Specializing in Computer Repair, Networking, Internet Solutions, Web Designing, Hybrid App Designing and Windows Operating systems.
Stephen Warwick / iMore:
Q&A with FOSS Patents' Florian Mueller and analyst Neil Cybart about the merits of Epic's lawsuits against Apple and Google, and the economics of the App Store — “Epic's complaints are very well-crafted, but the hurdle for establishing an antitrust violation is high”
Catalin Cimpanu / ZDNet:
Freepik, a popular freemium stock photos marketplace, discloses data breach and says hacker obtained info of 8.3M users, including usernames and password hashes — Freepik is one of the most popular websites on the internet, currently ranked #97 on the Alexa Top 100 sites list.
John D. McKinnon / Wall Street Journal:
U.S. WeChat Users Alliance, a nonprofit, files a lawsuit in district court in SF against Trump's executive order banning transactions with WeChat and ByteDance — Group of users of popular Tencent-owned app says Trump administration's planned curbs violate civil rights, target Chinese-Americans
Jane Wakefield / BBC:
UK's national statistics regulator says it will review the algorithm used by exam regulator Ofqual to decide grades for high school students — The national statistics regulator is stepping in to review the algorithm used by Ofqual to decide A-level grades for students who could not sit exams.
A few years ago, I came to the realization that my company, an HR consulting firm, was not as diverse as I wanted it to be. I value diversity because I know it makes teams better — more creative, more productive and more nimble. It helps my firm represent our community and serve our clients.
Though I tried to be inclusive in the language and the images I used on my website, in social media and when posting job openings, clearly something wasn’t working. I’m fortunate to know many talented diversity, equity and inclusion (DEI) experts. I asked them what I needed to do differently to attract a broader and more diverse pool of candidates. Here’s what they told me.
This may seem obvious, but it’s actually something many companies don’t do. When we talk about diversity, people tend to think only of race and gender. Our definition of diversity can be narrow, and we fail not only to include physical ability, gender identity and a host of other underestimated groups, but to recognize that even within a company, who is well represented versus underrepresented can vary by team or department.
I noticed a lack of diversity among my team of coaches; it was all women, but there were few women of color. The gender imbalance is not a surprise; according to the International Coaching Federation (ICF), approximately two-thirds of coaches are women. It would have been all too easy to throw up my hands and say “Well, there just aren’t enough qualified male coaches.” But blaming the pipeline is not a valid excuse and doesn’t fix the problem.
If I told people, “I’m trying to increase diversity on my team,” they would not have known what I meant; they would have been left to assume. Instead, I reached out to a small group of coaches who I know and trust, and told them “I’m looking for more coaches. Specifically, I would like to add women of color and I’d also like to have more men on the team.”
In the U.S., where we’ve been taught for so long not to talk about race or gender while hiring, this felt awkward. I had to push past that, and I’m thankful I did. The result was that I was not only able to add a number of experienced coaches to my team, I also built a whole new network of talented, diverse coaches from whom I continue to learn.
When you want to appeal to the most diverse candidates, language matters. It is (hopefully) obvious that terms like rock star, stud and ninja, which have been used all too frequently in job descriptions, are exclusive and off-putting to many candidates. But other words and phrases to use or avoid aren’t always common sense. The most appealing language can vary by job level, title and even geography.
Using a tool like Textio will help you create a job description that welcomes the most candidates to apply. Textio uses machine learning and algorithms from millions of job descriptions to help you spot and remove language that can unintentionally narrow your pool. Pop in your job description and you’ll get recommendations about the optimal length of your JD, word choices that skew masculine or feminine, sentence length and even whether your job suggests a fixed or growth mindset.
We’ve all seen the old equal employment opportunity (EEO) statement at the end of a job posting, which reads: “We’re an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.” It sounds like it came right off the government website, which it probably did. And that’s exactly how it comes across to candidates — like a canned message that you’ve added just to make sure you’re in compliance.
Did you know that you can customize your EEO statement? People do read it, and sticking with the legal jargon can be off-putting. A generic statement doesn’t say anything positive about your brand, and it doesn’t demonstrate a true commitment to diversity. If you haven’t already, now is the perfect time to update your statement, making it more reflective of your culture and values. For example:
“SurveyMonkey is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.”
