Rajesh Jain

A question to ponder: “AI is already creating chat, audio and video representations that mimic deceased people. Will conversing with them comfort loved ones, or prolong the feeling of loss and prevent them from moving on? If the chatbots are flawed, could they warp our understanding of who the person really was?”

Porchlight Books: “Life revolves around learning—in school, at our jobs, even in the things we do for fun. Yet learning is often mysterious. Sometimes it comes fairly effortlessly: quickly finding our way around a new neighborhood or picking up the routine at a new job. In other cases, it’s a slog. We may spend hours in the library, yet still not do well on an exam. We may want to switch companies, industries, or even professions, but not feel qualified to make the leap. Decades spent driving a car, typing on a computer, or hitting a tennis ball don’t reliably make us much better at them. Improvement can be fickle, if it comes at all. In Get Better At Anything, Scott Young argues that there are three key factors in helping us learn: See: Most of what we know comes from other people. The ease of learning from others determines, to a large extent, how quickly we can improve. Do: Mastery requires practice. But not just any practice will do. Our brains are fantastic effort-saving machines, which can be both a tremendous advantage and a curse. Feedback: Progress requires constant adjustment. Not just the red stroke of a teacher’s pen, but the results of hands-on experience.”

WSJ: “TV commercials have long stood as the cornerstone of modern advertising. This dominance was owed, in part, to TV’s capacity to reach vast and diverse audiences through ads that leverage sound, sight and motion to evoke emotional responses. These vast audiences aren’t tuning in anymore. “There is no longer that single lever you can pull,” said Vinny Rinaldi, Hershey’s U.S. head of media and analytics, referring to the role that television once played in advertising. The chocolate giant said the share of advertising dollars it spends on TV fell to about 30% from roughly 80% in five years. Brands have been preparing for the inevitable decline of television for years, but many had held out hope that the rise of ad-supported streaming TV would plug the gap. So far, that isn’t happening.”

Shantanu Narayen: “We are in the business of investing in technology. A couple of things have really influenced how we think about it at the company. Software has an S-curve. You have things that are in incubation and have a horizon that’s not immediate, and you have other things that are mature. I would say our PostScript business is a mature business. It changed the world as we know it right now. But it’s a more mature business. And so, I think being thoughtful about where something is in its stage of evolution, and therefore, you’re making investments certainly ahead of the “monetization” part, but you have other metrics. And you say, am I making progress against metrics? But we’re thoughtful about having this portfolio approach. Some people call it a horizon approach and which phase you’re in. But in each one of them, are we impatient for success in some way? It may be impatient for usage. It may be impatient for making technology advancements. It may be impatience for revenue and monetization. It may be impatience for geographic distribution. I think you still have to create a culture where the expectations of why you are investing are clear and you measure the success against that criteria.”

@aish_caliperce: “There’s a notion that AI will help cut down costs and increase efficiency. However, Marc presented a strong counterargument by referencing Jevon’s paradox. Jevon’s paradox: The Jevon’s paradox occurs when technological progress increases the efficiency with which a resource is used (reducing the amount necessary for any one use), but the falling cost of use induces increases in demand enough that resource use is increased, rather than reduced. He gave some eg of Jevon’s Paradox: ->Building roads will only lead to more cars & resulting in traffic. ->CGI in hollywood was developed to reduce the cost of film making but people’s expectation increased, so cost involved in CGI also increased. ->Coal consumption increased in Industrial Revolution when the coal prices decreased. He says,The paradox here making cost of a given piece of software would be reduced, but the massive surge of demand for more powerful softwares will actually increase the cost of building a software company. Customers will start seeking for more & more powerful features.”

Andrew Chen: “There is a reinvention of growth channels, but today’s most effective tactics are very different than what existed before. While we are unlikely to install new apps, we are willing to follow new creators, or share videos/links/photos. We spend a ton of time on social media, within video apps, and comms/collab products in the workplace. It’s partly why creators, short-form video, and shareable memes have become such important growth drivers for new startups today, even though sometimes the spikes are short and ephemeral. And within the workplace, why PLG and bottoms-up growth are often fueled as much from co-workers sharing content as much as observing content posted by AI influencers.”

