How do companies use artificial intelligence?
AI is created through machine learning, which involves training a system withhuge amounts of data. It then uses that trained system to make inferencesabout new data it’s never seen.The simplest example is a system designed to detect objects in images. Imageswith those objects are provided to the system, which “learns” how to detectthose objects in other images. The more objects in images it detects, the moreaccurate the detection system becomes.Companies employ artificial intelligence in two main ways. Many tech companiesuse AI to make their existing operations more powerful, such as through high-profile applications, including robotics, self-driving cars, and virtualassistants. Google, a subsidiary of Alphabet (NASDAQ:GOOGL), (NASDAQ:GOOG),uses AI to filter out spam for Gmail users. Amazon (NASDAQ:AMZN) uses AI torecommend products to customers, while Netflix (NASDAQ:NFLX) uses AI to guidecontent creation and recommendations.Some companies also profit directly from AI by selling hardware, software,services, or expertise the technology needs. These are true artificialintelligence stocks and include those listed and described below.What is AI?Machine learning, or AI, involves training a system with huge amounts of data,then using that trained system to make inferences about new data it has neverseen.
Top AI stocks to watch
Company | AI Focus —|— NVIDIA (NASDAQ:NVDA) | Graphics chips and self-driving cars IBM (NYSE:IBM) | Augmenting human intelligence across industries Micron Technology (NASDAQ:MU) | Memory chips for data centers and self-driving cars Amazon (NASDAQ:AMZN) | Voice-activated technology, cloud computing, ande-commerce C3.ai (NYSE:AI) | Software-as-a-service to provide enterprise-scale AIapplications
Deep learning stocks
Deep learning is a subset of machine learning that uses artificial neuralnetworks inspired by the human brain. It’s the most advanced kind ofartificial intelligence and is crucial in technologies such as self-drivingcars. Deep learning is making advances in areas such as preventive healthcare,where predictive algorithms are necessary, and it differs from machinelearning in that it doesn’t require human inputs.Among the companies closely associated with deep learning are Nvidia, whoseGPU chips use deep learning to power data centers and enable autonomousdriving and cloud computing, among other functions.Alphabet has exposure to deep learning through a number of its businesses,including its autonomous vehicle start-up Waymo. It also owns DeepMind, a deeplearning platform that can diagnose eye diseases, predict the shapes ofproteins, and accelerate the scientific discovery process.
AI is a growth business
Total spending on AI systems is forecast to reach $97.9 billion in 2023, upfrom $37.5 billion in 2019. For the five-year period ending in 2023, the AIsector is predicted to grow at an annualized rate of 28.4%.With the AI market already large and still growing quickly, plenty ofcompanies can profit from AI. Although picking stocks in a growth industrycomes with a lot of uncertainty, these top AI stocks are all worthconsidering.
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Earnings season is almost here. Can these fast-growing companies impress WallStreet?Daniel Sparks | Sep 29, 2021Valuing high-tech companiesFor the past several years, investors have once again been piling into sharesof companies with fast growth and high uncertainty—especially Internet andrelated technologies. The rapid rise and sudden collapse of many such stocksat the end of the 20th century raised questions about the sanity of a stockmarket that appeared to assign higher value to companies the more their lossesmounted. Now, amid signs that the current tech boom is wobbling, even the USSecurities and Exchange Commission is getting into the act, announcing in late2015 its plans to investigate how mutual funds arrive at widely varyingvaluations of privately held high-tech companies.In the search for precise valuations critical to investors, we find that somewell-established principles work just fine, even for high-growth companieslike tech start-ups. Discounted-cash-flow valuation, though it may soundstodgily old school, works where other methods fail, since the core principlesof economics and finance apply even in uncharted territories, such as start-ups. The truth is that alternatives, such as price- to-earnings or value-to-sales multiples, are of little use when earnings are negative and when therearen’t good benchmarks for sales multiples. More important, these shorthandmethods can’t account for the unique characteristics of each company in afast-changing environment, and they provide little insight into what drivesvaluation.Although the components of high-tech valuation are the same, their order andemphasis differ from the traditional process for established companies: ratherthan starting with an analysis of the company’s past performance, begininstead by examining the expected long-term development of the company’smarkets—and then work backward. In particular, focus on the potential size ofthe market and the company’s market share as well as the level of return oncapital the company might be able to earn. In addition, since long-termprojections are highly uncertain, always value the company under differentprobability-weighted scenarios of how the market might develop under differentconditions. Such techniques can help bound and quantify uncertainty, but theywill not make it disappear: high-growth companies have volatile stock pricesfor sound reasons. What follows is an adaptation of analysis we published in2015, using public data from 2014 and 2015. The analyses herein are presentedas an exercise to illustrate the methodology. They are not meant as acommentary on the current market situation and should not be used as the basisfor trading in the shares of any company.
