Aug
17
2018
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Incentivai launches to simulate how hackers break blockchains

Cryptocurrency projects can crash and burn if developers don’t predict how humans will abuse their blockchains. Once a decentralized digital economy is released into the wild and the coins start to fly, it’s tough to implement fixes to the smart contracts that govern them. That’s why Incentivai is coming out of stealth today with its artificial intelligence simulations that test not just for security holes, but for how greedy or illogical humans can crater a blockchain community. Crypto developers can use Incentivai’s service to fix their systems before they go live.

“There are many ways to check the code of a smart contract, but there’s no way to make sure the economy you’ve created works as expected,” says Incentivai’s solo founder Piotr Grudzie?. “I came up with the idea to build a simulation with machine learning agents that behave like humans so you can look into the future and see what your system is likely to behave like.”

Incentivai will graduate from Y Combinator next week and already has a few customers. They can either pay Incentivai to audit their project and produce a report, or they can host the AI simulation tool like a software-as-a-service. The first deployments of blockchains it’s checked will go out in a few months, and the startup has released some case studies to prove its worth.

“People do theoretical work or logic to prove that under certain conditions, this is the optimal strategy for the user. But users are not rational. There’s lots of unpredictable behavior that’s difficult to model,” Grudzie? explains. Incentivai explores those illogical trading strategies so developers don’t have to tear out their hair trying to imagine them.

Protecting crypto from the human x-factor

There’s no rewind button in the blockchain world. The immutable and irreversible qualities of this decentralized technology prevent inventors from meddling with it once in use, for better or worse. If developers don’t foresee how users could make false claims and bribe others to approve them, or take other actions to screw over the system, they might not be able to thwart the attack. But given the right open-ended incentives (hence the startup’s name), AI agents will try everything they can to earn the most money, exposing the conceptual flaws in the project’s architecture.

“The strategy is the same as what DeepMind does with AlphaGo, testing different strategies,” Grudzie? explains. He developed his AI chops earning a masters at Cambridge before working on natural language processing research for Microsoft.

Here’s how Incentivai works. First a developer writes the smart contracts they want to test for a product like selling insurance on the blockchain. Incentivai tells its AI agents what to optimize for and lays out all the possible actions they could take. The agents can have different identities, like a hacker trying to grab as much money as they can, a faker filing false claims or a speculator that cares about maximizing coin price while ignoring its functionality.

Incentivai then tweaks these agents to make them more or less risk averse, or care more or less about whether they disrupt the blockchain system in its totality. The startup monitors the agents and pulls out insights about how to change the system.

For example, Incentivai might learn that uneven token distribution leads to pump and dump schemes, so the developer should more evenly divide tokens and give fewer to early users. Or it might find that an insurance product where users vote on what claims should be approved needs to increase its bond price that voters pay for verifying a false claim so that it’s not profitable for voters to take bribes from fraudsters.

Grudzie? has done some predictions about his own startup too. He thinks that if the use of decentralized apps rises, there will be a lot of startups trying to copy his approach to security services. He says there are already some doing token engineering audits, incentive design and consultancy, but he hasn’t seen anyone else with a functional simulation product that’s produced case studies. “As the industry matures, I think we’ll see more and more complex economic systems that need this.”

Aug
17
2018
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Klarity uses AI to strip drudgery from contract review

Klarity, a member of the Y Combinator 2018 Summer class, wants to automate much of the contract review process by applying artificial intelligence, specifically natural language processing.

Company co-founder and CEO Andrew Antos has experienced the pain of contract reviews first hand. After graduating from Harvard Law, he landed a job spending 16 hours a day reviewing contract language, a process he called mind-numbing. He figured there had to be a way to put technology to bear on the problem and Klarity was born.

“A lot of companies are employing internal or external lawyers because their customers, vendors or suppliers are sending them a contract to sign,” Antos explained They have to get somebody to read it, understand it and figure out whether it’s something that they can sign or if it requires specific changes.

You may think that this kind of work would be difficult to automate, but Antos said that  contracts have fairly standard language and most companies use ‘playbooks.’ “Think of the playbook as a checklist for NDAs, sales agreements and vendor agreements — what they are looking for and specific preferences on what they agree to or what needs to be changed,” Antos explained.

Klarity is a subscription cloud service that checks contracts in Microsoft Word documents using NLP. It makes suggestions when it sees something that doesn’t match up with the playbook checklist. The product then generates a document, and a human lawyer reviews and signs off on the suggested changes, reducing the review time from an hour or more to 10 or 15 minutes.

