Blinded by the speed of change

My grandfather lived through an incredible period of technological change. He saw the invention of the automobile, the airplane and the rocket. He lived through the dawn of the atomic age and the mainframe computer. He didn’t live long enough to see the PC or the impact it would have on my professional life, but he was around for the creation of a lot of the technology that laid the foundation for what’s happening today.

I’ve been at this for a long time myself. I remember working on an early IBM PC. Later, I accessed the text-based internet via a 300 baud modem. I can recall the earliest days of the world wide web.

My first cell phone was a Motorola brick phone. My first iPhone was the 3. Bottom line is I’ve seen a lot of technological change, and I’ve never seen anything like we’re seeing these past months, weeks and days.

Consider for a moment that ChatGPT 3.5 took the world by storm in December. Last week, while I was on vacation, OpenAI released ChatGPT 4, which OpenAI unabashedly called “state of the art.” This week, we saw the announcement of plug-ins for the internet itself and useful tools like Expedia, WolframAlpha and so many others, suddenly accelerating generative AI in new and exciting directions.

All of this is happening with stunning speed. It feels like we’re living through an inflection point, much like we saw with the first IBM PC, the internet, the web, the iPhone. But this moment of change is happening so fast, we’ve barely got time to process the latest twist before the next iteration comes flying down the chute.

And like those moments we saw with the advent of personal computing, connected computing and mobile computing, you know that something huge is happening, but it’s not clear yet what it will become. At the moment, we know that there is an exciting new technology that can change the way we interact with computers, but we aren’t clear yet how that will play out, any more than we knew how the web or smartphones would transform our lives in ways we really couldn’t imagine in the earliest days.

On a panel last week led by Docker CEO Scott Johnston, Ilan Rabinovich, SVP of product and community at Datadog, talked about the similarities between what we’re seeing now and the early days of the internet.

Blinded by the speed of change by Ron Miller originally published on TechCrunch

Is generative AI really ready for the enterprise?

OpenAI released ChatGPT just a few short months ago, and it’s fair to say that it took the world by storm: It has over 100 million active users already. No wonder, when it can generate human-like, grammatically correct responses. Related technologies can also produce artwork and code by entering a description of what you want, and the tech produces it.

You can even interact with the AI after your initial question, so if you don’t like the output you got or need clarification, you can ask additional questions or make adjustments to your picture or code, so it more closely matches your vision. All of this happens instantly without the help of a subject expert, an artist or a coder.

But none of this comes without issues, which include the sourcing of the data used to train the underlying AI model, the currency of that training data, a lack of permissions to use the source data, bias in the model and, perhaps most importantly, the accuracy of the responses, which are sometimes laughably wrong.

None of this has stopped enterprise software companies from taking the generative AI plunge. These companies see massive commercial potential and a lot of enthusiasm from users and they clearly don’t want to get left behind.

Salesforce, Forethought and Thoughtspot all recently announced betas of their own flavors of generative AI. Salesforce is adding generative AI across the platform. Forethought is aiming at chatbots and Thoughtspot wants to use AI for data querying. Each company took the base technology and added some algorithmic boosters to tune the tech for their platform’s unique requirements.

Microsoft also announced that its OpenAI service aimed at enterprise users on Azure is generally available as a managed service.

Throughout this year you can expect to see many more companies joining in, but the limitations are real, which makes us wonder: Is the technology — as early and raw as it is, no matter how cool it looks on its face — really enterprise ready?

Is generative AI really ready for the enterprise? by Ron Miller originally published on TechCrunch

At Upfront Summit 2023, AI is the omnipresent celebrity

A marching band, a red carpet and a DJ who codes her beats are all things you can get before coffee (and a business card) at the Upfront Summit, one of venture’s most awaited conferences. But not even a marching band couldn’t pull focus away from the true star of the show: AI.

Upfront Summit founder Mark Suster and partners Kerry Bennett and Kobie Fuller even performed a sketch centered around AI. The takeaway? AI is a tempting sector to invest in, but it’s too still early to trust blindly.

This isn’t new; hyped-up technologies often get outsized interest. But the atmosphere is different from what it was in 2021 when investors were throwing billions of dollars at 15-minute grocery delivery companies and web3. Venture dry powder is locked up, deals are getting done slower, and some investors are still licking their wounds from the downturn thus far.

