SV Academy just landed $9.5 million to offer tuition-free training that puts people in tech jobs

When you live in Silicon Valley, it feels like nearly everyone works in tech and that entry into the industry is wide open. Of course, the reality is very different. Even as software eats the world, not everyone has the training or connections to land a high-paying job in either the traditional tech industry or with a company that’s actively embracing its digital future.

In fact, it would be challenging to interest an executive recruiter in someone who doesn’t have a tech background and didn’t go to college, yet a company called SV Academy is doing just that. According to cofounder and CEO Rahim Fazal, the nearly two-and-a-half-year-old, Bay Area company is currently helping 100 people every 30 days — or 1,200 per year — land jobs at companies like SurveyMonkey, Palo Alto Networks, and PayPal.

Did we mention that it costs these job candidates nothing, that instead employers pay SV Academy between $12,000 to $15,00 per hire?  All the prospects really need to do is convince SV Academy that they have the grit required to take a 12-week, tuition-free training program that teaches human-centered skills that place these individuals in sales roles, as well as that they will embrace a year of ongoing training and mentorship for a year after graduating.

It sounds like a great deal, and it is, which is why SV Academy says it has more interest than it can handle. Fazal tells us that the company, which received 1,000  applications over eight months in its first year of operations, is now receiving 1,000 applications a week from people who’ve largely heard of the company through word of mouth.

Because it’s focused on grooming candidates who are serious about developing new careers (and will stay in their jobs), SV Academy is loath to scale up to accommodate that kind of demand. Still, a new round of funding should help widen the funnel a bit. Until recently, the company was backed by $2 million that it raised a couple of years ago from Bloomberg Beta, Rethink Education, Precursor Ventures, Uprising Ventures, 500 Startups and WTI.

The money was enough for SV Academy to achieve profitability and get to the point of placing employers on a waiting list. But with demand beginning to more seriously outpace its supply of candidates, SV Academy recently hit the market again, sharing exclusively that it has just closed on $9.5 million in Series A funding led by Owl Ventures with participation from Kapor Capital, Strada Education Network, and several earlier backer participating, namely Bloomberg, Rethink, and Uprising.

It isn’t the first time that Fazal has started a company that has taken off. He cofounded a company a decade ago that sold to Oracle, where he spent the next two and a half years. But SV Academy is even closer to his heart, given that he is exactly the kind of person the SV Academy wants to lift up — someone smart but lacking resources. Fazal himself grew up in government housing. He didn’t go to college. He knows firsthand that with determination and right amount of guidance and support, apparent obstacles like a lack of financial stability or a fancy degree can fall away.

Fazal also recognizes the importance of having the right cofounder, which he seems to have landed on with Joel Scott, who is also the company’s COO. A Stanford-trained lawyer, Scott was previously VP of operations at Hewlett Packard, and according to Fazal has trained upwards of 500 SaaS salespeople since college.

Indeed, Scott played a major role in creating SV Academy’s curriculum, which is very focused on training people for SaaS jobs (for now) and that is entirely virtual, from the 12-week-training period, to the coaching that comes afterward. The reason: it enables it to reach students in the U.S. wherever they may be, and whatever their experience might be.

Though some of the applicants who it accepts are college graduates, many are also “working full-time jobs, or they’re caretakers, and it’s impossible for them to drive into the city several times a week for classes,” Fazal explains.

Whatever the case, it seems to be working. Fazal says that 100% of the individuals who complete the program are not only receiving median job offers of $79,000 plus benefits and, in many cases, equity, but 70% of them are also receiving promotions within their first year. Yes, the law of small numbers is a factor, but it’s also easy to understand investors’ enthusiasm for what they are seeing — including the cautious approach SV Academy is taking to expanding.

“Real transformation is difficult,” says Fazal. “You can’t create outcomes like this by throwing software at the problem.”

Above, left to right: Joel Scott and Rahim Fazal of SV Academy

DoorDash double downs on controversial pay model

There’s seemingly no end in sight for DoorDash’s compensation model where it subsidizes driver wages with customer tips. The mildly bright side, however, is that DoorDash is now providing more transparency after each completed delivery, DoorDash CEO Tony Xu wrote in a blog post today.

“With our current pay model, Dashers see a guaranteed minimum — including tips — prior to accepting a delivery,” Xu wrote. “The guaranteed minimum is based on the estimated time and effort required to complete the delivery. Providing this guarantee upfront means that Dashers are more likely to accept all kinds of deliveries because they know what their earnings will be even if the customer provides little or no tip.”