Screenshot: Klarity

They launched the first iteration of the product last year and have 14 companies using it with 4 paying customers so far including one of the world’s largest private equity funds. These companies signed on because they have to process huge numbers of contracts. Klarity is helping them save time and money, while applying their preferences in a consistent fashion, something that a human reviewer can have trouble doing.

He acknowledges the solution could be taking away work from human lawyers, something they think about quite a bit. Ultimately though, they believe that contract reviewing is so tedious, it is freeing up lawyers for work that requires a greater level of intellectual rigor and creativity.

Antos met his co-founder and CTO, Nischal Nadhamuni, at an MIT entrepreneurship class in 2016 and the two became fast friends. In fact, he says that they pretty much decided to start a company the first day. “We spent 3 hours walking around Cambridge and decided to work together to solve this real problem people are having.”

They applied to Y Combinator two other times before being accepted in this summer’s cohort. The third time was the charm. He says the primary value of being in YC is the community and friendships they have formed and the help they have had in refining their approach.

“It’s like having a constant mirror that helps you realize any mistakes or any suboptimal things in your business on a high speed basis,” he said.

Jul
25
2018
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Snark AI looks to help companies get on-demand access to idle GPUs

Riding on a wave of an explosion in the use of machine learning to power, well, just about everything is the emergence of GPUs as one of the go-to methods to handle all the processing for those operations.

But getting access to those GPUs — whether using the cards themselves or possibly through something like AWS — might still be too difficult or too expensive for some companies or research teams. So Davit Buniatyan and his co-founders decided to start Snark AI, which helps companies rent GPUs that aren’t in use across a distributed network of companies that just have them sitting there, rather than through a service like Amazon. While the larger cloud providers offer similar access to GPUs, Buniatyan’s hope is that it’ll be attractive enough to companies and developers to tap a different network if they can lower that barrier to entry. The company is launching out of Y Combinator’s Summer 2018 class.

“We bet on that there will always be a gap between mining and AWS or Google Cloud prices,” Buniatyan said. “If the mining will be [more profitable than the cost of running a GPU], anyone can get into AWS and do mining and be profitable. We’re building a distributed cloud computing platform for clients that can easily access the resources there but are not used.”

The startup works with companies with a lot of spare GPUs that aren’t in use, such as gaming cloud companies or crypto mining companies. Teams that need GPUs for training their machine learning models get access to the raw hardware, while teams that just need those GPUs to handle inference get access to them through a set of APIs. There’s a distinction between the two because they are two sides to machine learning — the former building the model that the latter uses to execute some task, like image or speech recognition. When the GPUs are idle, they run mining to pay the hardware providers, and Snark AI also offers the capability to both mine and run deep learning inference on a piece of hardware simultaneously, Buniatyan said.

Snark AI matches the proper amount of GPU power to whatever a team needs, and then deploys it across a network of distributed idle cards that companies have in various data centers. It’s one way to potentially reduce the cost of that GPU over time, which may be a substantial investment initially but get a return over time while it isn’t in use. If that’s the case, it may also encourage more companies to sign up with a network like this — Snark AI or otherwise — and deploy similar cards.

There’s also an emerging trend of specialized chips that focus on machine learning or inference, which look to reduce the cost, power consumption or space requirements of machine learning tasks. That ecosystem of startups, like Cerebras Systems, Mythic, Graphcore or any of the other well-funded startups, all potentially have a shot at unseating GPUs for machine learning tasks. There’s also the emergence of ASICs, customized chips that are better suited to tasks like crypto mining, which could fracture an ecosystem like this — especially if the larger cloud providers decide to build or deploy something similar (such as Google’s TPU). But this also means that there’s room to potentially create some new interface layer that can snap up all the leftovers for tasks that companies might need, but don’t necessarily need bleeding-edge technology like that from those startups.

There’s always going to be the same argument that was made for Dropbox prior to its significant focus on enterprises and collaboration: the price falls dramatically as it becomes more commoditized. That might be especially true for companies like Amazon and Google, which have already run that playbook, and could leverage their dominance in cloud computing to put a significant amount of pressure on a third-party network like Snark AI. Google also has the ability to build proprietary hardware like the TPU for specialized operations. But Buniatyan said the company’s focus on being able to juggle inference and mining, in addition to keeping that cost low for idle GPUs of companies that are just looking to deploy, should keep it viable, even amid a changing ecosystem that’s focusing on machine learning.

