SUCCESS

Strategist Scott Rubner says a growing list of stock-market buyers are pushing it in a positive direction, notably big hedge funds and institutional managers.

The flight-tracking company voluntarily dismissed, without prejudice and without sharing a motive for its decision. However, the flight cancellation market never took off with Kalshi users.

TheStreet aims to feature only the best products and services. If you buy something via one of our links, we may earn a commission.Why we love this dealSome folks may prefer “roughing it” when it comes to outdoor activities like camping, but we believe that products like the Pnkkodw Heated Camping Chair only make the experience better. Why settle for being cold and miserable when we can be nice and toasty and still get to enjoy a night or two under the stars? These days, there’s all kinds of fun things to buy that make everything from a few nights camping in a tent to a a day out on the beach even more enjoyable than before, and it’s made even more fun for us shoppers when those exciting accessories, knick-knacks, and gadgets are available for a super great price.The Pnkkodw Heated Camping Chair usually goes for $110, but for a limited time, the tricked-out chair is on sale for 26% off during a Walmart Flash deal. You can now get the chair with the full battery pack for only $81, and while the weather might be a bit too warm to use the heated feature right now, soon those autumn temperatures will have you happily cranking up the heat when the sun sets and the chill hits. Pnkkodw Heated Camping Chair, $81 (was $110) at Walmart

Courtesy of Walmart

Shop at WalmartWhy do shoppers love it?Powered with a rechargeable battery back, which you can buy the chair with or without if you already own one, this heated camping chair is the cozy way to stay warm even when the air gets cool. Measuring 35 inches long, 34.3 inches wide, and 38 inches high when it’s fully set up, the chair has a semi-enclosed design with an ergonomic back and dual arm support, as well as a built-in cup holder, a pillow, and side pockets for storage. What’s unique about it, besides the heating capabilities, is that it has a rocker base so you can move back and forth, gently swaying and enjoying the consistent soothing rocking motion, even when you aren’t on a granite or concrete surface. The chair has a steel frame and is made with 600D Oxford fabric and padded with 140G thick polypropylene cotton. In combination, the two materials ensure that the chair is very durable and sturdy, with a weight capacity of 330 pounds. It’s water-resistant, tear-resistant, and able to keep up with lots of use and wear without deteriorating in quality. Underneath that Oxford fabric sits a dual-zone heating that provides warmth when plugged into the included 2000 milliampere-hour battery pack (or one of your own choosing).The heating pad can provide warmth up to 126 degrees Fahrenheit, with three adjustable temperature stages. The heating pad not only is in the seat area but it runs up along the back so your full body is able to feel the warmth it emits. What’s convenient is that you can always choose not to engage with the heating feature, which provides lots of chair use when the weather is hot or when you simply don’t need the extra heat. Related: REI has a 40-degree sleeping bag that’s great for camping for just $45The chair comes in five colors and with a convenient carrying case that makes traveling with it a breeze. Details to knowDimensions: The chair measures 35 inches long, 34.3 inches wide, and 38 inches high. Material: Steel and 600D Oxford fabric.Weight limit: 330 pounds.Colors: Five. Called “the dream outdoor chair” and the “most comfortable chair ever” by shoppers, this camping chair is super cozy and provides a ton of warmth when you need it to. Not only does it provide great support to your back and head, but the rocking base adds an extra touch of relaxation. Tons of folks use it for other purposes besides camping, and say it’s perfect for folks who also suffer from pain or soreness. “We have two and everyone wants to sit in them,” one shopper said. Shop more deals Fundango Oversized Padded Loveseat Camping Chair, $86 (was $160) at WalmartCamkey Folding Bed Cot, $58 (was $115) at WalmartFairyland Sleeping Bag, $34 (was $50) at WalmartYou don’t have to be a camper to reap the benefits of the Pnkkodw Heated Camping Chair, but it certainly will make your next trip out to the woods that much more enjoyable. Heck, even in the backyard, it’ll make resting and relaxing so much easier. 

Non-qualified investors face a 300,000-ruble (approx. $3,600) annual purchase limit per intermediary, while qualified investors have no cap.

