Author: azeeadmin

Building a B2B AI startup is hard enough between struggling to obtain training data and fighting with major tech companies to secure talent. Building a B2B AI startup held to the well-established software-as-a-service (SaaS) metrics is even harder. While many AI businesses deliver value via software monetized by a recurring subscription like their SaaS counterparts, the similarities between the two types of businesses end there.

AI startups are a different animal

SaaS products built without data and AI offer generalized solutions to their customers. AI businesses more closely resemble a services business or consultancies because they provide solutions that become tailored to that customer’s specific needs. Like services providers or consultants, an AI product improves as it knows a customer better (as in, as it collects more data from customers with continued usage), and as it serves a broader customer base, from which it can collect best practices and make better predictions over a bigger data set.

Services revenue has been the antithesis of venture-style growth because it yields lower margins and lacks repeatability and scalability; as your services business brings on more customers, you will need to scale headcount accordingly to support those accounts, which keeps margins low. Palantir, a big data analytics unicorn, is one company mired in services demands. Unlike services providers, AI businesses have the potential to deliver that targeted and greater ROI at scale.

AI businesses are not scalable right out of the gate: AI models take time and require data to train. Moreover, not all AI businesses will scale. Here are the metrics we use to tell the difference early on.

AI metrics

Intervention ratio
Hype will make enterprise customers trigger-happy to pilot AI solutions, but at the end of the day, enterprise buyers buy the best solution available to address their problems and don’t care whether that solution comes in the form of SaaS software, a consultancy or an AI product. It is very difficult to build a high-performing MVP version of an AI model without data from customers. In order to demonstrate value right out of the box and be competitive against other vendors, you might automate which processes you can right off the bat using a rules engine, and provide a human operator to perform the rest of the work while simultaneously labeling the collecting data in order to train the AI.

As the AI improves over time, the human operator will offload more of the work and only jump in to intervene when the AI falls below a predetermined accuracy or confidence threshold. This enables you to serve an increasing number of customers with a limited number of staff. Lilt, which provides machine translation for enterprise, uses professional translators in this role. The translation AI automatically translates a text excerpt from one language to another. A human translator goes over the text looking for errors in translation or contextual corrections. As the translation AI improves, the human translator will have to make fewer corrections per translation task. More generally, the ratio of human interventions over total automated tasks should be decreasing.

ROI curve
As with SaaS products, exactly how that compounding AI performance increase is tied to bringing value for the customer is key to the startup’s long-term stickiness. The key difference with AI products is once the AI’s performance ramps up, it could very quickly exhaust all low-hanging fruit opportunities. If the AI cannot continue to provide value to the customer, the difference in value from one renewal cycle to the next may seem stark to the customer, who may decide to not renew.

Choosing the right applications of AI to enable long-term payoffs and avoiding hitting a wall with ROI is key. Typically, applications that improve the customer’s bottom line face finite opportunities for improvement, and applications that improve the customer’s top line have no ceiling on opportunities to grow. For example, once an AI improves the operating efficiency of a production line to the point where it is rate-limited by the time it takes for the raw materials to chemically react, the AI can no longer find value for the customer for that specific application.

There are only so many opportunities to take out costs before you are constrained by the laws of physics. An AI that helps customers find new opportunities for revenue like, Constructor.io, which provides AI-powered site search as a service and helps customers such as Jet.com increase cart conversions, will not hit that wall.

You should closely track the cumulative ROI for each customer over time to make sure the curve does not plateau and lead the customer to churn. Sometimes the long-term application is harder to sell because the value is difficult to demonstrate immediately, and you might get a foot in the door with the cost take-out value proposition. Understanding its ROI curve would enable you to design a longer contract period so that the AI has time to ramp on new problems before it exhausts the initial application. To ensure customer retention, you should make sure that the customer ROI increases over time and not plateau or taper off.

Rev-up costs
Deploying an AI product is a complicated process that leaves you at the mercy of each customer’s idiosyncratic tech stack and org chart. AI needs data to train, so an AI product may take more time than a SaaS product to deliver value. Acquiring or creating data for the AI model, integrating the product into the customer’s tech stack and workflows and getting the product to deliver value before the model is sufficiently trained on the customer’s data may significantly impact your own bottom line.