Is it worth the effort? According to FairyGodboss, these personalized EEO statements “…communicate an employer’s dedication to unbiased recruiting, hiring and employment practices, which may encourage traditionally marginalized groups to seek employment within the organization.”
Most people are familiar with unconscious bias, and how it can negatively impact every step of the hiring process. Even as early as the resume review, bias causes recruiters and hiring managers to favor resumes of candidates who are in the majority. Bias can result from information ranging from a candidate’s name to which college they attended or which sports they played.
For instance, those with white-sounding names receive preference. The National Bureau of Economic Research found that “Job applicants with white names needed to send about 10 resumes to get one callback; those with African-American names needed to send around 15 resumes to get one callback.” I have a friend from India who received similar treatment. Even though she had worked with well-known companies, including Google and Deloitte, she had difficulty landing a job when she first came to the U.S. When she was ready to change employers, she adopted an American nickname on her resume and LinkedIn profile, and promptly got five callbacks.
In a blind resume review, identity cues that indicate race or gender are hidden. Tools like TalVista do this automatically, or your team can do it manually by hiding the information. While this helps increase the number of diverse candidates who make it to the next step, it does not address bias that occurs during interviews or later in your hiring process. That’s going to require training.
People from underestimated groups are all too familiar with the phrase “you have to see it to be it.” If I can’t see myself as someone who will be welcome and included in your company, I’m far less likely to join it. Yet too often even when a candidate meets with multiple interviewers, none of those interviewers reflect the candidate’s race or gender.
Imagine a woman of color spending the better part of a day meeting with a potential employer. Over the course of several hours, she meets a number of leaders but she doesn’t meet a single woman of color. She might think there are no women of color in the company, or wonder why they are not included in important decisions like interviewing and hiring.
When Karenga Ross interviewed at Intel after meeting them at a National Society of Black Engineers conference, she was pleasantly surprised to meet two African American women on the interview panel — these were women who looked like her. “It’s nice to be able to look across that table and see someone whom I can aspire to be. I can see someone who looks like me. It was refreshing. It was inspiring.”
One question I get from small companies is how to assemble a diverse interview panel if they don’t yet have diversity within their organization. I encourage them to cast a wide net. Think about who’s affiliated with your company, even if they’re not employees. If you have diverse advisors, investors or board members who are willing to help, invite them to join your panel. It will improve the candidate experience and help eliminate bias from your decision making.
Increasing diversity is an important investment that takes commitment, and a willingness to learn and experiment. You’ll have to try out some new things, and perhaps have conversations that make you uncomfortable. Remember to take one step at a time, and measure your progress and results.
Diverse hiring is one important step toward increasing diversity in your organization. Retention, however, depends on all employees feeling a sense of belonging. Remember to review your internal practices and policies to make sure they too meet the test of inclusion.
Ashley Gold / Axios:
Tech for Campaigns, a volunteer group of ~13,500 tech employees, have worked on 468 election-related projects, flipping 3 state chambers Democratic since 2018 — Some 13,500 tech workers with day jobs at companies like Facebook, Google, Netflix and Disney are volunteering in their spare …
“Selling Sunset” is the kind reality TV show that doesn’t bother with things like sympathetic or relatable characters.
The Netflix series recently released its third season — which, like the seasons before it, follows the efforts of the largely female staff at a Los Angeles brokerage to sell high-end real estate.
As we explain on the latest episode of the Original Content podcast, “Selling Sunset” does make the occasional, perfunctory effort to tug at the heartstrings, but its attention is clearly elsewhere: on the glamorous Hollywood Hills houses up for sale, the ups and downs of the luxury real estate business and especially on the feuds between different factions at the brokerage.
It’s the kind of show where the most compelling and memorable characters are the ones who fully embrace their devilish and dramatic side, denouncing their coworkers at every opportunity and adopting tactics like holding a “Burgers and Botox” event to promote their listings.
For some of us, these superficial delights were enough to make us like the show; for others, it wasn’t. In addition to discussing the series, we also debated Netflix’s new test of a Shuffle Play feature.