Is your bank account secretly damaging your brain? In this eye-opening interview, Dr. Jacques Wells breaks down groundbreaking research revealing that financial hardship isn’t just stressful — it may be a direct driver of cognitive decline. We explore why the feeling of money worry can be more harmful than your actual income, how constant financial stress crowds out the mental “brain space” needed for protective activities, and why decades of accumulated hardship take a measurable toll on your mind. If you’ve ever felt overwhelmed by bills, this conversation will change how you think about the connection between money and your mental health.Jeffrey Snyder, Broadcast Retirement NetworkAnd joining me now is Dr. Jacques Wells. Dr. Wells, it’s great to see you. Thanks for joining us in the program this morning. Thank you for your invitation, Jeff. Very happy to be in your program.And I’m very happy. I love this research. You know, doctor, financial hardship can hit you in the wallet and in the pocketbook, but based on your research and your study, it can also hit you in your cognitive abilities.Tell us about that.Jacques Wels, PhD.,, University College LondonYeah, it hurts your brain, actually. And we didn’t know that before. So it’s not only financial hardship, but it’s actually the accumulation of this hardship over your life course.So if you pour all over your life, it has a huge effect on your brain. If you pour a little bit over your life, the effect is less strong. So it’s really the accumulation of this hardship over time that has effect on brain and huge effect actually.So it really leads to cognitive decline. That’s what the study showed.Jeffrey Snyder, Broadcast Retirement NetworkSo did you get a sense from the study that it was a specific type of financial decline or hardship? For example, can’t pay my bills, credit card debt, stock market going up or down, or going down, excuse me. Did anything in particular trigger the cognitive decline?Jacques Wels, PhD.,, University College LondonGo ahead, doctor. Well, yeah, no, we didn’t look at so much detail, but we had two variables. So, you know, we’re using a study where we track people over time.So it’s what we call a cohort. So it’s a group of people who were born in the UK in 1946. And we got, I mean, I wasn’t born there, but we gave them questionnaire all over the life course.And we have information about their financial status at different time points throughout their lives. And we had two main questions we were really interested in. The first one is household incomes.So we just got information on how much the household got. And we can see, actually we created a variable where we look at the lowest income. So people earning less than 20% of the population incomes.So that’s one of the variable. And the other one was, I think, an even more interesting variable and with even stronger results, but it’s self-reported financial hardships. How do you cope in general?And if your answer is no, and if you answer no over your life course, so over and over and over again, huge connection with cognitive decline. So we really have two measures. One that is more objective, money, household money, and the other one that is more subjective.How do you feel about money? And both have an effect, but the huge effect is really on the subjective variable.Jeffrey Snyder, Broadcast Retirement NetworkSo is some of this, does some of this have to do with expectation? So for, you know, and I don’t know how you, if you could even test for this, but if I have an expectation that I’m going to get X, Y, and Z in terms of my finances, therefore, if I don’t get X, Y, and Z, I’m going to be disappointed. And therefore I’m going to, you know, be all down and shrug my shoulders and all that kind of stuff.So does that, does expectation play a role here?Jacques Wels, PhD.,, University College LondonWell, as you said, we were not able to test exactly the mechanisms that explain that. We just see an association between money troubles and worry about money and cognitive decline. So you can make different assumptions.So there’s two assumptions we actually made, but we didn’t test them. I need to be clear about that. The first one is about behaviour.If you poo all over your life, you’re likely to have different kinds of health behaviours. We know that poverty is linked to alcohol consumption, smoking, et cetera. And all these behaviours might have an effect on your cognitive decline.So that’s one set of explanation. The other set of explanation, I think related to what you said, it’s worry. So actually, if you worry all over your life about paying your bills, to speak frankly, if you’re worrying about paying your bill, you have less brain space to think about something else.You know, as I explained to someone recently talking about this study, if you’re worrying about money all the time, you have no time to play chess. You know, you don’t want to spend one hour on evening thinking, okay, I’m going to think about chess. No, you’re worried all the time and you have less brain space to think.And actually, we really know in other research that you’re actually using your brain, doing interesting things. Chess is a very good example. Prevents against cognitive decline.Less space, less prevention. So it might be connected. But again, I repeat, we didn’t test actually for the mechanisms.Further studies should do that in the future. And if there are researchers in your audience, and I’m sure there are, I encourage them to really look at the mechanism that explain this relationship in death.Jeffrey Snyder, Broadcast Retirement NetworkSo is the lesson about coping? Because when you go through life, I mean, I’m 54 years old, and it’s not a straight line. There are ups and downs.So is it