Column: Why SPACs Are Today’s Best Option for an IPO

Spencer Rascoff

Spencer Rascoff serves as executive chairman of dot.LA. He is an entrepreneur and company leader who co-founded Zillow, Hotwire, dot.LA, Pacaso and Supernova, and who served as Zillow's CEO for a decade. During Spencer's time as CEO, Zillow won dozens of "best places to work" awards as it grew to over 4,500 employees, $3 billion in revenue, and $10 billion in market capitalization. Prior to Zillow, Spencer co-founded and was VP Corporate Development of Hotwire, which was sold to Expedia for $685 million in 2003. Through his startup studio and venture capital firm, 75 & Sunny, Spencer is an active angel investor in over 100 companies and is incubating several more.

Column: Why SPACs Are Today’s Best Option for an IPO

I have long been a proponent of going public because I believe it creates stronger, more disciplined companies that deliver greater shareholder value. It's great to see the pendulum in the founder and venture capital community swinging away from the "stay private longer" attitude that dominated tech over the last decade.

That said, the traditional IPO listing path has many shortcomings. I experienced this firsthand in 2011 when we took Zillow public. The cover price on the original S-1 was $12-$14 a share, but we upped it to $14-$16 due to strong demand on the IPO roadshow. We priced it at $20 a share, only to watch the first trade open at $60 that day. (Note: Zillow has since done a 3-for-1 stock split, so divide these numbers by three if you're trying to compare it with today's ~ $100 stock price.)


So on what should have been a day of high-fives and champagne, I couldn't help but feel disappointment that we left a huge amount of money on the table by underpricing our IPO. 🤦 Facepalm.

Our employees and our venture capital owners were penalized by this broken system. And it's not just the Zillow IPO — this problem is systemic; the typical tech IPO trades up by 43% one day later. That's a massive amount of money to leave on the table for an issuer.

Direct listings provide a second path to a public listing, and they typically avoid the underpricing issue of a traditional listing. But they have their own set of shortcomings, including the inability of the company to raise primary capital in the offering.

SPACs — Special Purpose Acquisition Companies — offer a third way, and remedy many of the problems with IPOs, while offering some new benefits, including the ability for a company to provide financial projections at the time of the SPAC IPO, when the private company merges with the public SPAC. In addition, the SPAC model offers a quicker, more certain path to going public. With the launch of Supernova Partners Acquisition Company (yes, our acronym is "SPAC"), my partners and I are creating that path for a company in the broader tech sector.

But going public by merging with a SPAC is just an express route to basecamp. Being a fast-scaling, successful public company is the summit. I know this because I've been up that mountain.

From the point we went public at Zillow, we navigated 16 acquisitions — including that of another public company who was our biggest competitor. We grew our employee base 10-fold in six years. We pulled off a complex business pivot. And most critically, we protected our culture from the volatility of the stock market and kept our people focused on our mission. The internal name for the Zillow IPO in 2011 was "Project Step," because it was just a step along the way. I've seen firsthand that going public is the beginning, not the end.

The transition from being privately held to being publicly traded is like graduating from college and entering the real world and the job market. Welcome to the big time. And for a newly public company, it's a scary world out there, full of potential facepalm moments. The right mentors, directors, and advisors can make a huge difference during those first few years as a public company. And that is yet another benefit of going public through a SPAC: you get the benefit of the experience which the sponsor group provides. In Supernova's case, we have assembled a world-class team with diverse skills available to help whichever company we take public. We are player-coaches who have all excelled on the field before, and are now excited to help shepherd a company and help them avoid facepalm moments of their own.

There are many SPACs (and more every day), but not all are created equal. Some teams are Wall Street-heavy and exist only to take a company public, exploiting a private-to-public valuation arbitrage opportunity; some are led by Silicon Valley founders who will act as advisors for a longer period of time. Very few combine both. With Supernova, my partners and I, along with our board, together create a Swiss Army knife of experience in company building, culture building, marketing, finance, deal-making, product design, tech, capital markets and operations.

We know the journey to IPO and beyond is filled with facepalm crevasses that can be avoided with an expert guide at your side. Our operations and managerial experience, combined with our mentoring and coaching of founders and executives over two decades, will help chart a path toward long-term value. Personally, this is the beginning of a very exciting journey in my career as it combines all of my passions — investing, mentoring and coaching — with my experience as a seasoned CEO, all together with an unparalleled team I'm proud to call partners.

Spencer Rascoff is the co-Chair of Supernova Partners Acquisition Company and the co-founder of dot.LA.

