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Getting older can be a time when declining vision, hearing, and cognitive abilities may mean it’s no longer safe to drive. It may even lead to giving up your driver’s license. In theory, those who age out of driving should be perfect new customers for ride-sharing apps. And yet, Lyft says only 5.6% of its U.S. riders are older than 65. The company sensed a disconnect. The app wasn’t meeting older riders needs, and it needed a redesign. Lyft Silver, now available nationwide, is designed specifically for older users, with a font that’s 1.4 times bigger than the standard app, and a simple interface. [Image: Lyft] “Developing Lyft Silver was truly a labor of care and intention,” Audrey Liu, Lyft’s EVP of rider experience, tells Fast Company via email. “We started by listeningreally listeningto the experiences and needs of older adults. We spoke with riders, caregivers, and organizations that serve this community to understand the specific challenges they face with transportation. Things like navigating complex apps, feeling unsure about who their driver will be, or needing a little extra time and assistance.” The new design represents a collaboration among experts on aging, as well as partners like AltaMed, Urban League, Self Help for the Elderly, and others. The specialized app leans on Lyft’s findings about how its older customers actually use the service, like matching riders with more accessible vehicles that are easier to get in and out of since Lyft data showed older adults were twice as likely to cancel rides when they got matched with a pickup truck. And because Lyft found older adults are 57% more likely to not show up for their rides, the app has a “Get Help” button that connects riders to a live agent during work hours. Lyft Silver profiles also have trusted contacts, so ride details can be shared with family and caregivers. [Image: Lyft] “Personally, thinking about my own mom and aunt, and the desire I have for them to move through their day with ease and independence, was a huge motivator,” Liu says. “We focused on building features that directly address those paint points: things like a simpler app interface with larger buttons and clearer instructions, the option for drivers who have indicated a preference for assisting older riders, and a longer wait time to enter and exit the vehicle without feeling rushed. It was about creating a service that feels less transactional and more supportive, fostering a sense of comfort and trust.” It’s simple by design, and by basing the app on the needs and experiences of its actual users, Lyft Silver shows how tech companies can better adapt their services to an aging population.
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Chances are, if youre not an Italian grandma or a skilled home chef from Rome, youve probably messed up while trying to make cacio e pepe. At least, thats the thesis underpinning the scientific study Phase behavior of Cacio e Pepe sauce, published on April 29 in the journal Physics of Fluids. The studyconducted by a group of scientists from the University of Barcelona, the Max Planck Institute for the Physics of Complex Systems in Germany, the University of Padova in Italy, and the Institute of Science and Technology Austriais pretty much what its title suggests: a full-on scientific investigation into the most optimized recipe for the creamy, peppery pasta dish. Were Italians living abroad, and we often get together for dinner to enjoy traditional recipes from home, says Ivan Di Terlizzi, the studys lead author and a postdoctoral researcher at the Max Planck Institute. Among the dishes weve cooked, cacio e pepe came up several times, and every time, we were struck by how hard it is to get the sauce right. Thats when we realized it might actually be an interesting physical system to study. And of course, there was also the very practical motivation of avoiding the heartbreak of wasting good pecorino! A very brief history of pasta-based physics experiments This isnt the first time that pasta has been used as inspiration for physicists. Probably the most famous example of “pasta as experiment,” Di Terlizzi says, is the observation that spaghetti almost never breaks cleanly in half, tending to snap into three or more fragments instead. This fact originally puzzled renowned physicist Richard Feynman (who died in 1988) and wasnt fully explained until 2005, when a team of French physicists showed that its caused by cascading cracks traveling along the pasta. Another example, Di Terlizzi adds, is the physics of ring-shaped polymers, which are notoriously hard to understand. A study in 2014 used a type of circular pasta, which the researchers called anelloni, to explain why these looped polymers behave so strangely in experiments. With cacio e pepe, the physics question of interest has to do with the sauces unusual behavior under heat. The main goal of our work wasnt just culinary; it was to explore the physics of this system, Di Terlizzi says. The sauces behavior under heat shares features with many physical and biological phenomena, like phase transitions or the formation of membrane-less organelles inside cells. The recipe is, in a sense, the practical byproduct of everything we learned. The most optimal cacio e pepe recipe, according to scientists Cacio e pepe traditionally only includes three ingredients: pasta, pecorino Romano cheese, and black pepper. While it seems like a simple enough concoction, the sauces creamy smoothness (the backbone of the dish) can be quite finicky to achieve. When the temperature gets too high or the mixing of cheese and pasta water isnt done carefully, the cheese proteins will denatureessentially unfolding and losing their normal 3D structure. In the unfolded state, the proteins then stick together and the emulsion breaks. Instead of a creamy consistency, you get a gooey mess, which we call salsa impazzita . . . that is, crazy sauce, Di Terlizzi says. The physics-based solution to crazy sauce? Its all about starch. It turns out that, by perfecting the ratio of starch in the pasta water to cheese mass, the cacio e pepe sauce becomes far more resistant to heat, which stabilizes the emulsion and prevents clumping. [Chart: AIP Publishing] Without starch, the so-called mozzarella phase kicks in at around 65°C, where the proteins start forming large aggregates, Di Terlizzi says. But if the starch concentration is above 1% relative to the cheese mass, the clumps stay small, and temperature becomes much less critical, making it much easier to get a good result. This is similar to using polymers to stabilize emulsions in soft matter physics, he adds. Phase behavior of cacio e pepe sauce contains ultra-detailed steps to a foolproof cacio e pepe, but here are the instructions in condensed terms: Step 1: For a pasta dish for two hungry people, start with 300 grams of the preferred tonnarelli pastaor opt for spaghetti or rigatoni, if you must. From there, youll need 200 grams of cheese. Traditionalists would insist on using only pecorino Romano DOP [protected designation of origin], but some argue that up to 30% parmigiano Reggiano DOP is acceptable; though this remains a point of debate, the recipe notes. Proceed based on your own personally held cheese preferences. Step 2: To prepare the sauce, dissolve 5 grams of starchlike potato or corn starchin 50 grams of water. Heat this mixture gently until it thickens and turns from cloudy to nearly clear. This is your starch gel. Step 3: Add 100 grams of water to the starch gel. Instead of manually grating the cheese into the resulting liquid, blend the two together to achieve a homogeneous sauce. Finish the sauce by adding black pepper to taste (for best results, toast the pepper in a pan before adding). Step 4: To prepare the pasta, cook in slightly salted water until it is al dente. Save some of the pasta cooking water before draining. Once the pasta has been drained, let it cool down for up to a minute to prevent the excessive heat from destabilizing the sauce. Finally, mix the pasta with the sauce, ensuring even coating, and adjust the consistency by gradually adding reserved pasta water as needed. Step 5: Garnish with grated cheese and pepper, and serve.