Jul
24
2018
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YC-backed Send Reality makes 3D virtual walkthroughs for residential listings

The fields of computer vision and VR are difficult. But a new company, Send Reality, is entering the race. The Y Combinator-backed company is looking to offer full 3D-modeling for virtual walkthroughs of real estate listings.

Founder and CEO Andrew Chen said he was the kid back in middle school and high school that spent hours walking around the streets of Paris, NYC and SF on Google Streetview.

“The thing I always wanted was to walk through the inside of all the interesting places of the world,” said Chen. “90 percent of the world’s most interesting physical content is inside, but I couldn’t do that.”

Chen explained that the field of computer vision has been able to make substantial technical breakthroughs, now allowing companies like Send Reality to create a videogame-style replica of the world.

For now, however, Send Reality is focused on luxury residential real estate.

Here’s how it works:

Send Reality sends photographers out to the listing with an iPad, a $250 commodity depth sensor, and a specialized Send Reality app. These photographers take hundreds of thousands of photos, and the Send Reality technology stitches those photos together to create a complete 3D model, as shown in the above .gif.

Chen says that what makes Send Reality tech special is how efficiently it’s able to stitch together these photos, explaining that the company can put together over 100K photos in the same time it takes for top academic labs in the world to put together 5,000.

“What this means is that the 3D models we create are so much more realistic than anything else anyone else has made,” said Chen.

For the luxury residential market that Send Reality is currently targeting, most listings are put up on their own website. Given this is still in beta, the numbers on Send Reality demoes are still rough. But Chen says that listing websites that include the Send Reality product see a 5x to 10x increase in the amount of time people spend on the website, with 75 percent to 80 percent of that extra time spent directly in the Send Reality viewer.

Send Reality sells directly to realtors, offering the product for $500 to $800 depending on the size and complexity of the home. In the future, the company can bring down that price point by allowing realtors to scan the home themselves from their own smartphone.

Send Reality has received funding from Y Combinator .

Jun
29
2018
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Leena AI builds HR chatbots to answer policy questions automatically

Say you have a job with a large company and you want to know how much vacation time you have left, or how to add your new baby to your healthcare. This usually involves emailing or calling HR and waiting for an answer, or it could even involve crossing multiple systems to get what you need.

Leena AI, a member of the Y Combinator Summer 2018 class, wants to change that by building HR bots to answer questions for employees instantly.

The bots can be integrated into Slack or Workplace by Facebook and they are built and trained using information in policy documents and by pulling data from various back-end systems like Oracle and SAP.

Adit Jain, co-founder at Leena AI, says the company has its roots in another startup called Chatteron, which the founders started after they got out of college in India in 2015. That product helped people build their own chatbots. Jain says along the way, they discovered while doing their market research a particularly strong need in HR. They started Leena AI last year to address that specific requirement.

Jain says when building bots, the team learned through its experience with Chatteron that it’s better to concentrate on a single subject because the underlying machine learning model gets better the more it’s used. “Once you create a bot, for it to really add value and be [extremely] accurate, and for it to really go deep, it takes a lot of time and effort and that can only happen through verticalization,” Jain explained.

Photo: Leena AI

What’s more, as the founders have become more knowledgeable about the needs of HR, they have learned that 80 percent of the questions cover similar topics, like vacation, sick time and expense reporting. They have also seen companies using similar back-end systems, so they can now build standard integrators for common applications like SAP, Oracle and NetSuite.

Of course, even though people may ask similar questions, the company may have unique terminology or people may ask the question in an unusual way. Jain says that’s where the natural language processing (NLP) comes in. The system can learn these variations over time as they build a larger database of possible queries.

The company just launched in 2017 and already has a dozen paying customers. They hope to double that number in just 60 days. Jain believes being part of Y Combinator should help in that regard. The partners are helping the team refine its pitch and making introductions to companies that could make use of this tool.

Their ultimate goal is nothing less than to be ubiquitous, to help bridge multiple legacy systems to provide answers seamlessly for employees to all their questions. If they can achieve that, they should be a successful company.

Jun
26
2018
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YC grad ZenProspect rebrands as Apollo, lands $7M Series A

ZenProspect, a startup that emerged from the Y Combinator Winter 2016 class to help companies use data and intelligence to increase sales, announced today that it was rebranding as Apollo. It also announced a $7 million Series A investment.

