February 2021 - Rise of the Data Cloud by Frank Slootman and Steve Hamm

This month we read a new book by the CEO of Snowflake and author of our November 2020 book, Tape Sucks. The book covers Snowflake’s founding, products, strategy, industry specific solutions and partnerships. Although the content is somewhat interesting, it reads more like a marketing book than an actually useful guide to cloud data warehousing. Nonetheless, its a solid quick read on the state of the data infrastructure ecosystem.

Tech Themes

The Data Warehouse. A data warehouse is a type of database that is optimized for analytics. These optimizations mainly revolve around complex query performance, the ability to handle multiple data types, the ability to integrate data from different applications, and the ability to run fast queries across large data sets. In contrast to a normal database (like Postgres), a data warehouse is purpose-built for efficient retrieval of large data sets and not high performance read/write transactions like a typical relational database. The industry began in the late 1970s and early 80’s, driven by work done by the “Father of Data Warehousing” Bill Inmon and early competitor Ralph Kimball, who was a former Xerox PARC designer. In 1986, Kimball launched Redbrick Systems and Inmon launched Prism Solutions in 1991, with its leading product the Prism Warehouse Manager. Prism went public in 1995 and was acquired by Ardent Software in 1998 for $42M while Red Brick was acquired by Informix for ~$35M in 1998. In the background, a company called Teradata, which was formed in the late 1970s by researchers at Cal and employees from Citibank, was going through their own journey to the data warehouse. Teradata would IPO in 1987, get acquired by NCR in 1991; NCR itself would get acquired by AT&T in 1991; NCR would then spin out of AT&T in 1997, and Teradata would spin out of NCR through IPO in 2007. What a whirlwind of corporate acquisitions! Around that time, other new data warehouses were popping up on the scene including Netezza (launched in 1999) and Vertica (2005). Netezza, Vertica, and Teradata were great solutions but they were physical hardware that ran a highly efficient data warehouse on-premise. The issue was, as data began to grow on the hardware, it became really difficult to add more hardware boxes and to know how to manage queries optimally across the disparate hardware. Snowflake wanted to leverage the unlimited storage and computing power of the cloud to allow for infinitely scalable data warehouses. This was an absolute game-changer as early customer Accordant Media described, “In the first five minutes, I was sold. Cloud-based. Storage separate from compute. Virtual warehouses that can go up and down. I said, ‘That’s what we want!’”
Storage + Compute. Snowflake was launched in 2012 by Benoit Dageville (Oracle), Thierry Cruanes (Oracle) and Marcin Żukowski (Vectorwise). Mike Speiser and Sutter Hill Ventures provided the initial capital to fund the formation of the company. After numerous whiteboarding sessions, the technical founders decided to try something crazy, separating data storage from compute (processing power). This allowed Snowflake’s product to scale the storage (i.e. add more boxes) and put tons of computing power behind very complex queries. What may have been limited by Vertica hardware, was now possible with Snowflake. At this point, the cloud had only been around for about 5 years and unlike today, there were only a few services offered by the main providers. The team took a huge risk to 1) bet on the long-term success of the public cloud providers and 2) try something that had never successfully been accomplished before. When they got it to work, it felt like magic. “One of the early customers was using a $20 