Many sectors have only recently begun to digitize, and valuable data might be in difficult-to-extract formats, such as handwritten notes, unstructured observation logs or PDFs. In order to capture this data, you may have to spend significant manpower on low-margin data preparation services before AI systems can be deployed. Depending on how the data is captured and organized, your deployment engineer may have to build new integrations to a data source before the model can be fully functional.

The way data is structured might also vary from one customer to the next, requiring AI engineers to spend additional hours normalizing the data or converting it to a standardized schema so the AI model can be applied. Over time, these costs may decrease as you build up a library of reusable integrations and ETL pipelines.

Products sold by SaaS companies either work or they don’t. AI performance is not binary; it works less well out of the box and improves with more data. Each application and each customer will accept a different minimum algorithmic performance (MAP). The deployment process should make sure to get the product to that customer’s specific MAP, and you might revert back to Wizard of Oz stop-gap approaches to deliver MAP until the model can perform at MAP on its own.

If you are selling to customers that allow you to pool anonymized data or use a model trained on their data with other customers, the AI product will perform better “out of the box” with each subsequent customer. Inside sales customers, for example, can get immediate suggestions on how to optimally target a sales lead using its sales acceleration platform thanks to that data pooled from its customer network.

AI products incur more significant rev-up costs than a typical SaaS product rollout and may have as much impact on margins as customer acquisition costs (CAC). You should carefully track how much time these rollouts and ramp-ups take, and how much it costs for each new customer. If there are true data network effects, these numbers should decrease over time.

Data moat
Unlike SaaS businesses that compete on new features, AI startups have an opportunity to build long-term defensibility. The AI startups that can scale will kick off a virtuous loop where the better the product performs, the more customers come on board to contribute and generate data, which improves the product’s performance. This reinforcement loop builds a compounding defensibility that was previously unheard of for SaaS businesses.

It’s too simplistic to merely aim for the largest volume of data. A defensible data strategy takes into account whether the appropriate data is being collected at a pace that is appropriate for the problem at hand. Ask yourselves these questions about your data to determine where you can strengthen your data strategy on the following dimensions:

• Accessibility: how easy was it to get?
• Time: how quickly can the data be amassed and used in the model?
• Cost: how much money is needed to acquire and/or label this data?
• Uniqueness: is similar data widely available to others who could then build a model and achieve the same result?
• Dimensionality: how many different attributes are described in a data set?
• Breadth: how widely do the values of attributes vary, such that they may account for edge cases and rare exceptions?
• Perishability: will the data be useful for a long time?

AI models perform better with more data, but that performance may plateau over time. You should take care to track the time and volume of data necessary to achieve an incremental unit of value for your customer, to make sure that the data moat continues to grow. In short, how much time, and how much data, would a copycat need to match your level of performance?

SaaS metrics aren’t enough

The higher upfront work necessary to launch an AI business means that most will look more like services businesses or will appear to underperform when they are evaluated under the framework of SaaS metrics. A small subset of AI startups will resemble SaaS businesses from the beginning, before AI is deployed in the product. In order to collect data for their AI models, some businesses first sell SaaS workflow tools and can even achieve meaningful revenue from that workflow tool alone. By SaaS metrics, that company may be blowing the competition out of the water. Without the reinforcement loop generating a compounding volume of data and an increasingly powerful AI over time, however, that company’s product remains vulnerable to copycats and will eventually be commoditized.

AI metrics captures this difference. AI offers the opportunity to deliver the customized and specialized ROI of a services business with the scalability of software, with the ability to defend against copycats. The high start-up costs of this approach to company-building may mean you will realize smaller profits and build the company prioritizing different elements than what has worked before. Vertical AI is so new as a category that many companies are not yet tracking these metrics, so we don’t yet have enough data points to establish benchmarks. In the meantime, these numbers will serve as helpful barometers for you to monitor the health and performance of this new type of company.

Recent headlines at TechCrunch and elsewhere have been filled with news about data breaches, data misuse and other data-related scandals. But has that actually affected how consumers think about their personal data?

A new report from Salesforce Reserach sheds some light on this question. In a survey of 6,723 individuals globally, Salesforce found that 59 percent of of respondents believe their personal information is vulnerable to security breach, while 54 percent believe that the companies with that data don’t have their best interests in mind.

Respondents also said that these feelings will affect their choices as consumers — for example, 86 percent said that if they trust a company, they’re more likely to share their experiences, and that number goes up to 91 percent among millennials and Gen Zers.

