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Here’s what all acknowledged AI startups have in common

Ben Dickson
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Ben Dickson

Ben Dickson is the architect of TechTalks. He writes consistently about business, technology and politics. Follow him on Twitt… (show all) Ben Dickson is the architect of TechTalks. He writes consistently about business, technology and politics. Follow him on Cheep and Facebook

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With tech giants pouring billions of dollars into bogus intelligence projects, it’s hard to see how startups can find their place and create acknowledged business models that advantage AI. However, while angrily competitive, the AI space is also consistently causing axiological shifts in many sectors. And this creates the absolute ambiance for fast-thinking and -moving startups to carve a niche for themselves before the big players move in.

Last week, technology assay firm CB Insights published an update on the status of its list of top 100 AI startups of 2020 (in case you don’t know, CB Insight publishes a list of 100 most able AI startups every year). Out of the hundred startups, four have made exits, with three going public and one being acquired by Facebook.

A closer look at these startups provides some good hints at what it takes to create a acknowledged business that makes use of AI. And (un)surprisingly, bogus intelligence is a small part — albeit an important one — of a acknowledged artefact administration strategy. Here are some of the key takeaways from AI startups that have managed to reach a stable status.

Lemonade: AI complements a acknowledged artefact strategy

lemonade ceo daniel schreiber at TC Disrupt 2018
Lemonade Inc. CEO Daniel Schrieber at TC Disrupt 2018

Lemonade, an insurtech startup founded in 2015, made its antecedent public alms in July with a $1.7 billion valuation. Lemonade is an online belvedere that aims to abode some of the key problems of the acceptable home allowance industry. The aggregation has been able to advance its business through smart design and a good business strategy. The AI basic was built on top of that.

The company’s website and mobile app are very easy to use. The action of buying allowance and filing claims with the app and website goes through agenda administration and is much faster than acceptable allowance companies. As one of the first movers in the insurtech space, Lemonade had the edge over other agnate companies that have circumscribed up in recent years, and it was able to bound snatch a lot of users who were attractive for a shift from acceptable allowance model to one that was more tech-focused.

Lemonade’s business model and messaging are also interesting. The aggregation takes a flat fee from premiums, which means the aggregation doesn’t make a profit from abstinent claims. The bearding money goes to charities of users’ choice. The aggregation also says that it will not invest premiums into heavily communicable industries and companies that cause harm. So, basically, Lemonade is business itself as the good guy in a historically reviled industry, on a mission, per the company’s words, to “transform allowance from a all-important evil to a social good.”

Insurance depends a lot on data, and accustomed agencies have more than a aeon of data they can use to advance risk models and create allowance policies. Lemonade didn’t have the data of acceptable agencies, but it also didn’t have their accoutrements of barter and old policies. It was able to create its entire technology stack from the ground up to cater to the needs of an AI factory.

With the entire acquaintance being digitized, the aggregation can aggregate a lot more data from each chump interaction, including data points that other agencies do not capture. This enables the aggregation to create machine acquirements models that not only adumbrate allowance risk with growing accurateness over time but can also create automation and personalization opportunities that were absurd before. The aggregation has two AI chatbots: Maya helps you create your allowance plan in a few minutes, and Jim handles the claims process. According to the company, AI handles a third of the cases and pays claims in a matter of minutes. The rest of the claims are transferred to human agents. The chatbot continues to advance as it gathers more data.

The aggregation believes that with time, the AI will give it the edge over acceptable agencies and allow it to accommodate much more affordable plans to customers. And its $480 actor pre-IPO allotment and its post-IPO growth show that investors accept its plan can work.

Lemonade’s head start is its better protection. Other startups that would want to copy its business model don’t have its data and can’t create appropriately able AI models. And it has also created a careful moat adjoin acceptable allowance agencies, which are much slower to move into new areas. By the time they do create their own AI factories, Lemonade will have carved a adequate niche for itself.

Butterfly Network: Specialized accouterments with AI enhancements

butterfly iq ultrasound probe
Butterfly Network iQ ultrasound probe

Butterfly Network will be listed on the New York Stock barter after a $1.5 billion appropriate purpose accretion aggregation (SPAC) merger with Longview Capital later this year.

The company’s artefact is Butterfly iQ, a medically accustomed single-probe, whole-body ultrasound device that connects to a smartphone and works with an accompanying mobile app. The device costs $2,000, which is much more affordable than the five- and six-digit-priced ultrasound sets usually found at hospitals. The aggregation aims to make high-quality ultrasound imaging accessible to communities that can’t afford high-end accessories and bring carriageable scanning to places where the bulky ultrasound sets can’t go.

iQ also uses bogus intelligence to create use cases that are not accessible on other ultrasound devices. For instance, one of the AI appearance of iQ is a slider in the app that shows the affection of the image to the user. As the user moves the probe on the patient’s body, the slider shifts to show whether the device is accepting a good abduction or not. The affection uses an artificial neural network that has been accomplished on tens of bags of images to discriminate amid good and bad images. For instance, frontline responders or clinics whose staff don’t have the ability with ultrasound can use the device to get proper images and send them to experts for added analysis.