You can listen to our review in the player below, subscribe using Apple Podcasts or find us in your podcast player of choice. If you like the show, please let us know by leaving a review on Apple. You can also follow us on Twitter or send us feedback directly. (Or suggest shows and movies for us to review!)
If you’d like to skip ahead, here’s how the episode breaks down:
0:00 Intro
0:46 Netflix shuffle discussion
6:46 “Selling Sunset” review
Few topics garner cheers and groans quite as quickly as the no-code software explosion.
While investors seem uniformly bullish on toolsets that streamline and automate processes that once required a decent amount of technical know-how, not everyone seems to think that the product class is much of a new phenomenon.
On one hand, basic tools like Microsoft Excel have long given non-technical users a path toward carrying out complex tasks. (There’s historical precedent for the perspective.) On the other, a recent bout of low-code/no-code startups reaching huge valuations is too noteworthy to ignore, spanning apps like Notion, Airtable and Coda.
The TechCrunch team was interested in digging in to what defines the latest iteration of no-code and which industries might be the next target for entrepreneurs in the space. To get an answer on what is driving investor enthusiasm behind no-code, we reached out to a handful of investors who have explored the space:
As usual, we’re going to pull out some of the key trends and themes we identified from the group’s collected answers, after which we’ll share their responses at length, edited lightly for clarity and formatting.
Our investor participants agreed that low-code/no-code apps haven’t reached their peak potential, but there was some disagreement in how universal their appeal will prove to various industries. “Every trend is overhyped in some way. Low-code/no-code apps hold a lot of promise in some areas but not all,” Lightspeed’s Raviraj Jain told us.
Meanwhile, Gradient’s Darian Shirazi said “any and all” industries could benefit from increased no-code/low-code toolsets. We can see it either way, frankly.
CapitalG’s Laela Sturdy says the breadth of appeal boils down to finding which industries face the biggest supply constraints of technical talent.
“There just isn’t enough IT talent out there to meet demand, and issues like security and maintenance take up most of the IT department’s time. If business users want to create new systems, they have to wait months or in most cases, years, to see their needs met,” she wrote. “No-code changes the equation because it empowers business users to take change into their own hands and to accomplish goals themselves.”
Mayfield’s Rajeev Batra agreed, saying it would be cool “to see not twenty million developers [building] really cool software but two, three hundred million people developing really cool, interesting software.” If that winds up being the case, the sheer number of monthly-actives in the no and low-code spaces would imply a huge revenue base for the startup category.
That makes a wager on platforms in the space somewhat obvious.
And those bets are being placed. On the topic of valuations and developer interest, our collected interviewees were largely bullish on startup prices (competitive) and VC demand (strong) when it comes to no-code fundraising today.
Sturdy added that the number of early-stage companies in the category “are being funded at an accelerating pace,” noting that her firm is “excitedly watching this young cohort of emerging no-code companies and intend to invest in the trend for years to come.” So, we’re not about to run short of fodder for more Series A and B rounds in the space.
Taken as a whole, like it or not, the no and low-code startup trend appears firm from both a market-fit perspective and from the perspective of investor interest. Now, the rest of the notes.
We’ve seen some skepticism in the market that the low-code/no-code trend has earned its current hype, or product category. Do you agree that the product trend is overhyped, or misclassified?
I don’t think it’s over-hyped, but I believe it’s often misunderstood. No code/low code has been around for a long time. Many of us have been using Microsoft Excel as a low-code tool for decades, but the market has caught fire recently due to an increase in applicable use cases and a ton of innovation in the capabilities of these new low-code/no-code platforms, specifically around their ease of use, the level and type of abstractions they can perform and their extensibility/connectivity into other parts of a company’s tech stack. On the demand side, the need for digital transformation is at an all-time high and cannot be met with incumbent tech platforms, especially given the shortage of technical workers. Low-code/no-code tools have stepped in to fill this void by enabling knowledge workers — who are 10x more populous than technical workers — to configure software without having to code. This has the potential to save significant time and money and to enable end-to-end digital experiences inside of enterprises faster.
What other opportunities does the proliferation of low-code/no-code programs open up when it comes to technical and non-technical folks working more closely together?