The findings seem similar to (if more general than research from Pew showing that Americans have become more cautious and and critical in how they use Facebook.

At the same time, it sounds like people do want some degree of personalization in their marketing — the same personalization that requires data. Eighty-four percent of respondents said they want to be treated “like a person, not a number,” and 54 percent said current marketing messages aren’t as relevant as they’d like.

Salesforce says that while this might seem like a paradox, personalization and trust are not mutually exclusive. To illustrate this, it notes that 86 percent of respondents said they’re more likely to trust a company with their personal information if it explains how that information leads to a better customer experience, and 68 percent said they’re more likely to trust companies with that info if they’ll use it to fully personalize the customer experience.

“With technologies like AI driving more personalized customer experiences, customer trust needs to be grounded in a deeper understanding of the technologies’ value,” the report says. “Among millennials and Gen Zers, 91% are more likely to trust companies with their persona information if they explain how its use will deliver a better experience — suggesting that strict security and privacy protocols alone may not be enough.”

You can read the full research brief here.

Recent headlines at TechCrunch and elsewhere have been filled with news about data breaches, data misuse and other data-related scandals. But has that actually affected how consumers think about their personal data?

A new report from Salesforce Reserach sheds some light on this question. In a survey of 6,723 individuals globally, Salesforce found that 59 percent of of respondents believe their personal information is vulnerable to security breach, while 54 percent believe that the companies with that data don’t have their best interests in mind.

Respondents also said that these feelings will affect their choices as consumers — for example, 86 percent said that if they trust a company, they’re more likely to share their experiences, and that number goes up to 91 percent among millennials and Gen Zers.

The findings seem similar to (if more general than research from Pew showing that Americans have become more cautious and and critical in how they use Facebook.

At the same time, it sounds like people do want some degree of personalization in their marketing — the same personalization that requires data. Eighty-four percent of respondents said they want to be treated “like a person, not a number,” and 54 percent said current marketing messages aren’t as relevant as they’d like.

Salesforce says that while this might seem like a paradox, personalization and trust are not mutually exclusive. To illustrate this, it notes that 86 percent of respondents said they’re more likely to trust a company with their personal information if it explains how that information leads to a better customer experience, and 68 percent said they’re more likely to trust companies with that info if they’ll use it to fully personalize the customer experience.

“With technologies like AI driving more personalized customer experiences, customer trust needs to be grounded in a deeper understanding of the technologies’ value,” the report says. “Among millennials and Gen Zers, 91% are more likely to trust companies with their persona information if they explain how its use will deliver a better experience — suggesting that strict security and privacy protocols alone may not be enough.”

You can read the full research brief here.

Powered Clothing is not a robotic exoskeleton. Not in the standard understanding of the term, at least. Seismic CEO Rich Mahoney wants to make that much clear. 

“Exoskeletons are what the public understands, with regards to wearable robotics,” Mahoney told TechCrunch in an interview ahead of the product’s debut onstage this week at Disrupt. “We’re absolutely not an exoskeleton. Part of our insight we saw is that everyone is wearing clothing and no one is wearing robots.”

Around the TechCrunch office, we’ve taken to calling the product “robotic underwear,” something that paints a bit more accurate a picture of what you’re getting yourself into. The product is designed to offer similar assistive functionality to what you get with SuitX or Ekso, albeit on a smaller scale, in a more discreet package.

“Our first product is integrating what we call intelligent wearable strength, focused on the core,” says Mahoney. “It symbiotically provides assistant to the hips and lower back to support mobility and posture. There are many people that can use that, but we’re really focusing on where the need is. It’s the broad consumer, wellness market.”

The company’s initial demographic is the aging baby boomer generation — wearers who are still mobile but can use an extra assist from Seismic’s textile-based tendons. It’s a far more subtle take on the company’s original model, a DARPA-funded spin-off of research organization, SRI International.

In those prehistoric days of 2016, the company was known as Superflex, and the product looked something a bit more akin to a bionic wetsuit. These days, the company employs a number designers alongside its robotics staff to deliver a wearable that’s, well, a bit more wearable.

“We understood that we were an apparel company and really saw a consumer-facing brand as being the way we wanted to approach the market,” says Mahoney. “We really have to think about what people understand and how we can bring functionality to clothing, while maintaining comfort, aesthetic and emotion. Really, our company is a fusion of apparel and robotics.”