The round was led by Nexus Venture Partners. Social Capital and Y Combinator also participated. Apparently Y Combinator liked what they saw enough to continue to invest in the company.

Apollo helps customers connect their sales people with the right person at the right time. That is typically a customer that is most likely to buy the product. It does this by combining a number of tools including a rules engine to automate prospect routing, a lead scoring tool and analytics to measure results at a granular level, among others.

Apollo analytics. Photo: Apollo

The company also uses data they have collected from 200 million contacts at 10 million companies to match sellers to buyers along with the information in the user’s own CRM tools — typically Salesforce. Apollo is making this vast database of company and contact data available for customers to use themselves for free starting today.

Apollo CEO and founder Tim Zheng says the company was born out of a need at a previous venture. He was working at a startup that was floundering and sales had flatlined. When they couldn’t find a product on the market to help them, they decided to build it and saw the number of users increase from 5000 to 150,000 users in just five weeks. That eventually reached a million users.  As he spoke to friends at other Bay area companies about what his company had done, he heard a lot of interest, and decided to turn that sales tool into a company.

The company launched as ZenProspect in 2015 and went through Y Combinator in 2016. They were the third fastest growing company in that YC batch, generating $1 million in annual recurring revenue (ARR) during their tenure. In fact, they were profitable out of the gate, using their own software to sell the product.

Zheng points out that there are thousands of sales tools out there, but he said, even if you bought every one of them and stitched them together you still wouldn’t have a great sales process. Zheng says his company has figured out how to solve that problem and provide that structure to deliver the best prospects to sales people to close deals.

The company works closely with Salesforce as 80 percent of its customers are using data inside of Salesforce in conjunction with the Apollo tool. It’s worth noting, however, that Apollo is not built on top of Salesforce platform. It just integrates with it.

They target both early stage startups looking to increase sales and established enterprise customers with huge sales teams. So far it’s been working. Today, Apollo has 500 customers and 50 employees. With the current influx of money, they expect to get to 120 in the next 12 -18 months.

May
10
2018
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For ScopeAR, the market is finally catching up with the technology

ScopeAR, a graduate of the Y Combinator Summer 2015 class, came to the augmented reality game very early, launching in 2011 when there was very little hardware and most people didn’t understand the technology. But it has managed to hang around long enough for the market and the hardware to finally catch with the founders’ vision of using AR as an advanced training tool in the enterprise.

Today, the company offers a pair of tools. First of all, there is RemoteAR, which CEO and company co-founder Scott Montgomerie describes as “Facetime with AR on top of it.” It allows a technician to virtually look over the shoulder at what a local person is seeing and provide directions and feedback in real time remotely. For example, the expert could circle the cover the technician needs to remove and point to the screws with two virtual arrows.

This could have great utility in any situation where you have an experienced person in the office, who doesn’t want to go on the road on anymore, but can still provide detailed instructions to a novice, acting as a trainer and helper. This is an actual problem in many industries with aging workforces.

Technician working with RemoteAR. Photo: ScopeAR

The second product, called WorkLink, lets you import a 3D CAD model, then associate that model with real equipment. When you put on hardware like Microsoft Hololens, you can see the 3D representation of the equipment and follow along with instructions on how to repair it. It also works with iOS, Android and Windows devices.

One big change since the company was established in 2011 is the variety of platforms you can use for augmented reality. “Last year was the thing where AR took off. Apple got into it with ARKit and Google with ARCore and awareness happened and people saw it was viable,” Montgomerie said.

By creating enterprise use cases like remote assistance and work instructions, the company has been gaining momentum over the last year, and reports tripling its revenue in 2017. Although they aren’t sharing a specific number, it’s fair to say they are growing quickly.

They developed an augmented reality training product early on that resonated with a mining company, and that along with consulting working with the likes of NASA, Boeing and Toyota, helped them stay afloat until things began to really click around AR in recent years, Montgomerie explained.

They also took some time to be part of the Y Combinator Summer 2015 class, and even scored a spot on TechCrunch’s list of favorite Demo Day 2 startups. During the YC experience, they developed the first version of RemoteAR. WorkLink followed a year later.

So far the company has taken on a fairly modest amount of investment with Montgomerie reporting three seed rounds including $2 million right after Y Combinator, $1.7 million last May and another $2 million this past December. If the company continues to grow at this rate, it’s a good bet they will be looking for a Series A at some point to help scale the company further.