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E-Commerce
Just two years ago, prompt engineering was hailed as a hot new job in tech. Now, it has all but disappeared. At the beginning of the corporate AI boom, some companies sought out large language model (LLM) translatorsprompt engineers who specialized in crafting the most effective questions to ask internal AIs, ensuring optimal and efficient outputs. Today, strong AI prompting is simply an expected skill, not a stand-alone role. Some companies are even using AI to generate the best prompts for their own AI systems. The decline of prompt engineering serves as a cautionary tale for the AI job market. The flashy, niche roles that emerged with ChatGPTs rise may prove to be short-lived. While AI is reshaping roles across industries, it may not be creating entirely new ones. AI is already eating its own, says Malcolm Frank, CEO of TalentGenius. Prompt engineering has become something thats embedded in almost every role, and people know how to do it. Also, now AI can help you write the perfect prompts that you need. Its turned from a job into a task very, very quickly. AI jobs are just jobs now Part of the prompt engineers appeal was its low barrier to entry. The role required little technical expertise, making it an accessible path for those eager to join a booming market. But because the position was so generalized, it was also easily replaced. Frank compares prompt engineering to roles like Excel wizard and PowerPoint expertall valuable skills, but not ones companies typically hire for individually. And prompt engineers may not be the only roles fading away. Frank envisions a world where AI agentsalready taking shapereplace many lower-level tasks. Its almost like Pac-Man just moving along and eating different tasks and different skills, he says. AI has the potential to displace thousands of workers. Its advocates have long argued that it will create as many jobs as it destroys. Prompt engineering once seemed to support that claima brand-new job title born from AI. But that optimism may be misplaced. Rather than inventing entirely new roles, AI is largely reshaping existing ones. Tim Tully wasnt surprised to see prompt engineering decline. As a partner at venture capital firm Menlo Ventures, hes witnessed the AI boom firsthand, especially through the firms investment in Anthropic. He also works closely with software developersa profession already transformed by tools like Cursor. His view is clear: The real impact of AI lies not in boutique job creation, but in widespread productivity gains. I wouldn’t say that [there are] new jobs, necessarily; it’s more so that it’s changing how people work, Tully says. Youre using AI all the time now, whether you like it or not, and its accelerating what you do. Did prompt engineers ever exist? It remains unclear whether companies were ever truly hiring for individually titled prompt engineers. They certainly arent now, says Allison Shrivastava, an economist with the Indeed Hiring Lab. It looks to me like prompt engineering is more being combined with, say, a machine learning engineering title or an automation architect title, Shrivastava tells Fast Company. Its probably a part of more job titles, but Im not necessarily seeing it as a job title in and of itself. But thats always been the caseeven in 2023, when LinkedIn was filled with self-described prompt engineers. Asked whether there was any change over time in the number of prompt engineer job postings, Shrivastava notes that it was never a large enough title to track mathematically. Which raises a larger question: Did prompt engineering roles ever truly exist? All experts interviewed for this piece were skeptical. The market itself was real enough: The North American prompt engineering market was valued at $75.5 million in 2023, with a compound annual growth rate of 32.8%. But whether that translated into formally titled roles is another matter. I think the discussion online of [prompt engineering] was probably much bigger than the head count, says Aline Lerner, CEO of Interviewing.io. It was such an appealing thing, precisely because it was this on-ramp for nontechnical people into this sexy, lucrative field. Where are the AI jobs, then? Lerner has observed a clear trend. While Interviewing.io has never offered mock interviews specifically for prompt engineering, it has offered them for machine learning engineering. The distinction is important: Prompt engineers focus on crafting questions for LLMs, while machine learning engineers build the models themselves. And while demand for the former has declined, demand for the latter is surging. Demand for mock interviews for machine learning engineers was flat for a while, and then in the last two months, it has hockey-sticked up and grown more than three times, Lerner says. The future is working on the LLM itself and continuing to make it better and better, rather than needing somebody to interpret it. Those easy-access AI jobs may no longer exist. Machine learning engineering roles demand deep technical expertiseskills that take years to develop, unlike the relatively shallow learning curve for prompt engineering. Even basic coding skills are no longer sufficient. Indeeds Shrivastava notes that while demand for developers is declining, engineering roles more broadly are on the rise. For those without a coding background, becoming a founder is often the most lucrativethough riskyroute. Management consulting has also seen a boom. As of February, consulting roles made up 12.4% of AI job titles on Indeed. As time goes on, we might see [AI] in more variety of sectors overall, Shrivastava says. They need someone tasked with really implementing that technology into that company.”
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