The device and app come arranged with a bunch of cloud accumulator and administration appearance that facilitate the use of data in a broader health care context.

The aggregation is also alive to add new apparatus learning-powered features to help with altitude and analysis.

So here too, I think that AI is a small but important part of the all-embracing business. The better value comes from the hardware. The small, carriageable ultrasound device allows Butterfly to differentiate itself from other manufacturers and create value for beginning segments of the market. AI is the added value that helps it advance the software stack that builds on top of the hardware. Given that the device uses chump smartphones, it also has the abeyant to add new AI appearance and always advance its product’s achievement as mobile device accouterments becomes better.

The one risk I see in Butterfly’s AI business is the achievability of agnate moves from domiciliary names such as Philips and Siemens. Should health tech giants decide to enter the handheld ultrasound business, Butterfly Network will need to find commodity that can assure its articles adjoin copycats. One accessible band-aid would be for Butterfly to work out a privacy-friendly plan to aggregate ultrasound data from iQ accessories to advance the achievement of its AI models. But it will not be very easy, given the acute nature of health data.

C3.ai: Action AI can work if you have the reputation

c3.ai action ai
C3.ai website

C3.ai, accession one of the acknowledged AI startups mentioned by CB Insights, is a provider of action AI software. C3.ai’s pre-IPO appraisal was $4 billion, but on the first day of trading, its market cap skyrocketed above $13 billion.

C3.ai software helps companies build AI models on top of their data for predictive maintenance, bigger account management, fraud detection, energy management, and other operational enhancements that can reduce costs and access productivity. C3.ai is not a provider of cloud casework but its software is accordant with most top cloud providers such as Microsoft Azure, Amazon Web Services, Google Cloud, and IBM Cloud.

Under normal circumstances, C3.ai’s artefact action would be advised risky. From a abstruse standpoint, it has no key differentiator. It is accouterment casework that can easily be replicated by accession aggregation that has the right resources, including the very cloud casework its software integrates with. And since its founding in 2009, the aggregation has afflicted its name twice from C3 Energy to C3 IoT and then to C3.ai, which sounds a bit opportunistic.

What makes C3.ai different, however, is its architect Thomas Siebel, a billionaire and a acclaimed and admired entrepreneur. C3.ai’s success hinges not on a lot of small barter but on creating ripple furnishings in altered sectors by accepting big customers. In this respect, having a person on board who has the acceptability and acquaintance of Siebel can make a big difference. Currently, C3.ai’s barter accommodate accouterment architect Caterpillar, oil and gas casework aggregation Baker Hughes, and energy aggregation Engie, all big names in their corresponding industries. Interestingly, 36 percent of its acquirement in 2020 came from Baker Hughes and Engie.

Therefore, although C3.ai provides very good AI development tools, the company’s success can be abundantly attributed not to its unique AI capabilities but its chump accretion and assimilation strategy. I’m not sure if that would have been accessible after having accession at the helm of the aggregation who has strong access in altered markets and a acceptability for carrying great products.

Mapillary: The value of data

Mapillary CEO Jan Erik Solem at RAAIS 2017
Mapillary CEO Jan Erik Solem at RAAIS 2017

The final aggregation that’s worth analytical in the CB Insights list is Mapillary, acquired by Facebook in June for an bearding amount. Mapillary launched in 2013 to create a massive dataset of street-level images, allusive Google’s Street View service.

Since its founding, Mapillary has collected more than one billion high-resolution images from cities around the world. Before being acquired by Facebook, Mapillary had partnered with Amazon’s AI belvedere to abstract advice from images through computer vision.

Mapillary didn’t have a super-advanced AI appliance or a very able roadmap to making a profit over its data. But its data and casework could prove to be a great accession to a larger ecosystem of AI software, such as that of Facebook. There are many ways Facebook, which is in the business of alive more and more about its users, can turn a profit from Mapillary’s data. For now, we know that it will be amalgam Mapillary’s data and applications into Facebook’s aggrandized absoluteness and Marketplace platforms. And there are many other uses Facebook’s AI assay unit can have for absolute access to this large data set of labeled street images.

Therefore, I don’t quite see Mapillary as an AI success story, but its accretion highlights the value of data in the AI industry. Large tech companies are often in search of ways to obtain absolute data to hone their AI models and gain an edge over competitors. And they’re more than accommodating to take a adjustment by purchasing accession company’s data—and conceivably the whole aggregation with it.

The “AI startup” misnomer

I think “AI startup” is a misnomer when activated to many of the companies included in the CB Insights list because it puts too much focus on the AI side and too little on the other acute aspects of the company.

Successful companies start by acclamation an disregarded or poorly solved botheration with a sound artefact strategy. This gives them the minimum market assimilation needed to authorize their business model and gather data to gain insights, steer their artefact in the right direction, and train apparatus acquirements models. Finally, they use AI as a appropriate factor to coalesce their position and advance the edge over competitors.

No matter how advanced, AI algorithms alone don’t make a acknowledged startup nor a business strategy.

This commodity was originally appear by Ben Dickson on TechTalks, a advertisement that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also altercate the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the aboriginal commodity here. 

Appear February 8, 2021 — 08:42 UTC

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