Apr
12
2018
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Background checks pay for Checkr, which just rang up $100 million in new funding

Criminal records, driving records, employment verifications. Companies that use on-demand employees need to know that all the boxes have been checked before they send workers into the world on their behalf, and they often need those boxes checked quickly.

A growing number of them use Checkr, a San Francisco-based company that says it currently runs one million background checks per month for more than 10,000 customers, including, most newly, the car-share company Lyft, the services marketplace Thumbtack, and eyewear seller Warby Parker.

Investors are betting many more customers will come aboard, too. This morning, Checkr is announcing $100 million in Series C funding led by T. Rowe Price, which was joined by earlier backers Accel and Y Combinator.

The round brings the company’s total funding to roughly $150 million altogether, which is a lot of capital in not a lot of time. Yet Checkr is very well-positioned considering the changing nature of work. The company was born when software engineers Daniel Yanisse and Jonathan Perichon worked together at same-day delivery service startup Deliv and together eyed the chance to build a faster, more efficient background check. The number of flexible workers has only exploded in the four years since.

So-called alternative employment arrangements, in the parlance of the Bureau of Labor Statistics, including gig economy jobs, have grown from representing 10.1 percent of U.S. employees in 2005 to 15.8 percent of employees in 2015. And that percentage looks to rise further still as more digital platforms provide direct connections between people needing a service and workers willing to provide it.

Meanwhile, Checkr, which has been capitalizing on this race for talent, has its sights on much more than the on-demand workforce, says Yanisse, who is Checkr’s CEO. While the 180-person company counts Uber, Instacart, and GrubHub among its base of customers, Checkr is also actively expanding outside of the tech and gig economy, he says. It recently began working with the staffing giant Adecco, for example, as well as the major insurer Allstate.

At present, all of these customers pay Checkr per background check. That may change over time, however, particularly if the company plans to go public eventually, which Yanisse suggests is the case. (Public shareholders, like private shareholders, love recurring revenue.)

“Right now, our pricing model for customers is pay-per-applicant,” says Yanisse. “But we have a whole suite of SaaS products and tools” — including an interesting new tool designed to help hiring managers eradicate their unwitting hiring biases — “so we’re becoming more like a SaaS” business.

While things are ticking along nicely, every startup has its challenges. In Checkr’s case, one of these would seem to be those high-profile cases where background checks are painted as far from foolproof. One situation that springs to mind is the individual who began driving for Uber last year, six months before intentionally plowing into a busy bike path in New York. Indeed, though Checkr claims that it can tear through a lot of information within 24 hours — including education verification, reference checks, drug screening — we wonder if it isn’t so fast that it misses red flags.

Yanisse doesn’t think so. “Overall background checks aren’t a silver bullet,” he says. “Our job is to make the process faster, more efficient, more accurate, and more fair. But past information doesn’t guarantee future performance,” he adds. “This isn’t ‘Minority Report.’”

We also ask Yanisse about Checkr’s revenue. Often, a financing round of the size that Checkr is announcing today suggests a revenue run rate of $100 million or so. Yanisse declines to say, telling us Checkr doesn’t share revenue or its valuation publicly. “It’s still a bit early,” he says. “There’s this obsession with metrics in Silicon Valley, and we just want to make sure we’re focused on the right things.”

But, he adds, “you’re in the ballpark.”

Correction: An earlier version of this story incorrectly listed Visa as a customer.

Mar
23
2018
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Y Combinator’s Jessica Livingston on Dropbox IPO: “It was just a dream of ours”

Dropbox, after more than a decade, finally went public this morning — and the stock soared more than 40% in its initial trading, making it a marquee success for one of the original Web 2.0 companies (at least for now).

While we still have to wait for the dust to settle, it’s been a very long road for Dropbox. From starting off as a file-sharing service, to hitting a $10 billion valuation in the middle of a massive hype cycle, to expectations dropping and then the announcement of a $1 billion revenue run rate. Dropbox has been a rollercoaster, but it’s another big moment this afternoon: it’s Y Combinator’s first big IPO. And Y Combinator still has a very deep bench of startups that are, thus far, obvious IPO candidates down the line like Airbnb and Stripe.

That isn’t to take away anything from the work of CEO Drew Houston and the rest of Dropbox’s team, but Y Combinator’s job is to basically take a bunch of shots in the dark based on good ideas and potentially savvy founders. Houston was one of the first of a firm that now takes in a hundred-odd founders per class. Y Combinator Founder and partner Jessica Livingston was there for the start of it, recalling back to the day that Houston rushed to her and Paul Graham to show him his little side project.