about learning, maybe I’m a Gen Xer, maybe my generation didn’t learn to cope as well. So is it maybe about learning to cope with some of these ups and downs that will help you long-term? Again, I know you didn’t test for this, but I’m just trying to draw some distinctions and tease out maybe what we can take away from this if we’re trying to be better.Jacques Wels, PhD.,, University College LondonNo, that’s a very good question. And I think learning to cope is definitely important, but to what extent can you cope is another question. I think what the study shows, and I think that’s an interesting point, is that most of these in the past looked at two things.They look at genetics. We know that genetics, having a certain gene explain cognitive decline. So a lot of studies looked at genetics.A lot of studies, as I mentioned, looks at behaviour. You smoke, you drink, you have an unhealthy life. You can, okay.So the pressure was on genes. You cannot do anything about genes, right? You cannot decide about, yet.The other one was about behaviour. You can do something on your behaviour. So we had this dual explanation and we just offer a third way, which is a societal impact.Saying, okay, of course it’s a behaviour. Of course, it’s our genes explaining our cognitive decline. But it’s also about what we experience as human being.We don’t have grip on. And I think that’s a good point. So coping, yes, it’s important, but it’s also a policy message saying that very simply, uncertainty over your life might have a negative effect on your brain.And we can do something on this uncertainty. Another question, and it is a political and very open question, is who should deal with this uncertainty? Is it an individual thing we need to deal, to cope with, or is it something the society needs to think about?My point being that if we want to prevent cognitive decline, there is something we can do, which is not only in terms of behaviour, but that is also in terms of how can we protect people over the life course.Jeffrey Snyder, Broadcast Retirement NetworkSo let’s talk about that. I’m sure you’ve got a lot of, we picked up a story about the research. I’m sure you have had policy makers.You’re in right now in London, but you typically teach in Brussels. I’m sure you had people reach out to you. What are policy makers, are policy makers asking what can we do?Because candidly, doctor, whether it’s the States, whether it’s Europe, whether it’s Asia, we have an ageing society. This is a issue that will continue to pop up because the world is ageing. We’re ageing, we’re getting older.Jacques Wels, PhD.,, University College LondonWell, that’s a very good, I mean, that’s a very broad question. And as you said, I’m in Brussels, I’m in London, I’m working with Japan. So we see that this is an issue that exists everywhere, actually.And I mean, if you look at London and Japan, it’s very interesting because not so much in Brussels, and I’m sure it’s the same in the US. If you look, there’s a real debate about needs. I don’t know if you heard about that.So that’s people not in employment, education and training. And we actually see a dual relationship, again, that is of the same nature. People with poor health are more likely to be in need, and people being in need are more likely to have a poor health.And this reinforces over the life course and has dramatic effect over the long run. So it’s something politicians think about, and there’s a lot of policy discussions. My point, I will say, that I’m a little bit afraid that policy discussions are very often about behaviours only.As I’ve said, it’s a triangle where we have genetics, behaviours, and what the society does for you. And a lot of the debate is only about behaviours, how we can make people behave better, drink less, smoke less, et cetera. Years ago, if you look at Europe, for example, there was a debate that is very updated.You probably don’t know that, you’re too young, but about something called, yes, you are, about the flexibility. So there was a debate, at least at the European Union, on how we can create a labour market where there is flexibility. You can move from one job to another, you can take risks.But at the same time, if you take a big risk, you’ll get a net. You’ll be protected if you fall too low. And I’m afraid that this net doesn’t exist that much anymore.The debate about need that I was talking about in the UK is a lot today about reducing social benefits, for example. So my point is that if we reduce social benefits, we actually reduce the net. What is the effect over the long term?It can have huge effect, and that’s what the study shows. It’s actually, if you’re poor, if you don’t have that security, it damages your brain. So yeah, I would say that the policy issue, according to me, is that there’s a lot of focus on behaviour, very important to focus on behaviour.We can do a lot about our health, but we need also to have a more society perspective on health and try to understand how society shapes health independently on our individual behaviour. I can quit smoking, I should. I can quit drinking, I should.But if I’m poor, I’m poor. And quitting being poor is quite hard. You would agree with me.Jeffrey Snyder, Broadcast Retirement NetworkYeah, and I would also add, in addition to financial hardship and other hardships, now you’ve got artificial intelligence that I think is creating a lot of angst among people. So that only adds to the burden. Dr. Wells, we’re gonna have to leave it there. It’s a great study. Thanks so much for doing the work and for joining us on the program. We look forward to having you back on the program again soon, sir.Jacques Wels, PhD.,, University College LondonThank you for the interview, Jeff. Thank you very much for your time.

