startup journey around the world.

Vectorspace AI

Written by

Context-controlled Natural Language Processing (NLP) & Feature Engineering for Hidden Relationship Detection in Data



Stories from the startup journey around the world.

Vectorspace AI

Written by

Context-controlled Natural Language Processing (NLP) & Feature Engineering for Hidden Relationship Detection in Data



Stories from the startup journey around the world.

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