📖 Venture Chronicles - September 2025
Overlooked #205
Hi, it’s Alex from 20VC. I’m investing in seed & series A European vertical solutions (vSol) which are industry specific solutions aiming to become industry OS and combining dynamics from SaaS, marketplaces and fintechs. Overlooked is a weekly newsletter about venture capital and vSol. Today, I’m sharing the most insightful tech news of September.
I curated updates and insights around three themes:
Vertical Software
General Venture Capital
Entropy - other news and personal topics of interest
Vertical Software
Mark Leonard organised a special call with shareholders to discuss the AI impact on Constellation Software which is a publicly listed rollup of vertical software companies. - CSU
“Jeff Hinton wasn’t wrong about the applicability of AI to radiology [forecasting in 2016 that radiologists will be replaced by AI]. Where he was wrong was that the technology would replace people, instead it’s augmented people. The quality of care delivered by radiologists has improved and the number of practicing radiologists has increased.”
“It’s really easy to get excited about 10x improvements in programmer productivity as you generate that new application. But if that new application goes out into the field and generates scads of bugs reported by clients and is fundamentally difficult to change and improve. You may give up on the roundabouts what you made on the swings. You may end up with higher lifetime cost of the code base. And similarly, you have to take into account the efficiency of the code that the AI produces. And I think we’re very early days. We know there are some wonderful advances in programmer efficiency on the front end, and we just don’t know the answers on the back end yet because we haven’t lived with it for long enough.”
“Make sure that you really understand the problem that you’re aiming to solve with AI. We’re seeing so many AI solutions, which are actually solutions in search of a problem. So it’s starting with the technology rather than starting with the customer journey or with the customer problem that we’re trying to achieve.”
“I see vertical market software sitting somewhere between horizontal applications that are cheap and cheerful and do 50% of what you want and highly customized systems that do exactly what you want.”
“We capture the small companies as they graduate from horizontals. We take them and some of them grow enormously and become very successful large companies. And then they graduate to no longer using our systems but to using a much more proprietary system that they have a much stronger hand in driving. Now AI has the potential to allow us to do way more work on making the client happy and customizing our solutions but it also allows the client to potentially do that and so there’s a natural tension there.”
“AI introduces a new COGS element, not historically present in software. Does this change the economics of the business model? I think specifically, what we’re seeing here is the adoption of AI is being massively subsidized by the AI model companies. At some stage, they will want to recapture that investment. And are they going to be in a position where we have -- we face very large switching costs, and they are going to be able to capture a large chunk of our value-added through the -- what they charge us for their systems, whether it be on a per token or per whatever basis.”
“I think we can easily cover the additional AI COGS and maintain our margins by having premium add-on where our end customers, if they want to leverage AIfeatures, they can buy those premium add-ons.”
“The good news is that currently, these large language models don’t have a very large moat around them. There will be high competition between model providers which will keep the pressure on the cost.”
There are 4 key pillars for value creation in AI rollups: (i) multiple expansion, (ii) margin expansion, (iii) top line expansion and (iv) upsell new products. - Vinay Iyengar
“I try to invest in spaces where at least doubling EBITDA margins is possible.”
“AI rollup/services world will fundamentally change how software is sold and delivered. Historically, companies sold software licenses and upsold services. Salesforce is the canonical example of this. However, I believe we are going to see more and more companies start off by selling a service and then upselling higher-margin software and products, especially as customers “graduate” from their service and want to in-house that function. As an example, an AI rollup in the accounting space can start off by selling basic accounting services, but over time, can either sell their “agent” to a business directly or offer new products that give real-time visibility into inventory, financial metrics, etc. AI rollups are essentially a way to work deeply with a single design partner to build the perfect software for a given vertical.”
Forbes wrote a portrait on Flock Safety. It’s a computer vision based operating system for cities to deter crimes. It raised $850m in funding including a $275m series H in March 2025 at a $7.5bn valuation. - Forbes
Flock generated $300m in revenues in 2024, has deployed 80k cameras in the US and solves 1m crimes per year.
“Growth has been explosive, with revenue up some 70% from the estimated $175 million it booked in 2023.”
“Each Flock license plate reader cam costs between $3,000 and $3,500, with an additional fee for FlockOS, the operating system that makes all the data Flock collects accessible via a browser or a mobile app, based on either the number of users or cameras.”