million system to do behavioral analysis of online advertising results. Typically, one big analytics job would take about thirty days to complete. When they tried the same job on an early version of Snowflake;’s data warehouse, it took just six minutes. After Mike learned about this, he said to himself: ‘Holy shit, we need to hire a lot of sales people. This product will sell itself.’” This idea was so crazy that not even Amazon (where Snowflake runs) thought of unbundling storage and compute when they built their cloud-native data warehouse, Redshift, in 2013. Funny enough, Amazon also sought to attract people away from Oracle, hence the name Red-Shift. It would take Amazon almost seven years to re-design their data warehouse to separate storage and compute in Redshift RA3 which launched in 2019. On top of these functional benefits, there is a massive gap in the cost of storage and the cost of compute and separating the two made Snowflake a significantly more cost-competitive solution than traditional hardware systems.
The Battle for Data Pipelines. A typical data pipeline (shown below) consists of pulling data from many sources, perform ETL/ELT (extract, load, transform and vice versa), centralizing it in a data warehouse or data lake, and connecting that data to visualization tools like Tableau or Looker. All parts of this data stack are facing intense competition. On the ETL/ELT side, you have companies like Fivetran and Matillion and on the data warehouse/data lake side you have Snowflake and Databricks. Fivetran focuses on the extract and load portion of ETL, providing a data integration tool that allows you to connect to all of your operational systems (salesforce, zendesk, workday, etc.) and pull them all together in Snowflake for comprehensive analysis. Matillion is similar, except it connects to your systems and imports raw data into Snowflake, and then transforms it (checking for NULL’s, ensuring matching records, removing blanks) in your Snowflake data warehouse. Matillion thus focuses on the load and transform steps in ETL while Fivetran focuses on the extract and load portions and leverages dbt (data build tool) to do transformations. The data warehouse vs. data lake debate is a complex and highly technical discussion but it mainly comes down to Databricks vs. Snowflake. Databricks is primarily a Machine Learning platform that allows you to run Apache Spark (an open-source ML framework) at scale. Databricks’s main product, Delta Lake allows you to store all data types - structured and unstructured for real-time and complex analytical processes. As Datagrom points out here, the platforms come down to three differences: data structure, data ownership, and use case versatility. Snowflake requires structured or semi-structured data prior to running a query while Databricks does not. Similarly, while Snowflake decouples data storage from compute, it does not decouple data ownership meaning Snowflake maintains all of your data, whereas you can run Databricks on top of any data source you have whether it be on-premise or in the cloud. Lastly, Databricks acts more as a processing layer (able to function in code like python as well as SQL) while Snowflake acts as a query and storage layer (mainly driven by SQL). Snowflake performs best with business intelligence querying while Databricks performs best with data science and machine learning. Both platforms can be used by the same organizations and I expect both to be massive companies (Databricks recently raised at a $28B valuation!). All of these tools are blending together and competing against each other - Databricks just launched a new LakeHouse (Data lake + data warehouse - I know the name is hilarious) and Snowflake is leaning heavily into its data lake. We will see who wins!

