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Booking your last minute summer getaway? Delta recently revealed to what extent it’s using artificial intelligence to set ticket prices. And while this is just the beginning of the AI revolution in pricing, here’s some insight into how it’s working so far, and how it may expand to other industries in the future. What has Delta said about AI pricing? Delta recently made headlinessparking backlash from consumer groups and concern from Congresswhen president Glen Hauenstein revealed in the airline’s latest earnings call that the company is currently using AI for 3% of domestic pricing, with the goal of using it for about 20% by the end of the year. “So, we’re in heavy testing phase. We like what we see. We like it a lot and we’re continuing to roll it out,” Hauenstein said. “But we’re going to take our time and make sure that the rollout is successful . . . the more data it has and the more cases we give it, the more it learns.” When Fast Company reached out to Delta about how this might affect consumers, the airline referred to its letter to senators Ruben Gallego, Richard Blumenthal, and Mark Warner, which stated it is not “using, and [does not] intend to use, AI for ‘individualized’ pricing or ‘surveillance’ pricing, leveraging consumer-specific personal data, such as sensitive personal circumstances or prior purchasing activity to set individualized prices . . . There is no fare product Delta has ever used, is testing or plans to use that targets customers with individualized prices based on personal data.” What AI will likely end up doing, according to ThePointsGuy’s Clint Henderson, is creating a dynamic pricing model on steroids,” pinpointing flight prices with greater accuracy, and a whole lot faster. What variables go into AI airline flight pricing? Delta has partnered with Israel-based software startup Fetcherr, which utilizes its Large Market Model (LLM) to decode intricate market behavior and forecast financial trends, coupled with historical and current data. Fetcherr’s approach encompasses a comprehensive range of factors that influence market dynamics, and its recent white paper lists a few of those variables that go into airline flight pricing, namely: “seat availability, current news, weather conditions, significant events, flight schedules, and even fluctuating oil prices, among others.” The question on most passengers’ minds is whether working with companies like Fetcherr will lead airlines to use personalized pricing. Like Delta, Fetcherr told Fast Company its technology has been developed to streamline processes already in place at companies, and “does not allow for individualized or personalized pricing.” “Our Generative AI system does not and will not use, collect or receive any Personally Identifiable Information (PII) [and] remains steadfast in our commitment to transparency and to our compliance with applicable regulations,” a spokesperson for Fetcherr said in a statement. Delta isn’t the only airline using AI In its white paper, Fetcherr said it has also partnered with: Virgin Atlantic, UK; Viva Aerobus, Mexico; West Jet, Canada; and Royal Air Maroc, with a primary focus on optimizing key aspects such as pricing and inventory management. What’s next for Fetcherr AI after airline pricing? Looking ahead, Fetcherr said it plans to expand into other industries like hospitality, insurance, commerce, and capital markets, with its models expanding beyond pricing and inventory decisions to more complex challenges, such as making supply chain decisions and engaging in B2B strategic decision making.
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E-Commerce
Traders in the stock market can decide next week if they feel bullish about Bullish, as the cryptocurrency exchange announced today it plans to raise as much as $629.3 million in its U.S. initial public offering. The Cayman Islands-based company plans to offer 20.3 million shares for $28 to $31 each when it lists on the New York Stock Exchange. That would mean the company is valued at as much as $4.2 billion, making it the latest digital asset firm to court investors in the stock market. Bullish has applied for the ticker symbol BLSH, and the IPO is expected to price on Aug. 12. The company launched in 2021 as a spin-off of Block.one, with an initial investment of $10 billion from backers that included Peter Thiel. In 2023, Bullish acquired the crypto media brand CoinDesk. Crypto goes IPO Crypto companies have been a hot spot in whats been a pretty slow IPO market this yearand investors have been more than eager to snatch up shares. Circle Internet Group went public in June, and its shares surged nearly 750% above its IPO price in less than a month. And theres already robust demand for Bullish shares: Funds and accounts managed by BlackRock and ARK Investment Management are separately interested in buying as much as $200 million of shares in aggregate at the IPO price, according to the companys filing with the U.S. Securities and Exchange Commission. Bullish offers an interesting read on how far Wall Street has come to embrace crypto assets. The company, which offers crypto spot trading, margin trading, and derivatives trading, notes in its SEC paperwork that institutional investors account for a significant portion of its customer base. Tom Farley, who joined Bullish as CEO in 2023, wrote a letter in the SEC filing documenting his own introduction to digital assets, which began while he was president of the New York Stock Exchange. He recalled that his first lesson in crypto happened on a sunny summer day in 2014 while sitting on his porch with a neighbor who was enthusiastic about joining Coinbase, then a blockchain technology startup. Bullish banks on Trump-era momentum The continued growth of digital assets, which Bullish says have become established as a mainstream component of the global financial system, will be a major driver of business growth, along with other positive trends that include greater adoption by traditional financial institutions and increasing regulatory clarity. Various steps taken by Donald Trumps administration have helped to invigorate the crypto market, and the president is name-dropped a handful of times in the filing paperwork. While the successful debut of Circle could bode well for Bullish, investors have already cooled on eToro, which went public in May. While eToro shares are still trading above the $52-per-share IPO price, they have fallen nearly 13% from where the stock began trading. Of note, the SEC filing shows that Bullish reported a net loss of nearly $349 million in the three months ended March, compared with a profit of almost $105 million in 2024. And in 2022, the company scrapped an attempt to go public through a special purpose acquisition company (SPAC).