This is where things get exciting. If you look at large businesses today, IT departments and business units are perpetually out of alignment because IT teams are resource constrained and unable to address core business needs quickly enough. There just isn’t enough IT talent out there to meet demand, and issues like security and maintenance take up most of the IT department’s time. If business users want to create new systems, they have to wait months or in most cases years to see their needs met. No-code changes the equation because it empowers business users to take change into their own hands and to accomplish goals themselves. The rapid state of digital transformation — which has only been expedited by the pandemic — requires more business logic to be encoded into automations and applications. No code is making this transition possible for many enterprises.
Rana Ayyub / Washington Post:
In India, like in Myanmar and the Philippines previously, Facebook has no issue aiding dishonest and hateful authoritarian leaders to increase its market share — On Aug. 14, the Wall Street Journal published a damning story about Facebook's complicity in aiding and abetting Prime Minister …
Varsha Bansal / Coda Story:
Hyderabad, with 500K+ CCTV cameras, has become India's most surveilled city and a test bed for AI-based visual surveillance tech for the rest of the country — Hyderabad's IT industry brought jobs and growth to the city. Critics now say the same technology is being used to watch the moves of every citizen
Since OpenAI first described its new AI language-generating system called GPT-3 in May, hundreds of media outlets (including MIT Technology Review) have written about the system and its capabilities. Twitter has been abuzz about its power and potential. The New York Times published an op-ed about it. Later this year, OpenAI will begin charging companies for access to GPT-3, hoping that its system can soon power a wide variety of AI products and services.
Is GPT-3 an important step toward artificial general intelligence—the kind that would allow a machine to reason broadly in a manner similar to humans without having to train for every specific task it encounters? OpenAI’s technical paper is fairly reserved on this larger question, but to many, the sheer fluency of the system feels as though it might be a significant advance.
We doubt it. At first glance, GPT-3 seems to have an impressive ability to produce human-like text. And we don’t doubt that it can used to produce entertaining surrealist fiction; other commercial applications may emerge as well. But accuracy is not its strong point. If you dig deeper, you discover that something’s amiss: although its output is grammatical, and even impressively idiomatic, its comprehension of the world is often seriously off, which means you can never really trust what it says.
Below are some illustrations of its lack of comprehension—all, as we will see later, prefigured in an earlier critique that one of us wrote about GPT-3’s predecessor.
Before proceeding, it’s also worth noting that OpenAI has thus far not allowed us research access to GPT-3, despite both the company’s name and the nonprofit status of its oversight organization. Instead, OpenAI put us off indefinitely despite repeated requests—even as it made access widely available to the media. Fortunately, our colleague Douglas Summers-Stay, who had access, generously offered to run the experiments for us.
OpenAI’s striking lack of openness seems to us to be a serious breach of scientific ethics, and a distortion of the goals of the associated nonprofit. Its decision forced us to limit our testing to a comparatively small number of examples, giving us less time to investigate than we would have liked, which means there may be more serious problems that we didn’t have a chance to discern. Even so, within the constraints of a small sample, many major issues with GPT-3 were immediately evident, in every domain of reasoning and comprehension that we tested.
In each example below, the prompt we generated is in ordinary font and the system’s continuation is in bold. Our comments are in italics. (A complete list of the experiments we did can be found here.)
The depressing thing is that none of this is new. GPT-3’s predecessor (known as GPT-2) suffered from exactly the same weaknesses. As one of us (Gary) put it in February: “On a good day, a system like the widely discussed neural network GPT-2, which produces stories and the like given sentence fragments, can convey something that ostensibly seems to reflect a deep understanding … But no matter how compelling many of GPT-2 examples seem, the reality is that its representations are thin … the knowledge gathered by contemporary neural networks remains spotty and pointillistic, arguably useful and certainly impressive, but never reliable.”
Too little has changed. Adding a hundred times more input data has helped, but only a bit. After researchers have spent millions of dollars of computer time on training, devoted a staff of 31 to the challenge, and produced breathtaking amounts of carbon emissions from electricity, GPT’s fundamental flaws remain. Its performance is unreliable, causal understanding is shaky, and incoherence is a constant companion. GPT-2 had problems with biological, physical, psychological, and social reasoning, and a general tendency toward incoherence and non sequiturs. GPT-3 does, too.