We caught up with Livingston this morning ahead of the IPO for a short interview. Here’s the conversation, which was lightly edited for clarity:

TC: Can you tell us a little bit about what it’s like to finally see the first Y Combinator company to go public?

JL: I feel like 13 years ago, it was just this dream of ours. It was this seemingly unattainable dream that goes, ‘maybe one of the startups we fund could go public someday.’ That was the holy grail. It’s an exciting day for Y Combinator. It shows what a long game investing is in early-stage startups. I do feel kind of validated.

TC: How did Y Combinator first end up in touch with Houston?

JL: He applied as a solo founder. We had met Drew the summer before. Back then, we were so small that we always encouraged people to bring friends to a Y Combinator dinner. [Xobni founder Adam Smith] brought [Houston], and we met him then and talked it through. When he applied, we invited him to come to an interview, and Paul [Graham] before the interview reached out to [Houston]. He said, “I see you’re a solo founder, and you should find a cofounder.” Three weeks later Drew showed up with [co-founder Arash Ferdowsi]. It was a great match that worked well.

TC: As Dropbox has grown, what’s stood out to you the most during changes in the market?

JL: They’re a classic example of founders who are programmers who built something to solve their own problem. Clearly, this is a perfect example of that. Drew gets on the bus, he forgets his files, and he can’t work on the whole trip down. He then creates something that will allow him to access files from everywhere. At the time, when he came on the scene with that, there were a lot of companies doing it but none were very good. I feel like Dropbox, regardless of market dynamics, from the very beginning was always dedicated to wanting to do well by building a better solution. They wanted to build one that actually works. I feel like they’ve stuck to that and that’s been driving them since. That’s been their guidepost.

TC: What was your first meeting with Houston like, and do you think he has changed in the past 10 years?

JL: When I first met him, he was young — he was very young — and he was always a good hacker, and very earnest. During Y Combinator he was very focused on building this product and was not distracted by other things. That’s when there were just two people. He’s really evolved over the years as an incredible leader. He’s grown this company and he’s navigated through all different parts of his life cycle. I’ve witnessed his growth as a leader and as a human being. He’s always been a great person. It’s sort of exciting to see where he is now that he’s come a long way, it’s really cool.

TC: Houston and Ferdowsi still own significant portions of the company even after raising a lot of venture capital. Do you think Y Combinator had any effect on companies looking for more founder friendly deals?

JL: I think when Y Combinator started, our goal in many ways was to empower founders. It was to level the playing field. You don’t have to have a connection in Silicon Valley to get funding. You just have to apply on our website. You don’t have to have gone to an Ivy League school. We [try to tell them], don’t let investors take advantage of you because you’re young and have never done this before. In general, times have changed over the past 15 years. Hopefully Y Combinator played a small role in some of those changes in making things a little more found friendly.

TC: What’s one of your favorite stories about Houston?

JL: He was always very calm, cool, and collected under pressure. I remember that was definitely a quality about him. His feathers didn’t get ruffled easily. One of the things I remember most clearly is from that summer when we had demo day. Back then it was, like, 40 people tops. Still, there was a lot of pressure. I remember Paul [Graham] came up with this idea that, ‘hey, Drew, during your demo day you should show people how well Dropbox actually works by deleting your presentation live and restoring it through Dropbox.’ That’s kind of risky, right? To delete your presentation. You’re just standing up there without anything. And he did it and he nailed the presentation. It sounds a little gimmicky, but it really worked and showed his product worked. I remember thinking, like, wow, he’s pretty calm. If it were me I don’t think I could hit the delete button in front of these people. That’s an important quality in someone, not to get flustered.

By the way, we funded them in 2007. If you asked me in 2008 how were they doing, I would say, well, they’re making progress. But it wasn’t like we funded them and we could say, ‘this is gonna be a great one.’ We just knew, yeah they’re making progress, but it’s always hard to know there.

TC: Back then, what were you just expecting? M&A? Did you even anticipate an IPO?

JL:  As we were formulating the idea, the hope was rather than going to work at Microsoft — I use them as an example because that was the company back then — and rather than going to get a job out of college, why not build a company and make Microsoft acquire you to get you to work for them? We had low expectations back then. We were hoping there’d be some small acquisitions. But yes, the hope was always acquisitions, but maybe someday in our wildest dreams there’d be an IPO. We didn’t even think YC would work when we started, people didn’t believe in YC’s models for many years.