An interesting data platform battle is brewing that will play out over the next 5-10 years: The Data Warehouse vs the Data Lakehouse, and the race to create the data cloud

Who's the biggest threat to @snowflake? I think it's @databricks, not AWS Redshifthttps://t.co/R2b77XPXB7
— Jamin Ball (@jaminball) January 26, 2021

Business Themes

Marketing Customers. This book at its core, is a marketing document. Sure, it gives a nice story of how the company was built, the insights of its founding team, and some obstacles they overcame. But the majority of the book is just a “Imagine what you could do with data” exploration across a variety of industries and use cases. Its not good or bad, but its an interesting way of marketing - that’s for sure. Its annoying they spent so little on the technology and actual company building. Our May 2019 book, The Everything Store, about Jeff Bezos and Amazon was perfect because it covered all of the decision making and challenging moments to build a long-term company. This book just talks about customer and partner use cases over and over. Slootman’s section is only about 20 pages and five of them cover case studies from Square, Walmart, Capital One, Fair, and Blackboard. I suspect it may be due to the controversial ousting of their long-time CEO Bob Muglia for Frank Slootman, co-author of this book. As this Forbes article noted: “Just one problem: No one told Muglia until the day the company announced the coup. Speaking publicly about his departure for the first time, Muglia tells Forbes that it took him months to get over the shock.” One day we will hear the actual unfiltered story of Snowflake and it will make for an interesting comparison to this book.
Timing & Building. We often forget how important timing is in startups. Being the right investor or company at the right time can do a lot to drive unbelievable returns. Consider Don Valentine at Sequoia in the early 1970’s. We know that venture capital fund performance persists, in part due to incredible branding at firms like Sequoia that has built up over years and years (obviously reinforced by top-notch talents like Mike Moritz and Doug Leone). Don is a great investor and took significant risks on unproven individuals like Steve Jobs (Apple), Nolan Bushnell (Atari), and Trip Hawkins (EA). But he also had unfettered access to the birth of an entirely new ecosystem and knowledge of how that ecosystem would change business, built up from his years at Fairchild Semiconductor. Don is a unique person and capitalized on that incredible knowledgebase, veritably creating the VC industry. Sequoia is a top firm because he was in the right place at the right time with the right knowledge. Now let’s cover some companies that weren’t: Cloudera, Hortonworks, and MapR. In 2005, Yahoo engineers Doug Cutting and Mike Cafarella, inspired by the Google File System paper, created Hadoop, a distributed file system for storing and accessing data like never before. Hadoop spawned many companies like Cloudera, Hortonworks, and MapR that were built to commercialize the open-source Hadoop project. All of the companies came out of the gate fast with big funding - Cloudera raised $1B at a $4B valuation prior to its 2017 IPO, Hortonworks raised $260M at a $1B valuation prior to its 2014 IPO, and MapR $300M before it was acquired by HPE in 2019. The companies all had one thing in problem however, they were on-premise and built prior to the cloud gaining traction. That meant it required significant internal expertise and resources to run Cloudera, Hortonworks, and MapR software. In 2018, Cloudera and Hortonworks merged (at a $5B valuation) because the competitive pressure from the cloud was eroding both of their businesses. MapR was quietly acquired for less than it raised. Today Cloudera trades at a $5B valuation meaning no shareholder return since the merger and the business is only recently slightly profitable at its current low growth rate. This cautionary case study shows how important timing is and how difficult it is to build a lasting company in the data infrastructure world. As the new analytics stack is built with Fivetran, Matillion, dbt, Snowflake, and Databricks, it will be interesting to see which companies exist 10 years from now. Its probable that some new technology will come along and hurt every company in the stack, but for now the coast is clear - the scariest time for any of these companies.
Burn Baby Burn. Snowflake burns A LOT of money. In the Nine months ended October 31, 2020, Snowflake burned $343M, including $169M in their third quarter alone. Why would Snowflake burn so much money? Because they are growing efficiently! What does efficient growth mean? As we discussed in the last Frank Slootman book - sales and marketing efficiency is a key hallmark to understand the quality of growth a company is experiencing. According to their filings, Snowflake added ~$230M of revenue and spent $325M in sales and marketing. This is actually not terribly efficient - it supposes a dollar invested in sales and marketing yielded $0.70 of incremental revenue. While you would like this number to be closer to 1x (i.e. $1 in S&M yield $1 in revenue - hence a repeatable go-to-market motion), it is not terrible. ServiceNow (Slootman’s old company), actually operates less efficiently - for every dollar it invests in sales and marketing, it generates only $0.55 of subscription revenue. Crowdstrike, on the other hand, operates a partner-driven go-to-market, which enables it to generate more while spending less - created $0.90 for every dollar invested in sales and marketing over the last nine months. However, there is a key thing that distinguishes the data warehouse compared to these other companies and Ben Thompson at Stratechery nails it here: “Think about this in the context of Snowflake’s business: the entire concept of a data warehouse is that it contains nearly all of a company’s data, which (1) it has to be sold to the highest levels of the company, because you will only get the full benefit if everyone in the company is contributing their data and (2) once the data is in the data warehouse it will be exceptionally difficult and expensive to move it somewhere else. Both of these suggest that Snowflake should spend more on sales and marketing, not less. Selling to the executive suite is inherently more expensive than a bottoms-up approach. Data warehouses have inherently large lifetime values given the fact that the data, once imported, isn’t going anywhere.” I hope Snowflake burns more money in the future, and builds a sustainable long-term business.

Dig Deeper

November 2020 - Tape Sucks: Inside Data Domain, A Silicon Valley Growth Story by Frank Slootman

This month we read a short, under-discussed book by current Snowflake and former ServiceNow and Data Domain CEO, Frank Slootman. The book is just like Frank - direct and unafraid. Frank has had success several times in the startup world and the story of Data Domain provides a great case study of entrepreneurship. Data Domain was a data deduplication company, offering a 20:1 reduction of data backed up to tape casettes by using new disk drive technology.