More data makes for a better, more fluent approximation to language; it does not make for trustworthy intelligence.
Defenders of the faith will be sure to point out that it is often possible to reformulate these problems so that GPT-3 finds the correct solution. For instance, you can get GPT-3 to give the correct answer to the cranberry/grape juice problem if you give it the following long-winded frame as a prompt:
GPT-3’s continuation to that prompt is, correctly: “B. This is a safe mixture.”
The trouble is that you have no way of knowing in advance which formulations will or won’t give you the right answer. To an optimist, any hint of success means that there must be a pony in here somewhere. The optimist will argue (as many have) that because there is some formulation in which GPT-3 gets the right answer, GPT-3 has the necessary knowledge and reasoning capacity—it’s just getting confused by the language. But the problem is not with GPT-3’s syntax (which is perfectly fluent) but with its semantics: it can produce words in perfect English, but it has only the dimmest sense of what those words mean, and no sense whatsoever about how those words relate to the world.
To understand why, it helps to think about what systems like GPT-3 do. They don’t learn about the world—they learn about text and how people use words in relation to other words. What it does is something like a massive act of cutting and pasting, stitching variations on text that it has seen, rather than digging deeply for the concepts that underlie those texts.
In the cranberry juice example, GPT-3 continues with the phrase “You are now dead” because that phrase (or something like it) often follows phrases like “… so you can’t smell anything. You are very thirsty. So you drink it.” A genuinely intelligent agent would do something entirely different: draw inferences about the potential safety of mixing cranberry juice with grape juice.
All GPT-3 really has is a tunnel-vision understanding of how words relate to one another; it does not, from all those words, ever infer anything about the blooming, buzzing world. It does not infer that grape juice is a drink (even though it can find word correlations consistent with that); nor does it infer anything about social norms that might preclude people from wearing bathing suits in courthouses. It learns correlations between words, and nothing more. The empiricist’s dream is to acquire a rich understanding of the world from sensory data, but GPT-3 never does that, even with half a terabyte of input data.
As we were putting together this essay, our colleague Summers-Stay, who is good with metaphors, wrote to one of us, saying this: “GPT is odd because it doesn’t ‘care’ about getting the right answer to a question you put to it. It’s more like an improv actor who is totally dedicated to their craft, never breaks character, and has never left home but only read about the world in books. Like such an actor, when it doesn’t know something, it will just fake it. You wouldn’t trust an improv actor playing a doctor to give you medical advice.”
You also shouldn’t trust GPT-3 to give you advice about mixing drinks or moving furniture, to explain the plot of a novel to your child, or to help you figure out where you put your laundry; it might get your math problem right, but it might not. It’s a fluent spouter of bullshit, but even with 175 billion parameters and 450 gigabytes of input data, it’s not a reliable interpreter of the world.
Gary Marcus is founder and CEO of Robust.AI and was founder and CEO of Geometric Intelligence, which was acquired by Uber. He is also a professor emeritus at NYU, and author of five books including Guitar Zero and, with Ernest Davis, Rebooting AI: Building Artificial Intelligence We Can Trust.
Ernest Davis is a professor of computer science at New York University. He has authored four books, including Representations of Commonsense Knowledge.
Bloomberg:
Chinese food delivery giant Meituan had sales of $3.6B, net profit of $317M in the June quarter; Meituan stock is up 2x+ in 2020, taking its market cap to $186B — - Food delivery, groceries were bright spots in the June quarter — Meituan shares jumped 4.5% before earnings were released
Sarah Kessler / OneZero :
Uber and Lyft's potential franchise model is reminiscent of FedEx's similar move, when FedEx shifted from employing independent contractors to fleet operators — A new ‘franchise’ model could help the companies without necessarily improving working conditions
Adam Laurie / Security Intelligence:
IBM's team of hackers uncovered a way, in Sep 2019, to bypass security checks to access secured data in millions of IoT devices; vulnerability was fixed in Feb. — By Adam Laurie co-authored by Grzegorz Wypych — Society relies so heavily on technology that the number …
Feeling as if you should better understand special purpose acquisition vehicles – or SPACS — than you do? You aren’t alone.