Mint: “India’s ascent to a middle income-economy hinges on better education and continued focus on infrastructure creation, Asian Development Bank (ADB) chief economist Albert Park said, citing the successful experience of other developed economies. India should also commit to remain an open economy and review import tariffs that may be making inputs costlier for sectors where it has an advantage, Park said on the sidelines of ADB’s annual meeting in Tbilisi…Park said that if ADB were to do an economic diagnosis of the country, education would be among the priority areas where it should really improve the quality since becoming a middle-income country means moving up the technology ladder.”

FT: “Artificial intelligence companies that have spent billions of dollars building so-called large language models to power generative AI products are now banking on a new way to drive revenues: small language models. Apple, Microsoft, Meta and Google have all recently released new AI models with fewer “parameters” — the number of variables used to train an AI system and shape its output — but still with powerful capabilities. The moves are an effort by technology groups to encourage the adoption of AI by businesses who have concerns about the costs and computing power needed to run large language models, the type of technology underpinning popular chatbots such as OpenAI’s ChatGPT.”

Deirdre McCloskey: “The price system doesn’t guarantee nirvana, heaven, perfection. But beware of making the imagined perfect the enemy of the actual pretty good. Money prices don’t value us ethically. But they have yielded a 3,000 percent increase in human material welfare since 1800. Not too shabby. Let’s keep it going.” [via CafeHayek]

WSJ: “More online calculators, wearable devices and medical tests are attempting to estimate your heart’s age. The companies and organizations behind the tools say that having insight into your heart health can prompt you to make lifestyle changes to help stave off cardiovascular disease down the road. It’s an extension of our newfound obsession with “biological age,” the concept that your body, or parts of it, can be physically aging faster or slower than your actual age. And that by knowing those ages, you can take control to live longer and healthier. As for the heart, scientists say the tools can be a helpful jumping-off point for conversations with doctors about habit changes or medications before heart disease sets in.”

Martin Wolf reviews “The Longevity Imperative”: “We need to rethink old age, as individuals and societies. We must not shuffle a huge proportion of our society into unproductive and unhealthy “old age”. We can and must do far better, both individually and socially. This is [the] “imperative”. Barring a disaster, there are going to be far more very old people: in 1990, there were only 95,000 people over 100 years old in the world. Today, there are over half a million, and rising.”

Lari Hämäläinen: “When we talk about gen AI agents, we mean software entities that can orchestrate complex workflows, coordinate activities among multiple agents, apply logic, and evaluate answers. These agents can help automate processes in organizations or augment workers and customers as they perform processes. This is valuable because it will not only help humans do their jobs better but also fully digitalize underlying processes and services. For example, in customer services, recent developments in short- and long-term memory structures enable these agents to personalize interactions with external customers and internal users, and help human agents learn. All of this means that gen AI agents are getting much closer to becoming true virtual workers that can both augment and automate enterprise services in all areas of the business, from HR to finance to customer service. That means we’re well on our way to automating a wide range of tasks in many service functions while also improving service quality.”

Rita McGrath and M Muneer: “Inflection points often burble along for decades. When they cross a tipping point, we say, ‘This came completely out of the blue!’ Take internet advertising, particularly programmatic ads that leverage data to target you and aim to sway your decisions. 97% of Meta‘s and 80% of Google’s revenue comes from programmatic ads, while Amazon rakes in close to $47 bn. Worldwide spend on digital advertising will be about $740 bn in 2024. What if all this spending is based on misconceptions about personal behaviour and the illusion of precision that these platforms offer? From an advertiser’s perspective, the more funds that go into creating compelling content and targeting the right potential buyers, the better. Everything else is a waste. But reality is different.”