Tech Themes

Data Domain’s 2008 10-K prior to being acquired

First time CEO at a Company with No Revenue. Frank is an immigrant to the US, coming from the Netherlands shortly after graduating from the University of Rotterdam. After being rejected by IBM 10+ times, he joined Burroughs corporation, an early mainframe provider which subsequently merged with its direct competitor Sperry for $4.8B in 1986. Frank then spent some time at Compuware and moved back to the Netherlands to help it integrate the acquisition of Uniface, an early customizable report building software. After spending time there, he went to Borland software in 1997, working his way up the product management ranks but all the while being angered by time spent lobbying internally, rather than building. Frank joined Data Domain in the Spring of 2003 - when it had no customers, no revenue, and was burning cash. The initial team and VC’s were impressive - Kai Li, a computer science professor on sabbatical from Princeton, Ben Zhu, an EIR at USVP, and Brian Biles, a product leader with experience at VA Linux and Sun Microsystems. The company was financed by top-tier VC’s New Enterprise Associates and Greylock Partners, with Aneel Bhusri (Founder and current CEO of Workday) serving as initial CEO and then board chairman. This was a stacked team and Slootman knew it: “I’d bring down the average IQ of the company by joining, which felt right to me.” The Company had been around for 18 months and already burned through a significant amount of money when Frank joined. He knew he needed to raise money relatively soon after joining and put the Company’s chances bluntly: “Would this idea really come together and captivate customers? Nobody knew. We, the people on the ground floor, were perhaps, the most surprised by the extraordinary success we enjoyed.”
Playing to his Strengths: Capital Efficiency. One of the big takeaways from the Innovators by Walter Issacson was that individuals or teams at the nexus of disciplines - primarily where the sciences meet the humanities, often achieved breakthrough success. The classic case study for this is Apple - Steve Jobs had an intense love of art, music, and design and Steve Wozniak was an amazing technologist. Frank has cultivated a cross-discipline strength at the intersection of Sales and Technology. This might be driven by Slootman’s background is in economics. The book has several references to economic terms, which clearly have had an impact on Frank’s thinking. Data Domain espoused capital efficiency: “We traveled alone, made few many-legged sales calls, and booked cheap flights and hotels: everybody tried to save a dime for the company.” The results showed - the business went from $800K of revenue in 2004 to $275 million by 2008, generating $75M in cash flow from operations. Frank’s capital efficiency was interesting and broke from traditional thinking - most people think to raise a round and build something. Frank took a different approach: “When you are not yet generating revenue, conservation of resource is the dominant theme.” Over time, “when your sales activity is solidly paying for itself,” the spending should shift from conservative to aggressive (like Snowflake is doing this now). The concept of sales efficiency is somewhat talked about, but given the recent fundraising environment, is often dismissed. Sales efficiency can be thought of as: “How much revenue do I generate for every $1 spent in sales and marketing?” Looking at the P&L below, we see Data Domain was highly efficient in its sales and marketing activity - the company increased revenue $150M in 2008, despite spending $115M in sales and marketing (a ratio of 1.3x). Contrast this with a company like Slack which spent $403M to acquire $230M of new revenue (a ratio of 0.6x). It gets harder to acquire customers at scale, so this efficiency is supposed to come down over time but best in class is hopefully above 1x. Frank clearly understands when to step on the gas with investing, as both ServiceNow and Snowflake have remained fairly efficient (from a sales perspective at least) while growing to a significant scale.
Technology for Technology’s Sake. “Many technologies are conceived without a clear, precise notion of the intended use.” Slootman hits on a key point and one that the tech industry has struggled to grasp throughout its history. So many products and companies are established around budding technology with no use case. We’ve discussed Magic Leap’s fundraising money-pit (still might find its way), and Iridium Communications, the massive satellite telephone that required people to carry a suitcase around to use it. Gartner, the leading IT research publication (which is heavily influenced by marketing spend from companies) established the Technology Hype Cycle, complete with the “Peak of inflated expectations,” and the “Trough of Disillusionment” for categorizing technologies that fail to live up to their promise. There have been several waves that have come and gone: AR/VR, Blockchain, and most recently, Serverless. Its not so much that these technologies were wrong or not useful, its rather that they were initially described as a panacea to several or all known technology hindrances and few technologies ever live up to that hype. Its common that new innovations spur tons of development but also lots of failure, and this is Slootman’s caution to entrepreneurs. Data Domain was attacking a problem that existed already (tape storage) and the company provided what Clayton Christensen would call a sustaining innovation (something that Slootman points out). Whenever things go into “winter state”, like the internet after the dot-com bubble, or the recent Crpyto Winter which is unthawing as I write; it is time to pay attention and understand the relevance of the innovation.