AI is feeling it. According to a TechCrunch analysis of PitchBook data, “generative AI companies aren’t going to even set a local quarterly maximum for fundraising in Q1 2023.” Understanding how recently humbled check-writers are thinking about AI will help tech better understand how to execute moonshot visions.

At Upfront Summit 2023, AI is the omnipresent celebrity by Natasha Mascarenhas originally published on TechCrunch

Will AI receive the same celebrity-fueled hype as crypto once did? It’s complicated

When the crypto world was at its latest peak, celebrities quickly joined the gold rush. Tom Brady started a buzzy NFT business for athletes and entertainers with backing from a16z and Kleiner Perkins, among others. Reese Witherspoon said crypto is here to stay, encouraging “more women to be a part of the conversation.” And Paris Hilton, a longtime crypto enthusiast, reportedly named two of her newest pets “Crypto Hilton” and “Ether Reum.”

As crypto has sputtered and struggled in recent months, the spotlight is now on AI. Will celebrities follow venture’s newest hype train? Buzzy products spun out from OpenAI, such as ChatGPT and GPT-3, are helping to land billions in VC interest. But core differences between AI and crypto may mean that influencers turned venture capitalists may not be as eager to jump.

But does AI even need celebrities touting its many applications?

Will AI receive the same celebrity-fueled hype as crypto once did? It’s complicated by Natasha Mascarenhas originally published on TechCrunch

A VC’s perspective on deep tech fundraising in Q1 2023

Like nearly every other sector, deep tech faced significant headwinds in 2022. As interest rates skyrocketed, deep tech deals, which inherently take more capital than other kinds of software businesses, became less attractive to many VCs and their LPs than lower-risk investments.

For instance, even though quantum computing suddenly became popular in the public markets as D-Wave, Rigetti and IonQ listed in the last year, private investment declined significantly — the sector received just over $600 million in venture capital in 2022, down from $800 million in 2021, according to Crunchbase.

Seasoned investors and operators in different segments of deep tech have been adapting to these changes in real time as the cheap money days dwindle in the rearview. For instance, in this environment, space tech startups would never have been able to raise the kind of money they did in 2021 to be able to deploy the technologies they’re working on today. As Delian Asparouhov, a principal at Founders Fund and the founder of Varda Space Industries, shared last month, it would be impossible to raise the $42 million his startup did in 2021 for its space factory “idea” in today’s market climate.

While some investors will continue to sit on the sidelines as we kick off 2023, it’s important to note that many funds are still sitting on amounts of dry powder like they’ve never had before. That doesn’t mean they or their LPs will be in a rush to deploy that capital, but money will be available to startups that can demonstrate current demand and are realistic about their valuations. As it becomes increasingly difficult to realize big exits in the years ahead, the technologies within deep tech that are transforming entire industries offer some of the only paths to “10x exits.”

These are positive signs for deep tech founders preparing to raise money this year. Another positive note is that some of the logic driving VCs to stay away from deep tech startups in down markets may be unfounded. Our team recently analyzed recent deep tech unicorns to understand how much money it took for them to get to the $1 billion mark. The results reinforced what we knew from experience: Deep tech startups’ capital and time requirements are on par with companies in other sectors. In fact, the median deep tech startup took $115 million and 5.2 years to become a unicorn.

While the space economy will continue to provide numerous opportunities to invest in atoms, there will also be an opportunity to invest in the bits moving atoms across our skies.

With that as a backdrop, let’s look at a few areas where deep tech will find interest from investors in 2023.

Startups moving beyond launch tech in space

While Delian noted correctly that funding for long-term “moon shots” will be tough to find in the current market, I still believe investors will look for startups that are closer to commercialization in the sector. To date, 99% of the total investment in the space tech market has gone to the satellite and launch industries. Now is the time to focus on moving objects around in space rather than just getting them there.

For instance, investors are increasingly interested in solutions that tackle astrodynamics or propulsion to guide the motion of satellites and other spacecraft — for example, AI startups working on ways to simulate scenarios and generate maneuver plans for operators so they can avoid space collisions. Investors are also interested in future machine learning and neural networks use cases for astrodynamics, such as orbit predictions and spacecraft flight modeling.