WSJ: “Demographics are supposed to be a slow-moving force, but the baby bust is happening so quickly and so widely that it’s taken many by surprise…In high-income nations, fertility fell below replacement in the 1970s, and took a leg down during the pandemic. It’s dropping in developing countries, too. India surpassed China as the most populous country last year, yet its fertility is now below replacement. “The demographic winter is coming,” said Jesús Fernández-Villaverde, an economist specializing in demographics at the University of Pennsylvania.”

Ninety-five theses on AI by Samuel Hammond.Among them: “The last 12 months of AI progress were the slowest they’ll be for the foreseeable future. Scaling LLMs still has a long way to go, but will not result in superintelligence on its own, as minimizing cross-entropy loss over human-generated data converges to human-level intelligence. Exceeding human-level reasoning will require training methods beyond next token prediction, such as reinforcement learning and self-play, that (once working) will reap immediate benefits from scale. RL-based threat models have been discounted prematurely. Future AI breakthroughs could be fairly discontinuous, particularly with respect to agents. AGI may cause a speed-up in R&D and quickly go superhuman, but is unlikely to “foom” into a god-like ASI given compute bottlenecks and the irreducibility of high dimensional vector spaces, i.e. Ray Kurzweil is underrated.” And this: “Biology is an information technology.”

ET: “India is poised to become the world’s third-largest consumer market by 2026, surpassing Germany and Japan. UBS forecasts a doubling of affluent individuals with annual incomes exceeding $10,000 to 88 million by 2028. Household spending per capita in India is projected to outstrip other Asian economies, with rural and urban consumption expenditures witnessing significant growth over the past decade, according to recent reports…All this means that consumption in India is not only rising but also maturing, from food to non-food items.”

WSJ: “Asking a chatbot to act as an expert—even a long-dead one—can yield better results, or at least be more entertaining. If you’re seeking information about designing a midcentury modern house, tell it to “act as architect Frank Lloyd Wright.” Here’s how Gemini began its response, which listed five useful tips: “Alright, here’s Frank Lloyd Wright speaking to you about your future home…”…Why does this technique work? “If you ask the AI to ‘imagine you’re a world-renowned chef,’ you’re signaling that you want a response that reflects culinary expertise,” Copilot responded when I asked it the question. “The AI will then generate a response that aligns with what it has learned about how chefs talk and the kind of information they might provide.” At the other end of the scale, telling a chatbot to act like a middle-school teacher should produce simple explanations for hard-to-understand concepts, such as how nuclear power is created or the techniques used in gene editing.”

FT: “An AI assistant [is] a personalised bot that is supposed to help you work, create or communicate better, and interface with the digital world on your behalf. This new class of products has stolen the limelight…among a flurry of new AI developments from Google and its AI division DeepMind, as well as Microsoft-backed OpenAI. The companies simultaneously announced a series of upgraded AI tools that are “multimodal”, which means they can interpret voice, video, images and code in a single interface, and also carry out complex tasks like live translations or planning a family holiday…While smart assistants powered by AI have been in train for nearly a decade, these latest advances allow for smoother and more rapid voice interactions, and superior levels of understanding thanks to the large language models (LLMs) that power new AI models. Now, a fresh scramble is under way among tech groups to bring so-called AI agents out to consumers.”

WSJ: “Job seekers, frustrated with corporate hiring software, are using artificial intelligence to craft cover letters and résumés in seconds, and deploying new automated bots to robo-apply for hundreds of jobs in just a few clicks. In response, companies are deploying more bots of their own to sort through the oceans of applications. “You’re fighting AI with AI,” said Brad Rager, chief executive of Crux, a recruiting firm that matches cybersecurity specialists with employers. The AI arms race is bad for job candidates, he said, who feel defeated when online applications come to nothing, and for employers, who are frustrated when imprecise AI tools highlight weak candidates. “There’s so much promise, but there’s a lot of crap and garbage,” Rager said of the tools used by employers.”