TC: Looking back, what would you say is one of the biggest things you’ve learned throughout this experience?

JL: What a long road it is for startups. When we started YC back then, it wasn’t a popular thing to do a startup. Now, thank goodness, more people are starting them, and more types of people are starting them. It’s not just super high-tech companies. That’s exciting, but what I think a lot of people don’t realize is how hard startups are. You say, yeah, I know how hard, but people don’t realize how difficult they are and how long the commitment is. If you’re successful, it takes such a long time. For [someone like Houston] to make it to that point, they’ve committed a lot of their life and energy and all their intellectual capacity to making this work. To me, that’s so exciting, but I think it would surprise people to know realistically how long that could take.

TC: What would you tell startups with the hindsight of what happened with Dropbox’s valuation hype cycle?

JL: I will say, with startups, sometimes you just have to stick to what you’re doing. There’s a lot of stuff going on around you, especially now with social media and things like that. With a startup, you just have to keep moving forward with building a company and building a great product.

Mar
19
2018
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Bear Flag Robotics wants to sell an autonomous tractor for farms

Autonomous vehicles are increasingly becoming the shiny object in Silicon Valley. But the opportunity doesn’t just extend to cars driving around the streets of a major metropolitan area, and Igino Cafiero and Aubrey Donnellan hope to take it somewhere a little less obvious: the middle of an orchard.

Cafiero and Donnellan are building an autonomously-driven tractor as part of a startup called Bear Flag Robotics. The pair argue that there’s increasingly a struggle to find enough labor to work on farms, and even then, the costs are continuing to rise over time — leading to a need to increase those efficiencies on the actual field in addition to a lot of new technology like satellite imagery and computer vision to analyze the health of plants. The first product for Bear Flag Robotics is a self-driving tractor, and the company is coming out of Y Combinator’s winter class this year.

“We got a tour of an orchard and just how pronounced the labor problem is,” Donnellan said. “They’re struggling to fill seats on tractors. We talked to other growers in California. We kept hearing the same thing over and over: labor is one of the most significant pain points. It’s really hard to find quality labor. The workforce is aging out. They’re leaving the country and going into other industries.”

There are certainly a lot of technical challenges that go into it, and not just pertaining from having the right computer vision products in place in order to create an autonomous tractor. For example, the tractors have to be able to operate without a GPS signal, Donnellan said, simply because operating a tractor in an orchard may mean driving around with a ton of canopy cover — which could block the signal. It might be a little simpler to just drive down a path in an orchard, but there’s still quite a lot to consider, she said.

“We have this platform that we’ve plugged a ton of sensors into it,” Cafiero said. “That includes cameras. When you look forward, once we’ve automated the driving part, the sky’s the limit in terms of utilizing some of this technology once it’s out there. When we’re out there we can use these cameras, and be able to make recommendations and spot treatment in the field.”

When it comes to testing, Cafiero and Donnellan just go out to an orchard over in Sunnyvale a few times a week to see what some of the challenges growers face.

While finding labor has been a challenge, Cafiero acknowledges that there are still questions around undocumented labor when it comes to labor on those farms. He said, in the end, Bear Flag Robotics’ aim is to augment the workforce by taking away some of the more mundane tasks required on the fields. Cafiero also said that there’s a lot of reverse immigration happening from the U.S., leading to more of a labor shortage.

“The work itself is really tough work,” Donnellan said. “You’re in the field all day long, sometimes in inclement conditions. One of the tasks we’re automating is spraying, fungicides, herbicides, and these people out there, they’re wearing hazmat suits. It’s not good for their health to be doing these tasks in general. When you’re presented in higher paying jobs in other fields, there’s less of a case to go into that job, and there’s demand in a lot of other industries like construction [and other industries] where it’s easier work and better pay.”

Selling the actual tractor can also be a challenge, simply because potential customers will be buying their equipment down the road at sellers they know. If something breaks down, they need someone to come over, in person, as soon as possible to fix it or risk losing yield. And the major equipment providers may too see the need to start working on autonomous tools. Cafiero’s hope is that the startup will be able to work with local sellers and get into those channels, and that’s the only logical place to start. There might be some aim to scale up over time, but the company hopes to just get started with local dealerships for now.

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