That means DoorDash’s base pay is sometimes just $1.

“Talking about transparency is good,” labor rights group Working Washington said in a statement to TechCrunch. “And admitting you pay $1/job is better than denying it. But $1 is still $1.”

In light of pay controversies at Instacart, DoorDash and Postmates, Working Washington formed the Pay Up Campaign, which unites thousands of workers across all those gig economy platforms.

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“They continue to subtract tips from worker pay,” the organization said in its statement. “And they continue to mislead customers about where their tips are going. When a customer tips more, DoorDash pays less — in other words, the customer is tipping the company.”

Despite what DoorDash said in its blog post about what workers want, the Pay Up campaign says it wants a minimum pay floor of $15 per hour plus expenses for time with an active job, tips, and a detailed breakdown of pay.

Freemium’s public moment

Slack’s recent direct listing marks a momentous 15-month stretch for freemium SaaS businesses. With Slack joining the public company ranks, six companies will have tapped the public markets since Dropbox’s IPO last March. This group of companies now has a collective market value of more than $50 billion.

When Zoom filed its public S-1, the tech industry fawned over the company’s financial profile. Here was a company growing revenue over 100% that had somehow managed to be cash flow positive. Conventional wisdom among many Silicon Valley investors has recently been that profits and rapid growth are mutually exclusive.

Uber and other high-growth tech companies aspire to be the next Amazon, foregoing profits into the foreseeable future to establish a dominant market position. This land grab mentality has held sway with most of the SaaS businesses that go to market with a traditional enterprise sales force. In contrast, the recent crop of public freemium businesses show they can actually make money while sustaining attractive growth rates.

Recent IPOs

How can this flavor of enterprise software business run so much more efficiently than their traditional enterprise brethren? The answer heavily lies in their approach to sales and marketing. Despite similar growth rates, traditional enterprise SaaS businesses spend an average of 10% more of their revenue on sales and marketing than their freemium comparables.

Freemium vs. enterprise: Different paths to sales success

A common criticism of freemium businesses is that their retention rates dramatically lag those of traditional enterprise software businesses. Customer relationships for freemium businesses usually begin online. An individual employee wants to use a product for her/his own productivity and simply charges a credit card to pay for a subscription. In contrast to larger-ticket enterprise relationships with multiple stakeholders that tend to renew at 90%+ annually, the retention profile of these individual users often resembles consumer subscription businesses — usually in the 60-80% range. When it comes time to renew the subscription a year later, the person may have changed jobs, changed credit cards or only used the product episodically.

It’s less well understood that many of these business models are evolving in real time as they leverage widespread individual adoption to establish broader enterprise customer relationships. When sales reps for freemium products call on enterprise buyers, they usually have hundreds, or even thousands, of their employees already using the product individually or in small teams.

Their products and brands are already widely known and loved, with power-users clamoring for broader internal support and acting as advocates in the sales process. This internal validation makes it much easier to convince an enterprise buyer that the product will deliver compelling value to end users. While some hands-on sales support is usually required to land a contract over $5,000, this job can be done by less-experienced and lower-cost sales reps. If you visit the offices of freemium businesses and ask to walk their sales floor, you’ll see a sea of millennials closing sizeable contracts over the phone.

Legacy enterprise players have historically successfully fended off competition from freemium businesses by accusing them of not being “enterprise-grade” technology. It can take years to build out robust security infrastructure, deep integration into other systems and administration and reporting capabilities, all of which are needed in the enterprise procurement process. This was a muscle that freemium businesses, whose product orientation was around end-user design rather than back-end infrastructure capabilities, needed to build. They also had to build sales motions to navigate the longer, complex sales cycles that come with six and seven-figure annual recurring revenue (ARR) deals.

However, the financial results of these public freemium companies show just how well this is now working, and there are many more private companies following their lead.

Freemium’s enterprise traction

Most public companies don’t report granular renewal rates for their larger enterprise relationships, but the unit economics of these businesses are incredibly compelling.

Lucid Software now has more than 3,000 enterprise customers, which generates more than 60% of the company’s revenue, up from just 350 and 15% when we invested in the company three years ago. The logo renewal rate for their 3,000+ enterprise customers is more than 95% and net revenue retention is more than 130% annually, because end user seat growth more than outstrips any customer losses.