NYTimes: “The root of the problem is the Communist Party’s excessive control of the economy, but that’s not going to change. It is baked into China’s political system and has only worsened during President Xi Jinping’s decade in power. New strategies for fixing the economy always rely on counterproductive mandates set by the government: Create new companies, build more industrial capacity. The strategy that most economists actually recommend to drive growth — freeing up the private sector and empowering Chinese consumers to spend more — would mean overhauling the way the government works, and that is unacceptable. The party had a golden opportunity to change in 1989, when the Tiananmen Square protests revealed that the economic reforms that had begun a decade earlier had given rise to a growing private sector and a desire for new freedoms. But to liberalize government institutions in response would have undermined the party’s power. Instead, China’s leaders chose to shoot the protesters, further tighten party control and get hooked on government investment to fuel the economy.”

Jaspreet Bindra: “There is no Google product which is not going to be baptized with AI. Google Search, with 2 billion plus users and 6 million searches a minute, gets a GenAI makeover. Gmail with 1.8 billion users gets a strong dose of Vitamin AI. YouTube’s 1.8 billion users can have AI-generated text summaries of the nearly 4 billion videos that the site hosts. Another 4 billion Android users get AI on tap. The list goes on. Ironically, however, it seems that Google is following the Microsoft playbook here. Microsoft famously had an EEE strategy of ‘Embrace, Extend and Extinguish’: First it created a product using open standards, then created a proprietary extension which quickly gained dominance through its brute distribution and ownership of the PC market, and it finally used this extension to swamp the market and extinguish its competitor. Latest example: MS 365 has 345 million users, 320 million of them get Teams free; rival Slack languishes at 39 million. So, OpenAI the plucky innovator can launch eye-popping products galore like ChatGPT, Sora and GPT4o, but what it lacks is distribution reach.”

Aaron Levie: “The reason I’m insanely bullish on AI is that since starting Box, we have never seen a bigger shift in how we can work with our enterprise information than today. AI completely revolutionizes how we can work with enterprise information. Since the mainframe era, it’s been relatively trivial to work with our *structured* data in an enterprise. We could query, compute, synthesize, summarize, and analyze anything that could be structured in a database – i.e. the data sitting in our ERP, CRM, and HR systems. But it turns out this is only a small fraction of our corporate information. If you were to “weigh” the amount of data inside of an enterprise (in the form of raw storage), roughly 10% of it would be structured data, and 90% of it would be unstructured data. And our content — things like our documents, contracts, product specs, financial records, marketing assets and videos — makes up the vast majority of this corporate data. Yet for essentially the entire history of computing, we haven’t *really* been able to make sense of this information unless a human is involved. Of course we can store it, send it, share it, and search for it — but deeply understanding what’s inside this information in a way that computers can interact with intelligently has been near-impossible. Well, for the first time ever, generative AI actually lets us talk to our unstructured data. Multimodal models especially allow us to process this content using a computer and essentially perform any task that a human can, but at infinite scale and speed. This is utterly game-changing when working with information in the enterprise. Instantly, our content goes from being digital artifacts that get touched once in a while, to a digital memory that anyone in the enterprise can tap into always. All of a sudden instead of the more information you have making things harder to find and make sense of, the opposite becomes true. And we enter a world where your digital information becomes one of your most valuable resources. When we can turn our content into valuable knowledge, everything about how we work changes. A new employee instantly has access to the same expertise of someone who’s worked at a company for 15 years; when you can understand what’s inside of content — like contracts, invoices, or digital assets— and extract its structured data, you can automate nearly any workflow; and AI can let us classify and protect content with a level of precision that’s never been possible before to prevent threats and risks across the enterprise. This is simply the biggest change we’ve ever seen with how we can work with our data, and this is what we’re building with Box AI. In 1945, Vannevar Bush wrote a seminal article which outlined eerily insightful predictions, including the idea of the “Memex”, a new device “in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility. It is an enlarged intimate supplement to his memory.” The vision laid out imagined a future where the more knowledge and information your “computer” had, the smarter and more informed you would become. While many aspects of PCs, mobile devices, and the cloud eventually resembled this early vision, the seamlessness in how we could work with our information never quite played out. Until today.” [This quote is so good that I reproduced it entirely. Memex is one of the things I was fascinated with many years ago.]