Space missions also call for hardened software and hardware. As we look toward edge solutions for space-bound vehicles and objects, startups that can create radiation-safe applications will be in demand. So while the space economy will continue to provide numerous opportunities to invest in atoms, there will also be an opportunity to invest in the bits moving atoms across our skies.

Deep tech riding climate’s regulatory wave

Software alone will never solve the multitude of issues contributing to our climate crisis. Hardware solutions and engineering-led innovations in deep tech are needed to solve our most significant climate challenges.

A VC’s perspective on deep tech fundraising in Q1 2023 by Ram Iyer originally published on TechCrunch

Show, don’t tell: Tips for robotics startups raising a Series B during a downturn

Raising a Series B for any startup is challenging right now, with many VCs pulling back on investments — funding for Series B rounds across all sectors fell 55% in August compared to a year earlier, for example.

But raising a Series B for a hardware startup can be even tougher. It has simply always been more difficult to get venture investors to fund a robotics project compared to a software-only venture, given robotics’ high capital requirements and the greater risk.

However, the climb uphill can get much easier if a robotics startup can showcase a solid business model, measurable metrics and a plan for the next 18 months. As an investor in AI and automation companies for over 20 years, I’ve backed dozens of robotics companies, and I continue to be bullish on the space.

You need to show that customers are deriving real value from your robots — saving time, money or both.

Here are several strategies founders can use to prepare their robotics companies for a successful Series B.

Show how your robot works

Robots are inherently visual (can anyone forget that video of Boston Dynamics robots dancing?) So when you pitch VCs on your automation company, it pays to demonstrate your robots in action.

If your robots are large installations in warehouses or on manufacturing lines, invite VCs to come to see them working. If they are small enough to transport, bring them with you to the pitch meeting. And always have high-quality video available to share on a computer or tablet during in-person pitches or online for virtual meetings. Seeing your product in action is critical to getting investors excited about it.

Show customer ROI

Show, don’t tell: Tips for robotics startups raising a Series B during a downturn by Ram Iyer originally published on TechCrunch

Robotics scene continues to be bullish, but layoffs are looming

This startup season is filled with goals of profitability, promises of higher margins and whispers about pivoting toward sustainability. So when it comes to robotics, a capital-intensive sector that has a longer sales time horizon and loads of infrastructure hurdles, tensions feel inevitable.

Or at least, you’d think. Crunchbase data shows that, despite a creaky market, venture funding for robotics startups remains strong. It’s a dissonance worth exploring, so that’s exactly what we did at TC Sessions: Robotics 2022 with investors Kelly Chen, partner at DCVC, Bruce Leak, founder of Playground Global and Helen H. Liang, founder of FoundersX Ventures. The trio of investors spoke about how the ambitious sector is surpassing some of the downturn’s harshest symptoms.

The answer includes a shift in investment strategy and Amazon.

No more moody robotic arms, please

Tortoise co-founder Dmitry Shevelenko: ‘You can’t do too many things at the same time’

For a company named after a slow reptile, Silicon Valley startup Tortoise has made some quick pivots into new business models over the past year.

Co-founded in 2019 by ex-Uber executive Dmitry Shevelenko, the company began with a mission of being the operating system for micromobility vehicles, one that uses remote operators to reposition shared electric scooters to locations where prospective riders are or send them back to the warehouse for a charge.

In January 2021, Tortoise began working with shared micromobility operator Spin to test three-wheeled scooters embedded with Tortoise’s repositioning software.

But right before the company scored its Spin pilot, it started realizing the potential behind remote positioning and all the cameras and sensors the company had placed on scooters. With COVID-19 causing the burgeoning shared micromobility industry to take a nose dive at the same time as people, huddled indoors, began to demand quick delivery services, Shevelenko realized it “would be malpractice” not to pursue the robotic sidewalk delivery.

Tortoise started delivering with smaller local clients first, and then with big names like grocery story chain Albertson’s, nationwide logistics company AxelHire, and convenience store chain KRS. All signs were pointing to sidewalk delivery being a success.

But then…

In early March 2022, Tortoise pivoted again, vowing to focus entirely on mobile smart stores, which are essentially fancy vending machines placed on top of Tortoise’s delivery robots and located outside retailers. Now, Tortoise has moved from a hardware-as-a-service model to a take-rate scheme that gives it 10% of any sales made from its card payment-enabled bots, whether it’s a box of pastries from a bakery or brand new headphones from an electronics store.