Size the market
Although Yelp management rightfully touts its unique visitors and growing baseof customer reviews, what really matters from a valuation perspective is itsability to convert local businesses into Yelp clients. Start with estimatinghow many local businesses are in Yelp’s target markets, how many businesseswill register with Yelp, and how many of those businesses will convert to itspaid services. There are approximately 66 million small and midsize businessesin Yelp’s target markets. As of 2014, the company had registered 2 millionbusinesses on its site. Of the businesses that registered, only 84,000 werepaying clients. With 1 percent market penetration, there is plenty of room forgrowth (exhibit).To build a revenue forecast, first estimate the number of business that mightregister with Yelp. We estimate both historical and future registration ratesby analyzing Yelp’s historical data. Since registration is free and Yelp iswell known, we model penetration, for this exercise, to reach 60 percent. Thattranslates to 8.5 million registered businesses by 2023. For most start-ups,forecasting a 60 percent share is extremely aggressive, since additionalcompetition is likely to enter the market. For this business, however, it isreasonable to assume that the largest company is likely to capture asignificant portion of the online market—since businesses desire anadvertising partner that generates the most traffic, and consumers desire awebsite with the most reviews. In that way, this business is similar to otherswith a community of users that reinforces the use of the product, such asMicrosoft’s Windows operating system, which still retains more than 80 percentof its market.With registered businesses in hand, next estimate the conversion rate frombasic (free) to enhanced (pay) services. To estimate this number, we analyzeddata from cohorts of Yelp’s markets based on entry dates to annual conversionrates the company has reported. Based on historical data, we project thatYelp’s penetration rate will grow from 4 to 5 percent as the cohorts mature.This number is quite conservative, but historical data have not pointed tomuch movement over time, even for Yelp’s earliest markets.Would you like to learn more about our Strategy & Corporate Finance Practice?Complete the forecast by estimating revenues per client. Again, data fromearly markets are relatively stable, averaging near $3,800 per business.Assuming average revenue per paying business increases at 3 percent per yearleads to revenue of $5,070 per business by 2023. Multiplying the number ofpaying clients in 2023 (423,000) by the average revenue per business leads toestimated total local-advertising revenue of $2.2 billion in 2023. Addingestimates of revenues for brand advertising and other services yields anestimate of total 2023 revenues of $2.4 billion.Next, we test our revenue estimate by examining potential market share in2023. BIA/Kelsey, a research and advisory company that focuses on localadvertising, estimated that local businesses spent $132.9 billion onadvertising in 2013, of which $26.5 billion was placed online. Between 2013and 2017, the research company expects online advertising to grow by 14percent per year, to $44.5 billion. Assuming that number grows by 5 percentper year, we estimated total online-advertising revenues will come to $60billion in 2023. Although search engines such as Google are likely to continueto capture the lion’s share of this market, there is still room for Yelp tocapture a portion of local advertising. Our estimate for Yelp in this exercisetranslates to a potential market share of 4 percent by 2023.