Like most casual observers, you’re probably already aware that Paul Ryan now has a SPAC, as does baseball executive Billy Beane and Silicon Valley stalwart Kevin Hartz. You probably know, too, that entrepreneur Chamath Palihapitiya seemed to kick off the craze around SPACS — blank-check companies that are formed for the purpose of merging or acquiring other companies — in 2017 when he raised $600 million for a SPAC. Called Social Capital Hedosophia Holdings, it was ultimately used to take a 49% stake in the British spaceflight company Virgin Galactic.
But how do SPACS come together in the first place, how they work exactly, and should you be thinking of launching one? We talked this week with a number of people who are right now focused on almost nothing but SPACs to get our questions — and maybe yours, too — answered.
First, why are these things suddenly spreading like weeds?
Kevin Hartz — who we spoke with after his $200 million blank-check company made its stock market debut on Tuesday — said their popularity ties in part to “Sarbanes Oxley and the difficulty in taking a company public the traditional route.”
Troy Steckenrider, an operator who has partnered with Hartz on his newly public company, said the growing popularity of SPACs also ties to a “shift in the quality of the sponsor teams,” meaning that more people like Hartz are shepherding these vehicles versus “people who might not be able to raise a traditional fund historically.”
Indeed, according to the investment bank Jefferies, 76% of last year’s SPACs were sponsored by industry executives who “typically have public company experience or have sold their prior business and are seeking new opportunities,” up from 65% in 2018 and 32% in 2017.
Don’t forget, too, that there are whole lot of companies that have raised tens and hundreds of millions of dollars in venture capital and whose IPO plans may have been derailed or slowed by the COVID-19 pandemic. Some need a relatively frictionless way to get out the door, and there are plenty of investors who would like to give them that push.
How does one start the process of creating a SPAC?
The process is really no different than a traditional IPO, explains Chris Weekes, a managing director in the capital markets group at the investment bank Cowen. “There’s a roadshow that will incorporate one-on-one meetings between institutional investors and the SPAC’s management team” to drum up interest in the offering.
At the end of it, institutional investors like mutual funds, private equity funds, and family offices buy into the offering, along with a smaller percentage of retail investors.
Who can form a SPAC?
Basically anyone who wants to create one and who can persuade shareholders to buy its shares.
These SPACs all seem to sell their shares at $10 apiece. Why?
Easier accounting? Tradition? It’s not entirely clear, though Weekes says $10 has “always been the unit price” for SPACs and continues to be, with the very occasional exception, such as with Bill Ackman’s Pershing Square Capital Management.
Last month it launched a $4 billion SPAC that sold units for $20 each.
Have SPACS changed structurally over the years?
Funny you should ask! This gets a little more technical, but when buying a unit of a SPAC, institutional investors typically get a share of common stock and a warrant or a fraction of a warrant. A warrant is security that entitles the holder to buy the underlying stock of the issuing company at a fixed price at a later date; warrants are used as deal sweeteners to keep investors involved with a company.)
Earlier in time, when a SPAC announced the company it planned to buy, institutional investors in the SPAC — who had to sign NDA-type agreements — would vote yes to the deal if they wanted to keep their money in, and no to the deal if they wanted to redeem their shares and get out. But sometimes investors would team up and threaten to torpedo the deal if they weren’t given founder shares or other preferential treatment. (“There was a bit of bullying in the marketplace,” says Weekes.)
Regulators have since separated the right to vote and the right to redeem one’s shares, meaning investors today can vote ‘yes’ or ‘no’ and still redeem their capital, making the voting process more perfunctory and enabling most deals to go through as planned.
Does that mean SPACs are more safe? They haven’t had the best reputation historically.
They’ve “already gone through their junk phase,” suspects Albert Vanderlaan, an attorney in the tech companies group of Orrick, the global law firm. “In the ’90s, these were considered a pretty junky situation,” he says. “They were abused by foreign investors. In the early 2000s, they were still pretty disfavored.” Things could turn on a dime again, he suggests, but over the last couple of years, the players have changed for the better, which is making a big difference.
How much of the money raised does a management team like Hartz and Steckenrider keep?