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E-Commerce
From Hollywood to Big Tech, major industries across the U.S. are increasingly going all-in on AI workflow tools, and theyre expecting employees to follow suit. Late last month, Business Insider reported that Microsoft has started evaluating some employees on their AI fluency, factoring their competency with AI tools into metrics like performance reviews. But in spite of the growing workplace incentive to adopt AI tools, some employees are actively resisting AI uptakeand their reasons make more sense than you might think. According to a new study conducted by a team of researchers at Peking University and The Hong Kong Polytechnic University, an emerging phenomenon is actively deterring employees from picking up AI tools, even at companies where doing so is strongly encouraged. Dubbed the competence penalty, this bias leads to AI users being seen as less competent by their peersregardless of actual performance. Its a perception gap thats especially damaging for women in technical roles. The background The researchers study was conducted at an unnamed leading tech company. In an article written for the Harvard Business Review (HBR), the studys authors explain that this company had previously rolled out a state-of-the-art AI coding assistant to its developers, which was promised to boost productivity significantly. Still, 12 months later, only 41% of the nearly 30,000 surveyed engineers had even tried the coding assistant. Adoption also varied based on employees identities. Just 39% of engineers 40 and older were using the tool, alongside a meager 31% of female engineers. Thats not for lack of trying on the companys part, either: Rather than throwing their employees into the AI deep end without guidance (a prevalent issue as AI workflow tools become more common), this company offered dedicated AI teams, adoption incentives, and free training. So, researchers set out to understand what was going wrong. The competence penalty To get to the bottom of this lackluster adoption pattern, the studys authors established an experiment with 1,026 engineers from the same company. The engineers were given a snippet of Python code to evaluate. While the code was the exact same for every participant, each was told that it was created under different conditionsincluding with or without AI and by a male or female engineer. The results showed that, when participants believed a fellow engineer had used AI to write their code, they rated that engineers competence 9% lower on average. The competence penaltys severity was also dependent on the reported gender of the engineer. If they were described as male, there was only a 6% competence reduction, compared to 13% for those described as female. Further, the reviewers own identity and stance on AI had an impact on how they rated others. Engineers who hadnt adopted AI themselves were most critical of AI-users, and male non-adopters penalized female AI-users 26% more harshly than their male AI-using counterparts. Through a follow-up study of 919 engineers, the researchers found that many employees were actually innately aware of this competence penalty, and were avoiding AI usage as a result. Those who most feared competence penalties in the tech industrydisproportionately women and older engineerswere precisely those who adopted AI least, the studys authors write. The very groups who might benefit most from productivity-enhancing tools felt they couldnt afford to use them. Women often face extra scrutiny The studys findings offer a strong counterpoint to the oft-repeated sentiment that AI tools might even the proverbial playing field at work, presenting a one-size-fits-all solution by making everyone more productive. Our results suggest that this is not guaranteed and in fact the opposite could be true, the authors write. In our context, which is dominated by young males, making AI equally available increased bias against female engineers. These results could help explain patterns that have already been observed in AI uptake. According to recent research conducted by Harvard Business School associate professor Rembrand Koning, women are adopting AI tools at a 25% lower rate than men, on average. In an article for Fast Company earlier this month, Kamales Lardi, author of the book Artificial Intelligence For Business, noted that, In my experience, women often face extra scrutiny over their skills, capabilities, and technical prowess. There may be a deep-rooted concern that leveraging AI tools may be perceived as cutting corners or reflect poorly on the users skill level. How leaders should prepare for the competence penalty Companies like the one in the study shouldn’t give up on implementing new AI tools, especially given that agentic AI is predicted to play a huge role in the future of work. Instead, leaders should use this data to put more AI adoption guardrails in place. In their analysis for HBR, the studys authors offer several main steps for managers to consider: Map your organizations penalty hotspots. Leaders should focus on identifying teams where the AI competence penalty might be highest, including those with more women and older engineers reporting to male non-adopters. Monitoring these teams might help to understand where and how the competence penalty is playing out. Convert the influential skeptics. Because non-dopters are the harshest critics of AI users, influential skeptics can have a major impact on the whole team. The studys authors suggest that breaking this cycle requires the skeptics to see respected colleagues successfully using AI without professional consequence. Redesign evaluations to remove the signal. Based on the study’s results, flagging a product as made with AI can negatively impact performance reviews. The solution is straightforward: Stop signalling AI use in performance evaluations until your culture is ready, the authors write.
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E-Commerce
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