Shevelenko, who served as Uber’s director of business development and was behind its acquisition of Jump bikes, says these pivots are just the beauty of a startup that’s responsive to market changes. The founder has advised or been on the board of a number of mobility and tech companies, including Skip, Superpedestrian, Codi, Payfare, Skyryse, SpotHero and Cargo Systems.

While Tortoise is his first time starting a company, Shevelenko is well versed in the factors that can cause a startup to win and lose.

We sat down with Shevelenko to talk about everything from Tier’s acquisition of Spin and the future of micromobility, how to own changing business directions, the difficulties in sidewalk robot delivery and the agility of startups.

The following interview, part of an ongoing series with founders who are building transportation companies, has been edited for length and clarity.

TC: At Uber, you were behind a lot of new mobility segments and the acquisition of Jump bikes. What do you think is the value of companies having multiple pillars, instead of just doing one thing really well?

Dmitry Shevelenko: For Uber, as a consumer-centric company, it’s ultimately a strategy of capturing all your transportation spend. The ultimate end state here — and this is why I think they’re putting so much money behind this Uber One subscription — is transportation-as-a-subscription product.

Ultimately, the way to win is to aggregate all the different ownership models so it’s shared, rented and owned. Dmitry Shevelenko

It’s not really effective for Uber and Lyft to try to win your business one trip at a time by offering you special incentives. If people are constantly switching back and forth between Uber and Lyft, they both lose. So the way to win is not by competing on a per-trip basis, but almost on an annual basis. How can you lock somebody in to be yours for a year? I think the essential nature of that consumer lock-in means you need to have more than just rideshare, right?

I think in rideshare, bundling is essential, because rideshare will have ups and downs. But the demand for transportation is constant. So if you have multiple modes, you’re always going to be doing well.

Tortoise’s original idea of repositioning scooters didn’t pan out in part because of the pandemic, but do you think it’s still a good idea?

Oh, absolutely. It’s just purely a function of sequencing and relative prioritization. The only reason delivery got so good, and there’s so much demand for it is because of COVID, too, right? It’s not only shared scooters that became bad.

Robotics founders: Build your pitch deck around problem-solving, not technology

In robotics, the remarkable often feels at odds with the practical. The Cassie robot captured the internet’s imagination (ours included) when it debuted in 2017 through a series of Oregon State University YouTube videos. It was one of the most exciting examples of robotics engineering since Boston Dynamics first made the scene.

Commercial applications, however, are a different conversation entirely. In a world of purpose-built systems, it’s not the first thing you see when you gaze upon the skinny legs of the ostrich-inspired bipedal ‘bot. When Agility Robotics first spun out of OSU’s College of Engineering, Cassie was being produced for research facilities. It’s a worthy mission, but not exactly a cash cow.

In a recent episode of TechCrunch Live, Agility’s co-founder and CTO, Jonathan Hurst, and Playground Global’s founding partner, Bruce Leak, joined us to discuss the robotic company’s journey from the lab to the commercial sector — and the role a good VC firm can play in that journey. The conversation spanned 30 minutes and includes a look at Agility Robotics’ early pitch deck. The deck and video are embedded below.

“If you’re building a company that’s building something that is really new and different, where are you going to hire engineers with experience with highly dynamic physical interaction, in the world, with force-sensitive behavior?” asks Hurst. “It’s just not common. Having students using the robots and a whole pipeline of people not only helps us, but it helps the whole infrastructure.”

From lab to launch

Playground Global, an early-stage investment firm based in Palo Alto, discovered the robot the way most of us did – watching cool videos online.

“We were surfing the internet like any good venture capital group, and we ran across the video that Agility released,” says Leak. “We were super impressed. This product, at some level, was just an incredible pair of legs. But it could walk for hours and even run across uneven terrain in a very practical way. Seeing something like that, which we thought might not even be possible, we knew we had to meet the Agility team.”

Agility’s seed/Series A pitch deck wasn’t focused on things like addressable market, and its insights into the robots’ practical commercial applications were cursory. What it did, however, was break down the startup’s impressive technologies. Hurst points to a tone shift between the presentation’s first slide, reading “Dynamic robots for human environments,” and its penultimate, “Made for work.”