Key TakeawaysAI is a major growth driver for stocks, but infrastructure bottlenecks could cut into cash flows for the largest companies, says Jennison’s Blair Boyer. Only about 20% of businesses use AI in any business function, so opportunities are still in its early innings, he says.Growing concentration in huge stocks has raised the importance of balancing with next-generation names.It’s been a tricky year for growth stock investors and Big Tech. The group started 2026 on the defensive, as investors rotated out of names that had benefited from the massive AI buildout. But sentiment has again improved, as the wave of AI capex shows no signs of abating. The rebound in growth stocks lifted the near-term fortunes of growth stalwarts like Silver medalist PGIM Jennison Growth Fund PJFZX, Bronze medalist PGIM Jennison Focused Growth ETF PJFG, and Silver medalist Harbor Long-Term Growers ETF WINN, all of which are run by investment manager Jennison, a unit of Prudential Financial.The three funds favor businesses with strong market positions, healthy balance sheets and durable competitive advantages. They’re concentrated portfolios; PGIM Jennison Growth has 53 holdings and a turnover of 34%. Jennison has “shown over longer time periods that it can identify the next generation of growth leaders, and this strategy has enough talent behind it to outperform its peers over time,” writes Morningstar analyst Natalia Arrigoni. We checked in with Blair Boyer, Jennison’s co-head of growth equity, about what lies ahead for the AI juggernauts and other growth companies. Boyer walked us through his theses on Eli Lilly LLY and Snowflake SNOW, and explained how he views Microsoft MSFT, Apple AAPL, and other key stocks.Leslie Norton: What do you expect for growth stocks in the second half 2026?Blair Boyer: Strong underlying corporate profits have been the rock that kept people in the market. Consensus operating earnings growth estimates for the S&P 500 are in the high-teens-to-low-20% range for 2026 and 13%-15% for 2027. These consensus numbers appear reasonable.Higher market volatility is a fact of life at this point. The first quarter was more around geopolitics. Inflation has proved stickier than expected. Then there are severe physical and operational constraints facing the AI infrastructure buildout. According to reports, over half all global AI data center construction projects are facing cancellations or delays, including high-profile expansion plans of Oracle ORCL and OpenAI. While combined hyperscaler AI capex has surged past $700 billion for 2026, companies still can’t bring on capacity fast enough to meet demand. Amazon revised its 2026 capex guidance higher, but said it still will not meet this year’s demand.Norton: What are you thinking about the returns on the capex binge?Boyer: Consensus estimates for capital spending by the five largest hyperscalers—Amazon AMZN, Alphabet GOOG, Microsoft, Meta META, and Oracle—are approximately $1.0 trillion in 2027 and $1.3 trillion in 2028. Free cash flow may turn slightly negative in 2027 as they scale to meet accelerating demand. The substantial cash generation should support a durable, long-duration return opportunity for investors. We already see early evidence of returns on AI-related capital spending, particularly among the hyperscalers. Our discussions with management teams suggest demand for AI compute could exceed available capacity for multiple years. That demand is appearing in the reacceleration of cloud revenue growth and margin contribution for Amazon, Microsoft, and Alphabet, and in the rapid annual recurring revenue growth of leading private frontier model companies such as OpenAI and Anthropic. Amazon is a good example. In the most recent quarter, AWS revenue growth accelerated to 37%, its fastest pace since 2021. Despite above-market growth, many hyperscalers and other AI-infrastructure-focused companies are trading at P/E multiples in the 20 range or lower. The market is actively pricing in timing uncertainties, power grid limitations, and construction bottlenecks. Norton: Are these benefits filtering through to non-tech companies? Boyer: From the discussions we’ve had across businesses in many industries, broadly, AI is generating significant cost savings and helping businesses grow faster. I can tell you that at Jennison, making and synthesizing our financial modeling work would have taken weeks and months in the past. That now takes hours and days. AI is a transformational computing cycle with an opportunity set well beyond the technology sector. We are still in the early stages of a multiyear adoption cycle. Commentary across a broad range of earnings conference calls suggests that only a small percentage of companies have identified concrete examples of AI improving current revenue growth. Most surveys indicate that only about 20% of businesses are using AI in any business function today. Norton: Let’s have some examples.Boyer: AI is being used in healthcare clinical trials, where the failure rate is nearly 90% and a huge drain on resources. Analyzing historical data allows companies to fail early and cheaply in the lab, rather than late and expensively in Phase III human trials. Pharmaceutical companies can drastically optimize R&D spending by eliminating placebo groups and reviving failed compounds. Eli Lilly has a $1 billion five-year joint venture with Nvidia NVDA to accelerate drug discovery and production through AI and robot-operated laboratory operations. Walmart WMT is using AI and machine learning to improve purchasing, optimize what it has in stores, and optimize its third-party