Andrew Chen: “Just as coupons are one marketing tactic that sits on the “surface area” of newspapers and mail, I think we are bound to see the surface area of “conversations with my companion friends” become host to new marketing interactions as well. This might seem weird right now, but I think we will simply have various flavors of companion friends, some who help us with specific tasks (and who are obviously going to recommend products— as ads in a podcast commercial might), and friends who are highly engaging and mostly non-commercial in nature. Sometimes I talk to a gym trainer companion who I know will plug the occasional nutritional products, and other times I talk to my funny gamer companion who tells me about the best new games. Is this any more weird than watching my favorite YouTube creator talk about a product they love (and that they are being paid to talk about)? These are all parasocial relationships, but the future AI ones will simply be interactive, more informative, and more engaging.”

John  Luttig: “Open and closed-source AI cannot both dominate in the limit: if centralizing forces hold, scale advantages will compound and leave open-source alternatives behind. Despite recent progress and endless cheerleading, open-source AI will become a financial drain for model builders, an inferior option for developers and consumers, and a risk to national security. Closed-source models will create far more economic and consumer value over the next decade…Open-source will have a home wherever smaller, less capable, and configurable models are needed – enterprise workloads, for example – but the bulk of the value creation and capture in AI will happen using frontier capabilities. The impulse to release open-source models makes sense as a free marketing strategy and a path to commoditize your complements. But open-source model providers will lose the capital expenditure war as open-source ROI continues to decline.”

Henry Oliver in his book “Second Act”: “What this showed is that processing speed (matching numbers and symbols) peaks much earlier than working memory (unfamiliar shapes and reciting lists of numbers). These are both aspects of fluid intelligence, but they peak at different times. The idea that fluid intelligence is one thing and declines early isn’t quite right. There are many aspects to intelligence and they peak at different ages throughout our lives. The authors of the study say: ‘Not only is there no age at which humans are performing at peak at all cognitive tasks, there may not be an age at which humans are at peak on most cognitive tasks.’” [via Tyler Cowen]

Ray Hennessey: “Apologies are the first step toward, if not forgiveness, then reacceptance into the communities our actions shattered. They are so prevalent because they tend to work. At bottom, we focus on the worst part of the cycle after our misdeeds—the yelling, the opprobrium, the digital torches and pitchforks of the anonymous, social media mob. Yet, the fire of anger, hurt and public shaming almost always dies down. An apology may not be accepted at first, but the fact that it’s offered shows people that you’re truly sorry, accept the consequences, are ready to learn and are ready to change. An apology allows you not only to speak to your detractors, but to also allow your defenders and supporters to more willingly re-admit you into their circles. An apology allows people to create a frame of reference to keep you accountable for the future. An apology lets you get back on the path of getting your life back.”

ET: “Deepak Shahdadpuri, MD of venture capital firm DSG Consumer Partners, said: “There is no connection between someone who is 18 years old and incumbent brands. There is zero emotional connection.”…”In the old days…it was difficult to build awareness, distribution was hard, and the only brand you got were the brands you saw,” Shahdadpuri said. “Now, all the brands are starting from scratch. You have to earn the right to be purchased – that’s the starting point.”…”Gen Z wants to consume from brands which their parents, or the older generation, were not consuming because they have this innate desire to stand out,” Jatan Bawa, cofounder at Perfora told ET. “They’re also very inclined towards design, which is a big element that goes into their purchase behaviour and decision-making approach. They are more about consuming a brand rather than consuming a product,” he said.”