Powered Clothing will come to market in limited quantities this year at a price “similar to high-end, premium apparel.”

Yesterday at TechCrunch Disrupt, Jina Choi, the longtime head of the SEC’s San Francisco unit, declined to confirm that her agency is investigating Tesla CEO Elon Musk for his now infamous tweet about securing funding for a take-private maneuver.

Choi did pull back the curtain substantially with regard to how the agency — which has never worked harder as it relates to private company investigations — operates.

The uptick in activity is no surprise. As companies linger as private entities for longer periods of time — often raising hundreds of millions, if not billions, of dollars along the way — the SEC has found itself spending more time understanding who the players are, as well as watching them more closely. In fact, while Cho’s 130-person unit covers much more than the Bay Area — its reach extends to Portland, Seattle, Idaho, Montana, and Alaska  — it could easily pour all of its resources into Silicon Valley and San Francisco because it’s so “incredibly busy.”

One of its most famous cases to date has centered on Theranos, the blood-testing company that is right now dissolving but was charged with massive civil fraud by the SEC back in March. It was a case that the SEC spent nearly two years building, and when we talked with Choi about what took so long, she explained how resource intensive the process really is.

She also talked about how much of the agency’s tips come through media accounts (the WSJ famously blew the covers off what had gone so wrong at Theranos) versus other means, including the SEC’s whistleblower program.

And we talked about how the SEC determines settlements in those frequent cases where it settles with a party it has charged with fraud. Recently, for example, the SEC settled with Bay Area investor Mike Rothenberg, who it accused of misappropriating millions of dollars of investor capital. Rothenberg didn’t admit to wrongdoing, but he did agree to be banned from the investment advisory and brokerage business for five years — a move that some industry observers thought didn’t go far enough, while others viewed as onerous.

As it happens, barring people from the industries in which operate is about as extreme a punishment as the SEC can deliver, she explained. “We’re a civil law enforcement agency, so that means that we’re we don’t have the power to take away people’s liberties . . . We can’t put people in jail . . . but one of our most impactful remedies is to bar people from [their] profession” when such a penalty is deemed appropriate.

In fact, she continued, “a lot of times [the target of the SEC’s investigations will say] ‘I can pay a bigger penalty,’ ‘I’ll pay you more money; just don’t ban me from this industry.’ And I think that that’s where we have a tough negotiation, because I think that’s where we can have our greatest impact.”

If you’re interested in how the SEC operates, as well as its evolving stance on ICOs and main street investors accessing private companies, you can learn a lot more by watching here.

A decade from now, ride-hailing will be less than 50 percent of Uber’s business, in terms of transactions, CEO Dara Khosrowshahi said Thursday at Disrupt SF.

The onstage prediction is in line with recent moves by Uber and its CEO to be part of, and make money from, all the different ways people might move within an urban environment. In Khosrowshahi’s one year as CEO, Uber has made a multimillion-dollar acquisition of JUMP bikes, launched UberRENT, announced plans to launch a dockless electric scooter service and launched a new modalities organization created to figure out what this multi-modal future might look like for Uber.

Ride hailing, the company’s first and primary revenue driver, and its delivery app UberEATS, will both be enormous in the future, Khosrowshahi said. But the long-term vision, and one that is already in motion, is to move away from travel that relies on passenger cars.

“We want to be the Amazon of transportation,” Khosrowshahi said. “And hopefully, 10 years from now no one in the audience is going to own a car.”

Dockless scooters and bikes — and someday even flying taxis — are central to that plan.

“I’m actually very, very bullish on personal individual electric vehicles,”Khosrowshahi said. “We’ve got to deconstruct that car and that’s a big part of the mission going forward.”

You can watch the full video below:

A group of workers at Whole Foods Market are leading an effort to establish a union for the Amazon-owned company’s 85,000+ workforce.

In a letter addressed to Whole Foods employees, the group — members of Whole Foods’ cross-regional committee — wrote that they are “concerned about the direction” of Whole Foods in an Amazon era. The letter outlines several demands, including a $15 minimum wage for all employees, 401k matching, paid maternity leave, lower health insurance deductibles and more.

“We cannot let Amazon remake the entire North American retail landscape without embracing the full value of its team members. The success of Amazon and [Whole Foods] should not come at the cost of exploiting our dedication and threatening our economic stability,” they wrote.