“Flock’s growth isn’t solely fueled by its 5,000 law enforcement customers across 49 states (it hasn’t yet installed its cameras in Alaska). It has 1,000 corporate customers, including blue chips like FedEx, Lowe’s and Simon Property, America’s largest mall owner. Then there are housing and homeowner associations, small businesses, schools and organizations like the Jewish Federation of Greater Atlanta.”
“Flock had a burgeoning partnership with the publicly traded ($59 billion market cap) Taser creator after Axon made a minority investment back in 2020. The market incumbent, founded in 1993, had promised to promote Flock license plate readers and make them work seamlessly with Axon’s tech. But in January, Axon CEO and billionaire cofounder Rick Smith killed their deal, accusing Flock of overcharging and trying to lock customers into its products. In April, Axon debuted its own stand-alone license plate reader cameras along with a shot-across-the-bow first customer: the Atlanta Police Department, a current Flock user. Axon is charging 20% less for its cameras, and early adopters get the first year of their software free.”
“The company is chugging along with new products in the pipeline. It’s adding car crash detection to its gunshot flagging system, Raven, and enhancements to its license plate camera feeds. Further out, there’s Nova. Nova promises to knit law enforcement records with all manner of public information—property and occupancy data, Social Security numbers and personal credit bureau histories—and make it all granularly searchable with AI.”
Axon acquired Prepared which is an AI-powered 911 communications company. - Axon
Prepared turns call audio, text, video, GPS and live translation into one view for more than 1,000 agencies in 49 states.
“The company’s flagship platform is an end-to-end assistive AI toolbox that consolidates critical functionality onto a single screen, empowering PSAPs to harness the power of fully integrated AI. Since its founding in 2019, the company now partners with over 1,000 agencies in 49 states that protect nearly 100 million people.”
Cadence announced an agreement to acquire Hexagon’s design and engineering division for €2.7bn. Alongside Synopsys, Cadence is a leading player in the Electronic Design Automation (EDA) market, which provides design software for the semiconductor industry. - Reuters, Hexagon
Hexagon’s Design Systems division includes MSC Software which is a key provider of computer-aided engineering solution for mission-critical simulation and analysis. It made €265m in revenues in 2025. It has a strong presence in automotive and aerospace with blue-chip customers like Volkswagen, BMW, and Lockheed Martin.
Luminary Cloud raised a $72m series B led by N47 with the participation of Sutter Hill and Nvidia. It makes manufacturing design simulations that normally take hours or days available in seconds. It operates in sectors like automotive, aerospace, and electronics. It plays in the Computer Aided Engineering (CAE) market competing with incumbents like Ansys, Dassault, Siemens or Altair. - Luminary Cloud
HappyRobot raised a $44m series B at a $500m valuation led by Base10. It previously raised a $15.6m series A led by a16z. HappyRobot uses voice agents to automate communication tasks in the logistics industry (e.g. managing inventory, doing sales calls, handling services requests). - Upstart
“HappyRobot is building a digital workforce for the supply chain and its operators, utilizing AI to automate otherwise repetitive and high-volume communications, and eventually entire workflows from managing inventory to fielding sales calls, and handling basic service requests.”
“Since launching its voice agent at the beginning of last year, HappyRobot now works with some of the industry’s most important companies, including DHL Supply Chain, trucking company Werner Enterprises, and transport business Schneider. Eight of the top 10 brokers and two of the top three ocean carriers use HappyRobot, Palafox says. Revenue has increased more than 10x since HappyRobot announced its $15.6 million Series A led by a16z in December and is “well into” the eight figures.”
Nory raised a $37m Series B led by Kinnevik with participation from Accel, Base10, Triple Point, and Samaipata. It will use the funding to double down on integrating AI in the platform and expand to the US. Nory describes itself as an AI-native restaurant management platform unifying business intelligence, inventory, workforce, payroll, and financial management for restaurants to automate back office operations. It can reduce operating costs by nearly 20%, increase net profits by up to 50% and save 100+ admin hours per restaurant per month. Since the Series A, Nory has tripled revenues and doubled its team. - Nory
Teton raised a $20m series A led by Plural with the participation of Bertelsmann, Antler, Nebular and PSV Tech. It’s a vertical SaaS in healthcare using hardware for patient monitoring in hospitals and leveraging the data collected to help nurses in their monitoring (e.g. preventing patient falls) and adjacent workflows (e.g. reducing admin paperwork). The company is in advanced U.S. pilots and expects to be live in 10 states by year-end. It will use the funding to launch in the US and double down on its European business. - Mobihealthnews
“Teton.ai generates digital twins that examine and recognize what is happening with residents and staff in the hospital setting to gain an understanding of health and behavior.”