marketplace (a relatively new business). That flywheel is also creating an advertising stream that didn’t exist a year or two ago.Norton: How are you navigating this heavily concentrated market, with your own heavily concentrated portfolio?Boyer: We’ve been through an extraordinary 12-to-15-year period in which companies that were already at scale saw their growth accelerate. Twenty-five years ago, we probably owned 60-70 names. Today, we own 50-60, reflecting these larger companies. We make sure there’s diversification outside the top 10. Companies that are earlier in their growth trajectory make up 15%-25% of the portfolio. They have a high degree of organic growth as a backdrop to the revenue growth.Norton: What are some next-generation names?Boyer: Snowflake is an important player in data warehousing and data access. Crowdstrike CRWD sits in the internet security space. The proliferation of data and of AI makes security ever more important to C-suite executives. For infrastructure software, AI deployment increases the need for observability, orchestration, governance, security, and control across increasingly complex systems. The market decline [let us] take advantage of price pressures. We didn’t waver about the longer-term outlook for those businesses.Norton: Let’s discuss Microsoft and Apple, which have faced different headwinds. They are in your top 10.Boyer: Microsoft is uniquely positioned at the center of enterprise AI adoption. It can monetize AI through both subscription and consumption-based models. Continued Azure acceleration, increasing Copilot uptake, and ongoing cloud migration and digital transformation trends support our high conviction. Microsoft is expected to deliver roughly 20% annual revenue growth—remarkable, given its size—while maintaining strong profitability and cash flow generation. The stock trades at approximately 20 times 2027 earnings. That’s compelling for a business with competitive advantages, an expanding AI opportunity, and a long runway for sustained growth.Apple AAPL has an unmatched installed base, a powerful brand, and the ability to monetize one of the largest and most loyal global customer ecosystems. The slowdown in its growth rate is driven by temporary headwinds including supply constraints, component inflation, and near-term services softness rather than deterioration in competitive position. Norton: What’s the thesis for Eli Lilly, one of your largest positions? Boyer: It was founded on diabetes treatment. Blockbuster tirzepatide therapies such as Mounjaro and Zepbound continue to gain share globally and drive exceptional revenue and earnings growth. Large pharmaceutical companies haven’t historically grown at high rates for a maintainable period. A year ago, they were expanding capacity, and much like with hyperscalers, [growth] wasn’t all linear. There were concerns around pricing. Some of that weakness allowed us to increase our position size. Part of what expands the market here is ever lower prices. There’s a really broad opportunity set ahead. Lilly can deliver sustained above-market earnings growth. That’s supported by durable competitive advantages in obesity and diabetes, continued international expansion, and a robust pipeline. In June, Lilly presented data for its next-gen obesity treatment showing a very clean safety and tolerability profile, making it more suitable for indefinite use than even tirzepatide.

Brendan McCann: A few themes have dominated the ETF market in 2026. Investors have piled into high-flying technology ETFs, specifically those oriented toward artificial intelligence and semiconductors. Small-cap stocks have seen a reversal in fortune and have outperformed large-cap stocks year to date. There are countless ways to gain exposure to those trends, but investors can flock to flawed ETFs or those that lack long-term merit. The three overrated ETFs outlined here are popular with investors, but leave something to be desired.3 Overrated ETFsVanguard Russell 2000 Index Fund ETF Shares VTWOIShares A.I. Innovation and Tech Active ETF BAIRoundhill Memory ETF DRAMThe first is Vanguard Russell 2000 Index Fund ETF Shares VTWO. Aptly named, it tracks the Russell 2000 Index. It’s a popular choice in the small-blend Morningstar Category, but it has drawbacks. The Russell 2000 Index quickly comes to mind when thinking of small-cap stocks. It’s often quoted in financial news, and it is a great barometer for keeping the pulse on that slice of the market, but it’s not an ideal candidate for an index fund, and there are better options out there. The ETF’s trading costs and lack of screens are the main issues. The Russell 2000 Index does not use turnover buffers for its smallest holdings. Those tend to be the hardest to trade in the most volatile stocks, which come with higher trading costs. The stocks cycle in and out of the portfolio, adding to the ETF’s transaction costs and cutting into returns. Some of the best small-blend peers incorporate liquidity screens, profitability screens, or focus on larger and more stable stocks. On the plus side, this ETF is cheaper than its cousin, iShares Russell 2000 ETF IWM. The next ETF targets AI. IShares A.I. Innovation and Tech Active ETF BAI is actively managed and focuses on the most advanced companies across the AI tech stack. It’s one of the most popular technology ETFs. Portfolio manager Tony Kim brings an extensive technology background to the strategy. However, the strategy’s narrow focus on artificial intelligence makes it less durable. The ETF holds around 40 stocks and stows about the same as the average technology fund in its top 