The grocery store chain was acquired by Amazon one year ago in a $13.7 billion deal that sent shockwaves through the e-commerce and brick-and-mortar retail industries. In that 12-month period, the e-commerce giant has implemented changes to the grocery chain’s nearly 500 stores. Amazon Echos have become part of the inventory in some locations and Amazon lockers have shown up to facilitate Amazon.com pick-ups and returns, for example.

The letter, which calls out both Jeff Bezos and Whole Foods’ CEO John Mackey directly, says there will “continue to be layoffs in 2019 and beyond as Amazon aims to aggressively trim our labor force before it expands with new technology and labor models.”

Since the Amazon acquisition, several hundred Whole Foods workers have been laid-off as Amazon infuses “Whole Foods with its efficient, data-driven ethos,” per The Wall Street Journal. Shoppers, however, have saved millions as a result of the shake-up.

In a statement provided to TechCrunch, a representative of Whole Foods said they respect the “individual rights of [their] team members.”

“[We] have an open-door policy that encourages team members to bring their comments, questions and concerns directly to their team leaders,” they said. “We believe this direct connection is the most effective way to understand and respond to the needs of our workforce and creates an atmosphere that fosters open communication and empowerment. We offer competitive wages and benefits and are committed to the growth and success of our team members.”

Amazon provided a virtually identical statement, adding that they encourage anyone concerned about employee treatment to take a tour of one of their fulfillment centers.

Here’s the full letter, obtained by New Food Economy.

 

In an ecosystem enthralled with private capital and delayed public debuts, Bill Gurley has been something of a maverick. The former dot-com equity analyst and long-time partner at Benchmark has pushed hard for companies to go public and “grow up,” including at his portfolio company Uber, where he was formerly a board member.

Earlier this year, he noted that “it’s cool to go public again,” and now we are starting to see the fruits of Benchmark’s labors. Over the past 24 hours, two companies – Elastic and Upwork – have submitted their S-1 registration statements to the SEC, and Benchmark is the largest shareholder in both. That follows last year’s IPO for Stitch Fix, where Gurley was the lead investor.

The story of these two public aspirants are certainly divergent. Upwork is the rebranded merger of two companies, Elance and oDesk, which merged in 2013. Benchmark got involved through oDesk, leading a Series B round of investment in the company in 2006, with founding partner Kevin Harvey joining the board. Considering oDesk was founded in 2003, and Elance in 1999, it has certainly been a circuitous route to the public markets for the company.

Elastic, on the other hand, is a relatively rare case of a company going public quite early in its evolution. The startup was founded just a few years ago in 2012 according to Crunchbase, and Benchmark’s Peter Fenton led a $10m series A into the company that same year. Only six years later, the company is heading to the public markets, with a projected unicorn valuation.

While Upwork has certainly been a journey, it’s Elastic that best exemplifies the startup trajectory that I think Gurley has been advocating for the past few years. Given its rapid revenue growth and key ownership of the search engine market, it is doubtful the company would have struggled to raise additional capital from the private markets. Indeed, six years from founding to IPO is more reminiscent of the 1990s, when the IPO was a key early milestone in the development of a startup since private investment was just not available.

The other interesting dynamic here is around capital efficiency. Elastic raised just $162 million in venture capital according to Crunchbase, a surprisingly low number considering its revenues, growth, and valuation. Enterprise startups have been raising more capital over time as sales and marketing costs have soared and the standards required to publicly debut have become more exacting. That capital efficiency is mirrored on the consumer side by Stitch Fix, which had raised just $42 million in venture capital before its IPO.

These are early data points, but it is clear that Gurley’s and Benchmark’s words around capital efficiency and public markets are influencing their advice to their startup boards and leading to very different actions from these founders. It’s a contrast to companies like Palantir and SpaceX, which have seemed to have committed to staying private for as long as possible.

Tiger Global and other crossover hedge funds are also pushing IPOs

Benchmark is not the only company that has had some good S-1 news this week. The lead investor in the other two prominent tech IPOs so far this season — Eventbrite and SurveyMonkey — is Tiger Global, the quiet but prolific crossover hedge fund. The fund owns 21.3% of Eventbrite and 29.3% of SurveyMonkey.

The rise of these crossover funds is driving renewed interest in early public liquidity. Unlike traditional venture firms, which typically have a decade investment horizon (plus frequent multi-year extensions), these hedge funds face greater pressure to get returns on a compressed timeline.