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AI Is Rapidly Advancing, but the Question Is, Can We Keep Up?

Lon Harris
Lon Harris is a contributor to dot.LA. His work has also appeared on ScreenJunkies, RottenTomatoes and Inside Streaming.
AI Is Rapidly Advancing, but the Question Is, Can We Keep Up?
Evan Xie

One way to measure just how white-hot AI development has become: the world is running out of the advanced graphics chips necessary to power AI programs. While Intel central processing units were once the most sought-after industry leaders, advanced graphics chips like Nvidia’s are designed to run multiple computations simultaneously, a baseline necessity for many AI models.

An early version of ChatGPT required around 10,000 graphics chips to run. By some estimates, newer updates require 3-5 times that amount of processing power. As a result of this skyrocketing demand, shares of Nvidia have jumped 165% so far this year.

Building on this momentum, this week, Nvidia revealed a line-up of new AI-related projects including an Israeli supercomputer project and a platform utilizing AI to help video game developers. For smaller companies and startups, however, getting access to the vital underlying technology that powers AI development is already becoming less about meritocracy and more about “who you know.” According to the Wall Street Journal, Elon Musk scooped up a valuable share of server space from Oracle this year before anyone else got a crack at it for his new OpenAI rival, X.AI.

The massive demand for Nvidia-style chips has also created a lucrative secondary market, where smaller companies and startups are often outbid by larger and more established rivals. One startup founder compares the fevered crush of the current chip marketplace to toilet paper in the early days of the pandemic. For those companies that don’t get access to the most powerful chips or enough server space in the cloud, often the only remaining option is to simplify their AI models, so they can run more efficiently.

Beyond just the design of new AI products, we’re also at a key moment for users and consumers, who are still figuring out what sorts of applications are ideal for AI and which ones are less effective, or potentially even unethical or dangerous. There’s now mounting evidence that the hype around some of these AI tools is reaching a lot further than the warnings about its drawbacks.

JP Morgan Chase is training a new AI chatbot to help customers choose financial securities and stocks, known as IndexGPT. For now, they insist that it’s purely supplemental, designed to advise and not replace money managers, but it may just be a matter of time before job losses begin to hit financial planners along with everyone else.

A lawyer in New York just this week was busted by a judge for using ChatGPT as part of his background research. When questioned by the judge, lawyer Peter LoDuco revealed that he’d farmed out some research to a colleague, Steven A. Schwartz, who had consulted with ChatGPT on the case. Schwartz was apparently unaware that the AI chatbot was able to lie – transcripts even show him questioning ChatGPT’s responses and the bot assuring him that these were, in fact, real cases and citations.

New research by Marucie Jakesch, a doctoral student from Cornell University, suggests that even users who are more aware than Schwartz about how AI works and its limitations may still be impacted in subtle and subconscious ways by its output.

Not to mention, according to data from Intelligent.com, high school and college students already – on the whole – prefer utilizing ChatGPT for help with schoolwork over a human tutor. The survey also notes that advanced students tend to report getting more out of using ChatGPT-type programs than beginners, likely because they have more baseline knowledge and can construct better and more informative prompts.

But therein lies the big drawback to using ChatGPT and other AI tools for education. At least so far, they’re reliant on the end user writing good prompts and having some sense about how to organize a lesson plan for themselves. Human tutors, on the other hand, have a lot of personal experience in these kinds of areas. Someone who instructs others in foreign languages professionally probably has a good inherent sense of when you need to focus on expanding your vocabulary vs. drilling certain kinds of verb and tense conjugations. They’ve helped many other students prepare for tests, quizzes, and real-world challenges, while computer software can only guess at what kinds of scenarios its proteges will face.

A recent Forbes editorial by academic Thomas Davenport suggests that, while AI is getting all the hype right now, other forms of computing or machine learning are still going to be more effective for a lot of basic tasks. From a marketing perspective in 2023, it’s helpful for a tech company to throw the “AI” brand around, but it’s not magically going to be the answer for every problem.

Davenport points to a similar (if smaller) whirlwind of excitement around IBM’s “Watson” in the early 2010s, when it was famously able to take out human “Jeopardy!’ champions. It turns out, Watson was a general knowledge engine, really best suited for jobs like playing “Jeopardy.” But after the software gained celebrity status, people tried to use it for all sorts of advanced applications, like designing cancer drugs or providing investment advice. Today, few people turn to Watson for these kinds of solutions. It’s just the wrong tool for the job. In that same way, Davenport suggests that generative AI is in danger of being misapplied.