Business Themes

Importance of Owning Sales. Slootman spends a considerable amount of this small book discussing sales tactics and decision making, particularly with respect to direct sales and OEM relationships. OEM deals are partnerships with other companies whereby one company will re-sell the software, hardware, or service of another company. Crowdstrike is a popular product with many OEM relationships. The Company drives a significant amount of its sales through its partner model, who re-sell on behalf of Crowdstrike. OEM partnerships with big companies present many challenges: “First of all, you get divorced from your customer because the OEM is now between you and them, making customer intimacy challenging. Plus, as the OEM becomes a large part of your business, for all intents and purposes they basically own you without paying for the privilege…Never forget that nobody wants to sell your product more than you do.” The challenges don’t end there. Slootman points out that EMC discarded their previous OEM vendor in the data deduplication space, right after acquiring Data Domain. On top of that, the typical reseller relationship happens at a 10-20% margin, degrading gross margins and hurting ability to invest. It is somewhat similar to the challenges open-source companies like MongoDB and Elastic have run into with their core software being…free. Amazon can just OEM their offering and cut them out as a partner, something they do frequently. Partner models can be sustainable, but the give and take from the big company is a tough balance to strike. Investors like organic adoption, especially recently with the rise of freemium SaaS models percolating in startups. Slootman’s point is that at some point in enterprise focused businesses, the Company must own direct sales (and relationships) with its customers to drive real efficiency. After the low cost to acquire freemium adopters buy the product, the executive team must pivot to traditional top down enterprise sales to drive a successful and enduring relationship with the customer.
In the Thick of Things. Slootman has some very concise advice for CEOs: be a fighter, show some humanity, and check your ego at the door. “Running a startup reduces you to your most elementary instincts, and survival is on your mind most of the time…The CEO is the ‘Chief Combatant,’ warrior number one.” Slootman views the role of CEO as a fighter, ready to be the first to jump into the action, at all times. And this can be incredibly productive for business as well. Tony Xu, the founder and CEO of Doordash, takes time out every month to do delivery for his own company, in order to remain close to the customer and the problems of the company. Jeff Bezos famously still responds and views emails from customers at jeff@amazon.com. Being CEO also requires a willingness to put yourself out there and show your true personality. As Slootman puts it: “People can instantly finger a phony. Let them know who you really are, warts and all.” As CEO you are tasked with managing so many people and being involved in all aspects of the business, it is easy to become rigid and unemotional in everyday interactions. Harvard Business School professor and former leader at Uber distills it down to a simple phrase: “Begin With Trust.” All CEO’s have some amount of ego, driving them to want to be at the top of their organization. Slootman encourages CEO’s to be introspective, and try to recognize blind spots, so ego doesn’t drive day-to-day interactions with employees. One way to do that is simple: use the pronoun “we” when discussing the company you are leading. Though Slootman doesn’t explicitly call it out - all of these suggestions (fighting, showing empathy, getting rid of ego) are meant to build trust with employees.
R-E-C-I-P-E for a Great Culture. The last fifth of the book is all focused on building culture at companies. It is the only topic Slootman stays on for more than a few chapters, so you know its important! RECIPE was an acronym created by the employees at Data Domain to describe the company’s values: Respect, Excellence, Customer, Integrity, Performance, Execution. Its interesting how simple and focused these values are. Technology has pushed its cultural delusion’s of grandeur to an extreme in recent years. The WeWork S-1 hilariously started with: “We are a community company committed to maximum global impact. Our mission is to elevate the world’s consciousness.” But none of Data Domain’s values were about changing the world to be a better place - they were about doing excellent, honest work for customers. Slootman is lasered focused on culture, and specifically views culture as an asset - calling it: “The only enduring, sustainable form of differentiation. These days, we don’t have a monopoly for very long on talent, technology, capital, or any other asset; the one thing that is unique to us is how we choose to come together as a group of people, day in and day out. How many organizations are there that make more than a halfhearted attempt at this?” Technology companies have taken different routes in establishing culture: Google and Facebook have tried to create culture by showering employees with unbelievable benefits, Netflix has focused on pure execution and transparency, and Microsoft has re-vamped its culture by adopting a Growth Mindset (has it really though?). Google originally promoted “Don’t be evil,” as part of its Code of Conduct but dropped the motto in 2018. Employees want to work for mission-driven organizations, but not all companies are really changing the world with their products, and Frank did not try to sugarcoat Data Domain’s data-duplication technology as a way to “elevate the world’s consciousness.” He created a culture driven by performance and execution - providing a useful product to businesses that needed it. The culture was so revered that post-acquisition, EMC instituted Data Domain’s performance management system. Data Domain employees were looked at strangely by longtime EMC executives, who had spent years in a big and stale company. Culture is a hard thing to replicate and a hard thing to change as we saw with the Innovator’s Dilemma. Might as well use it to help the company succeed!