10 holdings—roughly 50%—but the ETF is more volatile than its average peer. The team has the flexibility to invest across the globe and trade stocks that aren’t in the technology sector if they align with the AI theme. For example, the team invested in Siemens Energy ENR, which is an energy technology firm. However, the diversification benefit of those holdings is somewhat limited since companies exposed to AI can respond to the same market drivers.Moving on to an even more concentrated AI play: Roundhill Memory ETF DRAM focuses on companies and computer memory and storage tied to the buildout of AI infrastructure. The ETF has seen massive inflows since its April 2026 inception. It’s pulled in roughly $20 billion in just three months. The ETF’s popularity may have something to do with its performance; it returned 156% from its inception through June 2026. The ETF holds a hyperconcentrated portfolio with exposure to only 12 companies as of July 13, 2026. To maintain its favorable tax status, it uses swaps to gain exposure. Swaps can circumvent diversification rules the ETF would otherwise violate if it only held the stocks of its portfolio companies. Highly concentrated portfolios can skyrocket as this ETF has, but they can just as easily dive bomb. From the end of June through July 13, the portfolio fell 22%. Exposure to just a few companies leaves this ETF vulnerable. Three stocks took up roughly 75% of the portfolio as of July 13: Micron MU, Samsung 005930, and SK Hynix SKHY. Not to mention, the ETF’s April 1 launch date coincided with a temporary dip in price of those three stocks. The ETF’s concentrated portfolio makes it a poor choice for long-term investors. Sky-high performance is unlikely to persist, and the ETF’s fortunes could turn down quickly.Watch 3 Leading Active Fixed-Income ETFs for more on ETFs.

Skan AI, a startup that builds what it calls a “context graph of work” by observing how employees actually perform their jobs across enterprise software, has raised $63 million in Series C funding co-led by Cathay Innovation and Dell Technologies Capital, the company announced Wednesday.Citi Ventures, Bloomberg Beta, State Farm Ventures, and Wipro Ventures also participated in the round, which brings the seven-year-old company’s total funding to roughly $120 million. Alongside the raise, Skan is announcing the general availability of two new products — Skan AI Blueprint and Skan AI Agents — that, together with its existing Skan AI Intelligence offering, form a complete platform for discovering, modeling, and ultimately automating enterprise workflows.The announcement lands at a moment of deep frustration in enterprise AI. Companies have poured billions into generative AI pilots, but the results have been dismal: Gartner research cited by the company finds that only 8% of enterprises have AI agents in production, and 95% of early implementations will require a complete redesign. Those figures echo an MIT report last year, covered by Fortune, which found that roughly 95% of enterprise generative AI pilots were failing to deliver measurable returns.Avinash Misra, Skan’s co-founder and CEO, believes the industry has misdiagnosed the problem. The models are fine, he argues. What they lack is an accurate picture of the businesses they are being dropped into.”Everyone is obsessed with building a better driver,” Misra told VentureBeat in an exclusive interview ahead of the announcement. “We think the bigger opportunity is building a better navigation system.”Why enterprise AI agents keep failing when they rely on official process documentationThe standard playbook for grounding AI agents — feeding them process documentation, standard operating procedures, and system logs — is built on a fiction, Misra argues. The way work is documented and the way work actually happens inside a large enterprise are two different things, and the gap between them is precisely where agents fail.That gap is what sent Misra and co-founder Manish Garg down this path seven years ago, long before agents were a boardroom obsession. “Why is it so difficult for an organization, and a large enterprise especially, to understand how its own work actually gets done?” Misra said. “Why does it need to fly in McKinsey consultants for that?”The question has only grown more consequential as enterprises race to operationalize AI. Frontier models arrive at the company door brilliant but blind, with no knowledge of the exceptions, decisions, handoffs, and institutional habits that define how a claims department or a compliance team actually operates. Every company now stuffing agents with documentation and logs, Skan contends, is discovering the same uncomfortable truth: the source data was never the whole story. And a source data problem cannot be fixed downstream.Skan’s answer is to go to the source itself. The company deploys observation technology on employee desktops that continuously watches how work moves across applications — the spreadsheet, the CRM, the email client, the 40-year-old mainframe — and abstracts those observations into a living model of the underlying business process.”Think of it this way: if I were to share my screen here, and you were to observe my screen going from Excel sheet, CRM system, email client, in about two iterations you’d build a model of what I do,” Misra said. “Except you couldn’t do that at scale. You couldn’t do it 24/7, and for 1,500 people like me. Now replace yourself with our technology.”How screen-level observation captures the work that never shows up in system logsThat framing also explains how Skan positions itself against process