At Spectrum Equity, we’re watching with interest as these early freemium leaders emerge as successful public companies and the broader industry better understands how these business models work. Since leading SurveyMonkey’s first financing more than 10 years ago, Spectrum has invested in Lucid Software, Bitly, Litmus and Prezi, which are all charting a similar course from freemium online through to the enterprise. A number of Spectrum’s content businesses, such as Lynda.com, Teachers Pay Teachers, Headspace, DataCamp, Offensive Security and Digital Marketing Institute, have also successfully built hybrid individual and enterprise distribution strategies.

We believe companies that create the most compelling end-user experiences will win over the long term, and this trend of the consumerization of enterprise technology has only just begun.

(Note from Spectrum Equity: The specific companies identified above may not represent all of Spectrum’s investments, and no assumptions should be made that any investments identified were or will be profitable. View the complete list of our portfolio companies.)

Apple Music surpasses 60 million subscribers

Today’s major Apple news may be the departure of its design guru Jony Ive, but the even as the company stomachs the executive loss, their software plows ahead. Today, in an interview with French news site Numerama, Apple honcho Eddy Cue revealed that the number of Apple Music subscribers has now climbed to 60 million.

The company seems to give updates every time it surpasses another additional 10 million subscribers, we last heard that they had crossed the 50 million mark back in April.

Now, the company’s music service is well past the halfway market in its mission to surpass Spotify which currently has 100 million subscribers.

Police body-cam maker Axon says no to facial recognition, for now

Facial recognition is a controversial enough topic without bringing in everyday policing and the body cameras many (but not enough) officers wear these days. But Axon, which makes many of those cameras, solicited advice on the topic from and independent research board, and in accordance with its findings has opted not to use facial recognition for the time being.

The company, formerly known as Taser, established its “AI and Policing Technology Ethics Board” last year, and the group of 11 experts from a variety of fields just issued their first report, largely focused (by their own initiative) on the threat of facial recognition.

The advice they give is unequivocal: don’t use it — now or perhaps ever.

More specifically, their findings are as follows:

  • Facial recognition simply isn’t good enough right now for it to be used ethically.
  • Don’t talk about “accuracy,” talk about specific false negatives and positives, since those are more revealing and relevant.
  • Any facial recognition model that is used shouldn’t be overly customizable, or it will open up the possibility of abuse.
  • Any application of facial recognition should only be initiated with the consent and input of those it will affect.
  • Until there is strong evidence that these programs provide real benefits, there should be no discussion of use.
  • Facial recognition technologies do not exist, nor will they be used, in a political or ethical vacuum, so consider the real world when developing or deploying them.

The full report may be read here; there’s quite a bit of housekeeping and internal business, but the relevant part starts on page 24. Each of the above bullet points gets a couple pages of explanation and examples.

Axon, for its part, writes that it is quite in agreement: “The first board report provides us with thoughtful and actionable recommendations regarding face recognition technology that we, as a company, agree with… Consistent with the board’s recommendation, Axon will not be commercializing face matching products on our body cameras at this time.”

Not that they won’t be looking into it. The idea, I suppose, is that the technology will never be good enough to provide the desired benefits if no one is advancing the science that underpins it. The report doesn’t object except to advise the company that it adhere to the evolving best practices of the AI research community to make sure its work is free from biases and systematic flaws.

One interesting point that isn’t always brought up is the difference between face recognition and face matching. Although the former is the colloquial catch-all term for what we think of as being potentially invasive, biased, and so on, in the terminology here it is different from the latter.

Face recognition, or detection, is just finding the features that make up a face in the picture — this can be used by a smartphone to focus its camera or apply an effect, for instance. Face matching is taking the features of the detected face and comparing it to a database in order to match it to one on file — that could be to unlock your phone using Face ID, but it could also be the FBI comparing everyone entering an airport to the most wanted list.

Axon uses face recognition and tracking to process the many, many hours of video that police departments full of body cams produce. When that video is needed as evidence, faces other than the people directly involved may need to be blurred out, and you can’t do that unless you know where the faces are. (Update: This paragraph originally stated that Axon was using a “lesser form of face matching,” which matches faces within videos but not with any central database, that it calls face re-identification. In fact this technology is not currently deployed commercially and is only in the research phase.)

That particular form of the technology seems benign in its current form, and no doubt there are plenty of other applications that it would be hard to disagree with. But as facial recognition techniques grow more mainstream it will be good to have advisory boards like this one keeping the companies that use them honest.