That’s indicative here with Tiger Global. It’s investments in Eventbrite and SurveyMonkey took place in 2013, so it is just roughly five years from investment to IPO. Certainly, the hedge fund targets growth-stage opportunities which have shorter liquidity times in general, yet, the speed of liquidity is still notable even for growth investors.

For an ecosystem that has in many ways avoided the public markets, these changing norms will not just increase the pressure to go public, but may also present challenges for boards where discordant voices may be simultaneously pushing the exec team to stay private or go public. It’s a dynamic that founders are going to have to increasingly think through as they select investors through each of their venture rounds, in order to ensure that every investor is on the same page regarding the timeline for the public markets.

As a speaker I often find myself mumbling into a microphone with little thought about the sound system powering it. While most PAs are massive affairs requiring a soundboard operator and lots of wiring, I’ve also had to hoot into portable PAs, a practice I rarely relish. But who was I to judge the quality of a portable PA system? When JBL asked me to review their new $1,299 JBL Eon One Pro I decided to send it to a real professional, my childhood friend Rick Barr, who helped me tag-team on the review.

The most important reason that Rick liked the Eon One Pro was the built-in battery. Everything else, he said, was icing on the cake.

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Rick is a professional musician, performing shows every weekend, and some weeknights, in a wide variety of venues. His go-to PA is the Bose L1 Model II with the B2 bass unit. It’s a beast in terms of sound quality and immersion, doesn’t take up much floor space, and really soars when used in outdoor environments.

We immediately recognized that a smaller, more portable unit could be extremely useful. He had just recently performed at a new outdoor event that wasn’t well-equipped with power and he had to come up with a makeshift solution. It worked, but the idea of being able to “cut the cord” to avoid all that was certainly appealing.

JBL says you can get up to six hours of battery life from the extended-life lithium-ion. In our tests, he was able to make it through three-hour shows without a problem. Charging it is as simple as plugging in the AC cord to the back. So, in short, we were pleased with the battery performance. Still, going cordless is all well and good, but it’s really the sound that matters. So, let’s take a look at what this unit can do.

The Eon One Pro weighs 37.5 pounds, and it’s all very compact. The 8” subwoofer is right up front, and you fit the 118 dB speaker array directly on top. This, and the two optional spacers, fit nicely in the back of the unit. The overall design of the Eon Pro really is nice. The spacers essentially increase the range of the speaker, so their usefulness is really dependent on your environment.

The 7-channel mixer features 2 Hi-Z inputs, 4 combo ¼” / XLR inputs, a 3.5mm jack, and an RCA input. Each of the 4 combo inputs has controls for volume, treble, bass, and reverb. This allows for very basic mixing, but if you prefer to have more options, it is easy enough to plug in an external mixer and run through that. In our tests, we used the on-board controls.

You can also stream from a mobile device via Bluetooth, or connect directly via USB. Rick connected via his cell phone using Bluetooth and found the overall sound to be extremely good. There is also phantom power for condenser mics and an XLR Pass Thru to other systems, as well as RCA output jacks for a monitor.

So, on to the show. The first venue Rick played in was your typical bar, with a medium-sized square room, wood floors, and a decent crowd. He was able to get set up in just 10 minutes, compared to 20 for my Bose. It took some extra time to adjust levels and once he started playing, just a little more tinkering got him where he needed to be. He did notice that he had to turn the volume up for his Sennheiser 935 mic quite a bit in order to match the guitar level, which leads to an interesting omission: lack of level meters. There are none, so you need to rely solely on your ears to get the right mix.

The speaker did a fine job of filling the room, while the subwoofer provided some nice depth to the overall sound. Rick had some friends out who sat just six feet in front of the speaker who said they weren’t overwhelmed by the volume and others will able to hear the music very clearly outside of the room.

The speaker covers 100 x 50 degrees, and while testing this at his shows, Rick stood slightly behind and to the side. This worked well enough, though in a noisy environment, having a monitor speaker might be helpful. He could hear the music pretty well, but it seems you’d want to be at least 90 degrees on either side, if not a little forward.

The second show we took the Eon One out to was another small bar, fairly narrow but long. It was completely different from the other bar in terms of dimensions, and a really good test of how far the speaker could project. Again, folks sitting up front were just fine with the volume, while people in the back, some 50-60 feet away, could hear it as well (and reported that it sounded very nice).