mining vendors like Celonis, which reconstruct workflows from the data trails left in backend systems. System logs, Misra argues, only capture completed transactions — not the messy human work that produced them.”All backend data, by definition, is a committed state of work. Work is really what happens between those committed states,” he said. “Eighty percent of what you’re interested in, from an AI point of view, in execution of work, actually lies between those systems.”The screen, in Skan’s view, is the one place where everything converges. “It brings together human agency, it brings together the entire application landscape, and it brings together the data that matters,” Misra said. Two decades of user interface design have quietly buried enormous amounts of process knowledge in the space between a worker’s eyes and their monitor; Skan’s pitch is to bring that hidden layer back to the surface.But watching, he insists, was never the hard part — a point aimed squarely at the incumbents who might be tempted to copy the approach. “The hard problem is not screen observation,” Misra said. “The hard problem is abstraction of what you see on the screen — the intent extraction.” A human watching a colleague’s screen can instantly tell whether a jump back to step one means a new case or rework on an old one, because humans understand the signature of the work. Teaching a model to make that same judgment, statefully and at enterprise scale, is where Skan believes its seven-year head start lives.The result is a context model that AI can reason over and act on — the raw material for the agents that now sit at the top of the company’s product stack, and the foundation for everything else the platform does.Walking the line between operational telemetry and workplace surveillanceAn approach built on continuously watching employee screens invites an obvious objection, and it is not a hypothetical one. In June, Reuters reported that Meta scaled back an internal tool that tracked employee mouse clicks after workers raised concerns — a sign that even AI-forward companies are wary of the line between operational telemetry and surveillance.Misra says he heard the objection before he wrote a line of code. When he first pitched the concept to Delphine Icart, then chief transformation officer at AXA Mexico, her reaction was blunt. “Delphine’s first words to me were, ‘This sounds like a great idea, but you are dead on arrival,'” Misra recalled. “‘You are observing things that you shouldn’t be observing — the privacy of my operators, and the sovereignty of my data on those screens.'”That conversation, he says, shaped the architecture. Skan aggregates rather than individuates: the system surfaces statistical patterns across hundreds of workers performing the same process, not the behavior of any one of them. “We’re not interested in what John is doing at 10 hours and 43 seconds,” Misra said. “We are interested in what hundreds of Johns put together — what are the statistical and the semantic decisions that they are making in that business process?”Organizations control what the technology can see through an opt-in scoping model — specific applications and URLs, nothing else — and the data Skan produces never leaves the enterprise firewall. A three-tier architecture sends only anonymized metadata to the cloud. Misra points to deployments approved by European works councils, among the most privacy-protective labor bodies in the world, as evidence the model holds up under scrutiny — and credits it for clearing security review at institutions where most AI tools cannot operate.Whether aggregation fully defuses the concern is likely to remain contested. The same telemetry that reveals a broken process can, in principle, reveal an underperforming team, and Misra acknowledged that the technology has led some customers to reduce headcount in certain processes.What $500 million in claimed customer value actually measuresSkan claims more than $500 million in cumulative customer value to date, a figure worth unpacking. Pressed on whether that represents realized savings or projections, Misra was direct that it is an envelope, not a bank balance.”The number comes from the cumulative, across all our customers, of the quantified savings that we have brought to them — the savings that they have expected they would save,” he said. “Now they are on the roadmap of recouping those savings through a variety of interventions,” including process redesign, technology changes, and, increasingly, AI agents. In other words, $500 million is identified opportunity, some portion of which has been captured.The more concrete evidence comes from individual deployments. At one top U.S. bank, according to the company, Skan observed 11.2 million context switches across 1,500 finance professionals and uncovered $37 million in operational friction. Turning those observations into agent-executable context cut cost per transaction by 32%, lifted throughput by 41%, and delivered $18 million in annualized savings.Misra pointed to an anti-money-laundering operation at one bank where “60% of the cases are now being run by AI agents,” adding that the results surprised even him: “The accuracy of those agents surpasses many times over the accuracy of humans. It’s not just an argument of efficiency; it has also become an argument of quality.” Among insurers, he said, Skan typically delivers roughly 25% productivity uplift in core claims processes; one customer doubled its case volume over the past year without adding a single claims specialist.Skan’s publicly referenceable customers include Unum, the $13.8 billion