“In May, Teton.ai created a real-time, live 3D reconstruction of a hospital room using Gefion, Denmark’s national supercomputer. The aim was to help nurses recognize risks early, reduce paperwork and improve patient care.”
General Venture
Jeff Horing who is the co-founder and Managing Director of Insight Partners recorded a podcast episode on Invest Like the Best. - Invest Like the Best
“Two numbers really matter.
Gross Dollar Retention (GDR): how sticky is your customer base, and more importantly, how much of that bucket do you have to fill every year? There are very few companies with low 80% GDR that are in the top 20 market cap businesses.”
Net new bookings. That second derivative is really powerful where you see a lot of companies with almost zero change in the new business that they add each year off a small number that could still look a really big growth rate, sometimes as much as 100% or 75%. But you can extrapolate that number if it’s flat. Wiz has been able to double or more their net new bookings each year for six years.”
“There are five ingredients to perfect software investments: big ROI, big ASP, short time to value, amazing CEO, and amazing technical team.”
For enterprise companies, “15 customers that are referenceable is sort of a magic number for inflecting on growth. The challenge with those companies is they’re very hard to sell those products. It’s a lot of missionary selling early on. You can’t really scale your sales organization until you have a certain number of referenceable customers that you could lean on.”
“The best bet on the table is the double down bet. If you went back in time and looked at our double down checks, they were our best checks.”
Gil Dibner at Angular wrote about his investment strategy in a venture industry that has become super consensual trying to find a good balance between consensus and contrarian bets. - Angular
“There appears to be only one game in town right now and one way to play it: find a team of legible, pedigreed founders to build the next big AI application and focus on raising a big follow-on round almost immediately.”
“When the entire market agrees on nearly everything, it’s difficult, frustrating, and painful to be contrarian.”
“It would be imprudent for any investor to build a portfolio comprised solely of one type of risk (either consensus or contrarian).”
“Historically, we’ve done our best and most rewarding work when we’ve invested in pre-traction and illegible companies. That said, we have invested in known-good (sometimes repeat) founder teams in the past. We’ve also increasingly focused on some traction at pre-seed, especially when a company is doing something exceptionally weird. The weirder a company is, the less likely it is to easily raise follow-on financing. In such cases, we believe that some early traction is both a signal of potential future follow-on financing and a cash-flow insurance policy in case that follow-on capital takes too much time to arrive. We’re trying to strike a balance. We think it would be foolish to slavishly avoid any investment that stumbles too close to consensus and starts to attract heat. At the same time, we wouldn’t be venture capitalists if we didn’t maniacally pursue the hunt for “true alpha” in its purest form.”
Leslie Feinzaig wrote about the bifurcation in the venture industry between early stage funds doing contrarian bets and mega-platforms that are deploying as much capital as possible into consensus companies. - Leslie Feinzaig
“Since its earliest iterations in the 1940s, Venture Capital has always meant investing in early stage companies with the potential to generate alpha - high-risk, high-reward, uncorrelated with efficient (public) markets. It’s never been about investing in the obvious.”
“Large funds are not picking contrarian bets. They’re picking consensus ones.”
“From what I see today, there’s four defining factors for consensus capital:
The focus on giant outcomes only - forget unicorns, their hunt is for trillion dollar outcomes.
The belief that only one type of founder can achieve such an enormous outcome - the “consensus” founder, if you will.
Complete price-insensitivity, or a willingness to pay up at the entry point for that one type of founder.
Funds so large that they can deploy huge amounts - tens or hundreds of millions - in a single early stage round.”
“There are great consensus bets to be made, capital itself becomes the moat for some of these companies, exits may be (are presumed to be) sooner than true venture exits, and you’ll make money off the beta, highly correlated with the growth of the entire category”
“When we talk about these giant funds as if they represent venture capital, we flatten the story of our ecosystem, and mislead non-consensus founders in the process. Consensus capital goes to founders that have a very particular, very predictable pedigree.”
“Everyone predicting the death of venture capital is wrong. Plain and simple. As long as there are non-consensus founders with a real shot at the American Dream, investors willing to partner with them, and exit opportunities awaiting on the other side, VC will be alive and well.”
David Clark, CIO at Vencap, shared the last 12 months performance of the 119 venture funds they backed between 2010 and 2020. It illustrates the power law of the VC industry with 10 funds accounting for 80% of the total return during this timeframe. - David Clark
“Overall, these funds are doing well – 3.1x TVPI and 20% IRR. Just three are below 1x.”