“I’d played at this venue before but this time, the electrical outlet wasn’t working. The girl at the bar didn’t know how to turn it on. This is something that rarely happens, but if I’d had my Bose or any other kind of amp, I would have been hosed. I hadn’t planned on testing the battery again but in this instance, it saved me,” Rick said.

Given that most offices purchase something like this at some point for broadcasting at meetings or meetups it makes sense to get something that works well for a gigging musician. Rick’s requirements – that this thing be reliable and sound great – is in line with the average desk jockey’s and the built in battery can save the day when it comes to situations where power is unavailable.

Whitney Wolfe Herd, the founder and CEO of social and dating app Bumble, said today that Facebook’s move into dating is a “huge validation, the best thing that could happen to the dating space, maybe ever.” But all the same, the profitable service — which is notable for how it puts women into the driver’s seat for all interactions — has been taking some significant steps in asserting its independence as a multi-faceted social platform in its own right.

That has included not only building its own social graph, but rebuffing acquisitions for now while it continues to grow, and exploring a new wave of online and offline services, such as its Hive networking pop-ups.

One of the big problems with a lot of apps that rely on people tapping networks of people they know to grow and be used is that many of them are built on Facebook’s data, making them dependent on the whims and business priorities of their partner. No so with Bumble.

“We’re really not reliant on Facebook anymore,” she said during an interview this morning at TechCrunch Disrupt in San Francisco, “so [Facebook’s move into dating] isn’t a direct response to us. When we released a non-Facebook login, our registrations went up 40 percent virtually overnight. That’s very telling of who’s coming to [Bumble].” She added that Bumble “skews both younger and older.”

The company today is actually a subsidiary of London-based dating company Badoo, but it was reportedly in talks for an acquisition by Match Group — owner of Tinder, which Wolfe Herd helped co-found before leaving under a controversial cloud — for $1 billion in 2017. But although Bumble’s made a big effort of trying to “clean up” the world of online dating and network for women by letting them have more control over the process (most recent development: the launch of a “snooze” feature to help manage how often you are getting alerts), dating remains a messy business: both Match and Bumble have also been suing each other over trade secrets and other allegations.

While Bumble remains where it has been, under Badoo, the M&A rumors have raised the prospects of just how it would work with other companies down the line.

Facebook has also served as a cautionary tale, said Wolfe Herd, as the company develops its own standalone network independent of third-party data, and considers how it might ever work with other social networking and social media companies in the future.

“Who knows, maybe there is an opportunity for a partnership down the road,” she said. “[But] the drama that Facebook’s gone through over the last few months changes the way you feel about ever partnering or working or being acquired by Facebook, or any of the other big networks,” she said. Shared values, she said, had become a big part of how she evaluates working with another company. 

“We’re not spending our days really thinking about an acquisition,” she followed up quickly after. “I haven’t spent too much time thinking about that. We’re so busy, just focused on taking our future to where we want to go.” 

In the meantime, the company has been doubling down on new services for its community of users.

Among them, Wolfe Herd said that Bumble would be expanding its Hive interactive networking spaces next year. The company in the past has hosted what it calls “pop-up Hives” — hives, referencing the beehive branding that Bumble sports.

These spaces have allowed customers to interact with Bumble in the real world – an idea that makes sense, given the app’s focus on helping people make real life connections.

“Our users have shown us that they want to be a part of our brand in a deeper way, more than just using our product,” Wolfe Herd explained. She said users want to engage with Bumble and its values in the physical world, which is why it first launched its pop-up hives.

“In 2019, we’re rolling out physical Hives,” she said, declining to detail those plans during the on-stage interview.

“They’ll be something no one’s seen in the space, and it won’t be a model that you’ve seen outside the space.”

That seems to indicate these won’t involve copying the typical real-world dating events — like speed dating, for example. Instead, she described the spaces as places where good people would be brought together.

Bumble’s pop-ups have offered a variety of activities, including entertainment, interactive sessions with entrepreneurs and influencers, as well as drinks and snacks.

Wolfe Herd noted, too, that there’s a monetization model attached to these plans, something that’s not necessarily needed, given that Bumble is already a profitable business, based on its subscription model.

Advertising will be a part of this monetization strategy in the physical space, as well as online.