The rough rule of thumb is 2% of the SPAC value, plus $2 million, says Steckenrider. The 2% roughly covers the initial underwriting fee; the $2 million then covers the operating expenses of the SPAC, from the initial cost to launch it to legal preparation, accounting, and NYSE or NASDAQ filing fees. It’s also “provides the reserves for the ongoing due diligence process,” he says.
Is this money like the carry that VCs receive, and do a SPAC’s managers receive it no matter how the SPAC performs?
Yes and yes.
Here’s how Hartz explains it: “On a $200 million SPAC, there’s a $50 million ‘promote’ that is earned at $10 a share if the transaction consummates at $10 a share,” which, again, is always the traditional size of a SPAC. “But if that company doesn’t perform and, say, drops in half over a year or 18-month period, then the shares are still worth $25 million.”
Hartz calls “egregious,” though he and Steckenrider formed their SPAC in exactly the same way, rather than structure it differently.
Says Steckrider, “We ultimately decided to go with a plain-vanilla structure [because] as a first-time spec sponsor, we wanted to make sure that the investment community had as as easy as a time as possible understanding our SPAC. We do expect to renegotiate these economics when we go and do the [merger] transaction with the partner company,” he adds.
From a mechanics standpoint, what happens right after SPAC has raised its capital?
The money is moved into a blind trust until the management team decides which company or companies it wants to acquire. Share prices don’t really move much during this period as no investors know (or should know, at least) what the target company will be yet.
Does a $200 million SPAC look to acquire a company that’s valued at around the same amount?
No. According to law firm Vinson & Elkins, there’s no maximum size of a target company — only a minimum size (roughly 80% of the funds in the SPAC trust).
In fact, it’s typical for a SPAC to combine with a company that’s two to four times its IPO proceeds in order to reduce the dilutive impact of the founder shares and warrants.
In the case of Hartz’s and Steckenrider’s SPAC (it’s called “one”), they are looking to find a company “that’s approximately four to six times the size of our vehicle of $200 million,” says Harzt, “so that puts us around in the billion dollar range.”
Where does the rest of the money come from if the partner company is many times larger than the SPAC itself?
It comes from PIPE deals, which, like SPACs, have been around forever and come into and out of fashion. These are literally “private investments in public equities” and they get tacked onto SPACs once management has decided on the company with which it wants to merge.
It’s here that institutional investors get different treatment than retail investors, which is why some industry observers are wary of SPACs.
Specifically, a SPAC’s institutional investors — along with maybe new institutional investors that aren’t part of the SPAC — are told before the rest of the world what the acquisition target is under confidentiality agreements so that they can decide if they want to provide further financing for the deal via a PIPE transaction.
The information asymmetry seems unfair. Then again, they’re restricted not only from sharing information but also from trading the shares for a minimum of four months from the time that the initial business combination is made public. Retail investors, who’ve been left in the dark, can trade their shares any time.
How long does a SPAC have to get all of this done?
It varies, but the standard seems to be around two years.
What do you call that phase of the deal after the partner company has been identified and agrees to merge, but before the actual combination?
That’s called De-SPACing and during this stage of things, the SPAC has to obtain shareholder approval through that vote we talked about, followed by a review and commenting by the SEC.
Toward the end of this stretch — which can take 12 to 18 weeks — bankers aretaking out the new operating team and, in the style of a traditional roadshow, getting the story out to analysts who cover the segment so when the combined new company is revealed, it receives the kind of support that keeps public shareholders interested in a company.
Will we see more people from the venture world like Palihapitiya and Hartz start SPACs?
So far, says Weekes, he’s seeing less interest from VCs in sponsoring SPACs and more interest from them in selling their portfolio companies to a SPAC. As he notes, “Most venture firms are typically a little earlier stage investors and are private market investors, but there’s an uptick of interest across the board, from PE firms, hedge funds, long-only mutual funds.”
That might change if Hartz has anything to do with it. “We’re actually out in the Valley, speaking with all the funds and just looking to educate the venture funds,” he says. “We’ve had a lot of requests in. We think we’re going to convert [famed VC] Bill Gurley from being a direct listings champion to the SPAC champion very soon.”
In the meantime, Hartz says his SPAC doesn’t have a specific target in mind yet. But he does takes issue with the word “target,” preferring instead “partner” company.
“A target sounds like we’re trying to assassinate somebody.”