employee benefits provider, and Mitie, the U.K. facilities management company, whose chief technology and digital officer, Cijo Joseph, said Skan’s technology “gives us unprecedented operational visibility that has dramatically accelerated our AI transformation.” The company declined to share revenue but said it grew more than 300% year over year — for the second consecutive year — with net dollar retention around 150%, and now counts seven of the ten largest U.S. banks and a quarter of the Fortune 50 as customers.Can AI models learn good work from imperfect employees?Skan’s thesis rests on observing how work actually gets done — which raises an uncomfortable question. Real employees make mistakes, take shortcuts, and entrench inefficiencies. What happens when the context graph faithfully encodes bad process?Misra’s answer reaches for the most famous precedent in modern AI. “Think for a moment what OpenAI did,” he said. “OpenAI took the totality of the world’s text and fed it into a transformer architecture, and semantic understanding emerged. OpenAI’s model has seen bad language and has seen good language, and yet it is able to have semantic understanding.”Skan, he argues, does the analogous thing with work: treat business process execution as a language, where process steps, screen features, and handoffs stand in for words and sentences. Fed enough end-to-end executions, the model learns the full distribution of paths — efficient ones, slow ones, compliant ones — without assuming any single path is best. “The longest path may be the best path, because it is more compliant,” Misra said. An organization then constrains the model along the axes it cares about, and the model returns the path that satisfies them.”It is not record and play — and that’s the fundamental difference between us and a lot of our competition, UiPath and so on,” he said. “It is fundamentally creating an AI model that understands work, and then constraining that model.”He offered a concrete illustration of what that unlocks: at one large bank, Skan’s telemetry continuously compares live case execution against a 600-page controls inventory, with agents that trigger alerts when cases miss required compliance steps — turning a document no human could hold in their head into a real-time enforcement layer. It is the kind of application that only becomes possible, Misra argues, once a model genuinely understands the work rather than merely replaying it.The race to own the context layer of enterprise AISkan sits at the intersection of several crowded categories, and its answer to each competitor is a variation on the same theme: scope. Process mining vendors see only what the logs record. RPA incumbents replay tasks without understanding them. And the platform giants — ServiceNow, Salesforce, Microsoft — are shipping capable agents whose vision ends at their own walls.”The context that these agents have access to is limited to ServiceNow, limited to Salesforce, whereas work spans processes across the board,” Misra said. “Creating a customer entry is a task. To receive an email and decide whether a customer entry has to be created, or something else — that is the process, and that’s what we are after.”The deeper strategic argument, and the one that seems to resonate with Skan’s regulated customer base, is about differentiation in a world where every enterprise has access to the same frontier models. “If every insurance company, every bank had access to the same models, then the outcomes will asymptotically decay to the outcome of the model,” Misra said. “Historically, you have competed and differentiated in the way you have organized work. That old word — process — now comes back as context for AI. But that context is protected by you. It’s not part of the model.”That logic explains both the company’s posture toward the model makers — “the more they are successful, the more power we have,” Misra said, disclaiming any ambition to compete with them — and the Nvidia partnership featured prominently in the announcement. Skan runs on Nvidia AI Enterprise and NIM microservices, and Misra described growing demand for private appliances that can observe work, hold the context model, and execute agents entirely inside a customer’s own infrastructure. It also fits the market’s direction: venture investors surveyed by TechCrunch at the end of last year predicted enterprises would spend more on AI in 2026 but through fewer vendors — a consolidation that favors Skan’s decision to ship discovery, intelligence, and agents as a single closed loop.Misra argues that loop matters more, not less, as automation scales, because agents demand oversight in a way humans never did. “It is an irony of sorts,” he said, “that you’ll probably need much more observation and much more understanding of work in an automated way than you would with humans.” The bet embedded in this round is that work context becomes foundational infrastructure for enterprise AI the way CRM became the system of record for customers — a comparison Cathay Innovation partner Simon Wu made explicitly, calling Skan “one of the defining platform companies of the next decade.”Misra put the stakes more simply. “You cannot retrieve context that you do not capture,” he said. “The battleground is shifting from the smartest model to knowing how your company actually works — because everyone will have access to the smartest model.”The frontier labs, in other words, can keep their arms race for the better driver. Skan just raised $63 million on the conviction that the money is in the map.

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