Dig Deeper

February 2020 - How the Internet Happened: From Netscape to the iPhone by Brian McCullough

Brian McCullough, host of the Internet History Podcast, does an excellent job of showing how the individuals adopted the internet and made it central to their lives. He follows not only the success stories but also the flame outs which provide an accurate history of a time of rapid technological change.

Tech Themes

Form to Factor: Design in Mobile Devices. Apple has a long history with mobile computing, but a few hiccups in the early days are rarely addressed. These hiccups also telegraph something interesting about the technology industry as a whole - design and ease of use often trump features. In the early 90’s Apple created the Figaro, a tablet computer that weighed eight pounds and allowed for navigation through a stylus. The issue was it cost $8,000 to produce and was 3/4 of an inch thick, making it difficult to carry. In 1993, the Company launched the Newton MessagePad, which cost $699 and included a calendar, address book, to-do list and note pad. However, the form was incorrect again; the MessagePad was 7.24 in. x 4.5 in. and clunky. With this failure, Apple turned its attention away from mobile, allowing other players like RIM and Blackberry to gain leading market share. Blackberry pioneered the idea of a full keyboard on a small device and Marc Benioff, CEO of salesforce.com, even called it, “the heroin of mobile computing. I am serious. I had to stop.” IBM also tried its hand in mobile in 1992, creating the Simon Personal Communicator, which had the ability to send and receive calls, do email and fax, and sync with work files via an adapter. The issue was the design - 8 in. by 2.5 in. by 1.5 in. thick. It was a modern smartphone, but it was too big, clunky, and difficult to use. It wasn’t until the iPhone and then Android that someone really nailed the full smart phone experience. The lessons from this case study offer a unique insight into the future of VR. The company able to offer the correct form factor, at a reasonable price can gain market share quickly. Others who try to pioneer too much at a time (cough, magic leap), will struggle.
How to know you’re onto something. Facebook didn’t know. On November 30, 2004, Facebook surpassed one million users after being live for only ten months. This incredible growth was truly remarkable, but Mark Zuckerberg still didn’t know facebook was a special company. Sean Parker, the founder of Napster, had been mentoring Zuckerberg the prior summer: “What was so bizarre about the way Facebook was unfolding at that point, is that Mark just didn’t totally believe in it and wanted to go and do all these other things.” Zuckerberg even showed up to a meeting at Sequoia Capital still dressed in his pajamas with a powerpoint entitled: “The Top Ten Reasons You Should Not Invest.” While this was partially a joke because Sequoia has spurned investing in Parker’s latest company, it represented how immature the whole facebook operation was, in the face of rapid growth. Facebook went on to release key features like groups, photos, and friending, but most importantly, they developed their revenue model: advertising. The quick user growth and increasing ad revenue growth got the attention of big corporations - Viacom offered $2B in cash and stock, and Yahoo offered $1B all cash. By this time, Zuckerberg realized what he had, and famously spurned several offers from Yahoo, even after users reacted negatively to the most important feature that facebook would ever release, the News Feed. In today’s world, we often see entrepreneur’s overhyping their companies, which is why Silicon Valley was in-love with dropout founders for a time, their naivite and creativity could be harnessed to create something huge in a short amount of time.
Channel Partnerships: Why apple was reluctant to launch a phone. Channel partnerships often go un-discussed at startups, but they can be incredibly useful in growing distribution. Some industries, such as the Endpoint Detection and Response (EDR) market thrives on channel partnership arrangements. Companies like Crowdstrike engage partners (mostly IT services firms) to sell on their behalf, lowering Crowdstrike’s customer acquisition and sales spend. This can lead to attractive unit economics, but on the flip side, partners must get paid and educated on the selling motion which takes time and money. Other channel relationships are just overly complex. In the mid 2000’s, mobile computing was a complicated industry, and companies hated dealing with old, legacy carriers and simple clunky handset providers. Apple tried the approach of working with a handset provider, Motorola, but they produced the terrible ROKR which barely worked. The ROKR was built to run on the struggling Cingular (would become AT&T) network, who was eager to do a deal with Apple in hopes of boosting usage on their network. After the failure of the ROKR, Cingular executives begged Jobs to build a phone for the network. Normally, the carriers had specifications for how phones were built for their networks, but Jobs ironed out a contract which exchanged network exclusivity for complete design control, thus Apple entered into mobile phones. The most important computing device of the 2000’s and 2010’s was built on a channel relationship.