While the industry and end users both race to solve the AI puzzle in real time, governments are also feeling pressure to step in and potentially regulate the AI industry. This is much easier said than done, though, as politicians face the same kinds of questions and uncertainty as everyone else.

OpenAI CEO Sam Altman has been calling for governments to begin regulating AI, but just this week, he suggested that the company might pull out of the European Union entirely if the regulations were too onerous. Specifically, Altman worries that attempts to narrow what kinds of data can be used to train AI systems – specifically blocking copyrighted material – might well prove impossible. “If we can comply, we will, and if we can’t, we’ll cease operating,” Altman told Time. “We will try, but there are technical limits to what’s possible.” (Altman has already started walking this threat back, suggesting he has no immediate plans to exit the EU.)

In the US, The White House has been working on a “Blueprint for an AI Bill of Rights,” but it’s non-binding, just a collection of largely vague suggestions. It’s one thing to agree “consumers shouldn’t face discrimination from an algorithm” and “everyone should be protected from abusive data practices and have agency over how their data is used.” But enforcement is an entirely different animal. A lot of these issues already exist in tech, and are much larger than AI, and the US government already doesn’t do much about them.

Additionally, it’s possible AI regulations won’t work well at all if they aren’t global. Even if you set some policies and get an entire nation’s government to agree, how to set similar worldwide protocols? What if US and Europe agree but India doesn’t? Everyone around the world accesses roughly the same internet, so without any kind of international standard, it’s going to be much harder for individual nations to enforce specific rules. As with so many other AI developments, there’s inherent danger in patchwork regulations; it could allow some companies, or regions, or players to move forward while others are unfairly or ineffectively stymied or held back.

The same kinds of socio-economic concerns around AI that we have nationally – some sectors of the work force left behind, the wealthiest and most established players coming in to the new market with massive advantages, the rapid spread of misinformation – are all, in actuality, global concerns. Just as the hegemony of Microsoft and Google threaten the ability of new players to enter the AI space, the West’s early dominance of AI tech threatens to push out companies and innovations from emerging markets like Southeast Asia, Subsaharan Africa, and Central America. Left unfettered, AI could potentially deepen social, economic, and digital divisions both within and between all of these societies.

Undaunted, some governments aren’t waiting around for these tools to develop any further before they start attempting to regulate them. New York City has already set up some rules about how AI can be used during the hiring process while will take effect in July. The law requires any company using AI software in hiring to notify candidates that it’s being used, and to have independent auditors check the system annually for bias.

This sort of piecemeal figure-it-out-as-we-go approach is probably what’s going to be necessary, at least short-term, as AI development shows zero signs of slowing down or stopping any time soon. Though there’s some disagreement among experts, most analysts agree with Wharton professor and economist Jeremy Siegel, who told CNBC this week that AI is not yet a bubble. He pointed to the Nvidia earnings as a sign the market remains healthy and not overly frothy. So, at least for now, the feverish excitement around AI is not going to burst like a late ‘90s startup stock. The world needs to prepare as if this technology is going to be with us for a while.

Rivian CEO Teases R2, New Features in Instagram AMA

David Shultz

David Shultz reports on clean technology and electric vehicles, among other industries, for dot.LA. His writing has appeared in The Atlantic, Outside, Nautilus and many other publications.

Rivian CEO Teases R2, New Features in Instagram AMA
Rivian

Rivian CEO RJ Scaringe took to Instagram last weekend to answer questions from the public about his company and its future. Topics covered included new colors, sustainability, production ramp, new products and features. Speaking of which, viewers also got a first look at the company’s much-anticipated R2 platform, albeit made of clay and covered by a sheet, but hey, that’s…something. If you don’t want to watch the whole 33 minute video, which is now also on Youtube, we’ve got the highlights for you.

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From AI to Layoffs, Here's Why College Grads No Longer Want Tech Jobs

Lon Harris
Lon Harris is a contributor to dot.LA. His work has also appeared on ScreenJunkies, RottenTomatoes and Inside Streaming.
From AI to Layoffs, Here's Why College Grads No Longer Want Tech Jobs
Evan Xie

A new report in Bloomberg suggests that younger workers and college graduates are moving away from tech as the preferred industry in which to embark on their careers. While big tech companies and startups once promised skilled young workers not just the opportunity to develop cutting-edge, exciting products, but also perks and – for the most talented and ambitious newcomers – a relatively reliable path to wealth. (Who could forget the tales of overnight Facebook millionaires that fueled the previous dot com explosion? There were even movies about it!)

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