Business Themes

AOL-Time Warner: the merger destined to fail. To fully understand the AOL-Time Warner merger, you must first understand what AOL was, what it was becoming, and why it was operating on borrowed time. AOL started as an ISP, charging customers $9.95 for five hours of dial-up internet access, with each additional hour costing $2.95. McCullough describes AOL: “AOL has often been described as training wheels for the Internet. For millions of Americans, their aol.com address was their first experience with email, and thus their first introduction to the myriad ways that networked computing could change their lives.” AOL grew through one of the first viral marketing campaigns ever; AOL put CDs into newspapers which allowed users to download AOL software and get online. The Company went public in March of 1992 and by 1996 the Company had 2.1 million subscribers, however subscribers were starting to flee to cheaper internet access. It turned out that building an ISP was relatively cheap, and the high margin cash flow business that AOL had built was suddenly threatened by a number of competitors. AOL persisted with its viral marketing strategy, and luckily many americans still had not tried the internet yet and defaulted to AOL as being the most popular. AOL continued to add subscribers and its stock price started to balloon; in 1998 alone the stock went up 593%. AOL was also inking ridiculous, heavily VC funded deals with new internet startups. Newly public Drkoop, which raised $85M in an IPO, signed a four year $89M deal to be AOL’s default provider of health content. Barnes and Noble paid $40M to be AOL’s bookselling partner. Tel-save, a long distance phone provider signed a deal worth $100M. As the internet bubble continued to grow, AOL’s CEO, Steve Case realized that many of these new startups would be unable to fufill their contractual obligations. Early web traffic reporting systems could easily be gamed, and companies frequently had no business model other than attract a certain demographic of traffic. By 1999, AOL had a market cap of $149.8B and was added to the S&P 500 index; it was bigger than both Disney and IBM. At this time, the world was shifting away from dial-up internet to modern broadband connections provided by cable companies. One AOL executive lamented: “We all knew we were living on borrowed time and had to buy something of substance by using that huge currency [AOL’s stock].” Time Warner was a massive media company, with movie studios, TV channels, magazines and online properties. On Jan 10, 2000, AOL merged with Time Warner in one of the biggest mergers in history. AOL owned 56% of the combined company. Four days later, the Dow peaked and began a downturn which would decimate hundreds of internet businesses built on foggy fundamentals. Acquisitions happen for a number of reasons, but imminent death is not normally considered by analysts or pundits. When you see acquisitions, read the press release and understand why (at least from a marketing perspective), the two companies made a deal. Was the price just astronomical (i.e. Instagram) or was their something very strategic (i.e. Microsoft-Github)? When you read the press release years later, it should indicate whether the combination actually was proved out by the market.
Acquisitions in the internet bubble: why acquisitions are really just guessing. AOL-Time Warner shows the interesting conundrum in acquisitions. HP founder David Packard coined this idea somewhat in Packard’s law: “No company can consistently grow revenues faster than its ability to get enough of the right people to implement that growth and still become a great company. If a company consistently grows revenue faster than its ability to get enough of the right people to implement that growth, it will not simply stagnate; it will fall.” Author of Good to Great, Jim Collins, clarified this idea: “Great companies are more likely to die of ingestion of too much opportunity, than starvation from too little.” Acquisitions can be a significant cause of this outpacing of growth. Look no further than Yahoo, who acquired twelve companies between September 1997 and June 1999 including Mark Cuban’s Broadcast.com for $5.7B (Kara Swisher at WSJ in 1999), GeoCities for $3.6B, and Y Combinator founder Paul Graham’s Viaweb for $48M. They spent billions in stock and cash to acquire these companies! Its only fitting that two internet darlings would eventually end up in the hands of big-telecom Verizon, who would acquire AOL for $4.4B in 2015, and Yahoo for $4.5B in 2017, only to write down the combined value by $4.6B in 2018. In 2013, Yahoo would acquire Tumblr for $1.1B, only to sell it off this past year for $3M. Acquisitions can really be overwhelming for companies, and frequently they don’t work out as planned. In essence, acquisitions are guesses about future value to customers and rarely are they as clean and smart as technology executives make them seem. Some large organizations have gotten good at acquisitions - Google, Microsoft, Cisco, and Salesforce have all made meaningful acquisitions (Android, Github, AppDynamics, ExactTarget, respectively).
Google and Excite: the acquisition that never happened. McCullough has an incredible quote nestled into the start of chapter six: “Pioneers of new technologies are rarely the ones who survive long enough to dominate their categories; often it is the copycat or follow-on names that are still with us to this day: Google, not AltaVista, in search; Facebook, not Friendster, in social networks.” Amazon obviously bucked this trend (he mentions that), but in search he is absolutely right! In 1996, several internet search companies went public including Excite, Lycos, Infoseek, and Yahoo. As the internet bubble grew bigger, Yahoo was the darling of the day, and by 1998, it had amassed a $100B market cap. There were tons of companies in the market including the players mentioned above and AltaVista, AskJeeves, MSN, and others. The world did not need another search engine. However, in 1998, Google founders Larry Page and Sergey Brin found a better way to do search (the PageRank algorithm) and published their famous paper: “The Anatomy of a Large-Scale Hypertextual Web Search Engine.” They then went out to these massive search engines and tried to license their technology, but no one was interested. Imagine passing on Goolge’s search engine technology. In an over-ingestion of too much opportunity, all of the search engines were trying to be like AOL and become a portal to the internet, providing various services from their homepages. From an interview in 1998, “More than a "portal" (the term analysts employ to describe Yahoo! and its rivals, which are most users' gateway to the rest of the Internet), Yahoo! is looking increasingly like an online service--like America Online (AOL) or even CompuServe before the Web.” Small companies trying to do too much (cough, uber self-driving cars, cough). Excite showed the most interest in Google’s technology and Page offered it to the Company for $1.6M in cash and stock but Excite countered at $750,000. Excite had honest interest in the technology and a deal was still on the table until it became clear that Larry wanted Excite to rip out its search technology and use Google’s instead. Unfortunately that was too big of a risk for the mature Excite company. The two companies parted ways and Google eventually became the dominant player in the industry. Google’s focus was clear from the get-go, build a great search engine. Only when it was big enough did it plunge into acquisitions and development of adjacent technologies.