“50 of the 119 funds (42%) recorded negative performance over the last 12 months. The median fund return was just 2.9%.”
“There were four funds that more than doubled in value over the last 12 months, with the best-performing fund showing a 280% return. Overall, the top 10 performing funds on a $ basis accounted for nearly 80% of the total return. Across these 10 funds, just eight companies drove the vast majority of gains: (i) Wiz, (ii) OpenAI, (iii) RocketLab, (iv) Figma, (v) Robinhood, (vi) Revolut, (vii) Xiaohongshu and (viii) Circle.”
America’s AI boom is unprecedented, with nearly $400bn spent this year and projected above $3trn by 2028. - The Economist
“This year America’s large tech firms will spend nearly $400bn on the infrastructure needed to run AI models.”
“The scale of these bets is so vast that it is worth asking what will happen at payback time. Even if the technology succeeds, plenty of people will lose their shirts. And if it doesn’t, the economic and financial pain will be swift and severe.”
“In the rosiest scenario AGI will arrive and usher in a new world of economic growth of perhaps 20% a year, as we wrote in July. Some shareholders would enjoy astronomical returns; many others would face big losses. More mundane scenarios should also be considered, however. The technology may evolve in ways that investors do not expect.”
“Although the shells of data centres and new power capacity could find other uses, more than half the CAPEX splurge has been on servers and specialised chips that become obsolete in a few years.”
“The more the investment boom spreads, the more financing structures could get riskier, and the more indebted firms could be drawn in. Power companies are desperate to increase their investments to supply AI with the electricity it needs; a heavily indebted utility could easily become overextended.”
“The bigger the boom gets, the bigger the knock-on consequences of an AI chill could be.”
Platforms follow the same playbook: open, grow, close, then monetize, often crushing the apps that helped them rise. ChatGPT looks like the next major distribution platform that will reshape how products find users. - Brian Balfour
“AI is actively destroying the distribution channels we’ve relied on for decades. SEO traffic is plummeting as users shift to answer engine platforms. Social algorithms are more unpredictable than ever. The old playbooks are burning.”
“If anything, AI is destroying distribution channels. SEO is collapsing as answer engines like Perplexity, ChatGPT Search, and others keep users in its experience rather than sending traffic out. Social platforms are becoming walled gardens, limiting traffic being sent elsewhere. X has banned links, LinkedIn punishes posts that link externally, the list goes on.”
“The companies that thrive through platform shifts aren’t necessarily the biggest or best-funded. They’re the ones who understand the game being played. They build with the end in mind. They extract value during Step 2 [open the gates to build the moat with an open ecosystem] while preparing for Step 3 [close the gates for monetisation].”
“Facebook’s platform shift established the modern template: (i) Use developers to build what you can’t build alone, (ii) Let them take the risks and validate use cases, (iii) Once you’ve won, take back control and monetize aggressively.”
“Users still have relatively low switching costs. Copy your prompt, paste it elsewhere, get similar results. There’s no lock-in yet. No data gravity. No compelling reason to stay with one provider.”
“OpenAI has identified their moat, and it’s not the model quality. It’s context and memory.”
“OpenAI needs something far more intimate: your entire digital context. Every document, every conversation, every preference, every workflow.”
“If I’m right, in six months, it’s likely every SaaS product and consumer application will be rushing to complete a ChatGPT integration. In twelve months, users will expect it. In eighteen months, the platform taxes will arrive. In twenty-four months, the graveyard will be full.”
Avenir shared the bull case to invest in consumer AI. - Avenir
“Over the past year, the foundational capabilities required for 10x better consumer AI products (real-time voice, reasoning models, multi-modal, memory, and agents) have become widely accessible. This convergence of capabilities and cost has resulted in mass adoption—ChatGPT is the fastest scaling consumer application in history—but we’re still in the early days of mass consumer AI adoption.”
“We believe those who create a 10x better, full-stack product and compound moats that incumbents and generalized assistants can’t replicate will create the next wave of consumer winners who will unlock superpowers for billions of people.”
Yoni Rechtman at Slow wrote about the evolution of margins in software companies. - Yoni Rechtman
“The notion of software as a good business with high (gross) margins reflects three assumptions of its operating model: (i) zero marginal cost of reproduction (same code, infinite users), (ii) non-ephemeral value (software doesn’t depreciate like media so you can keep selling across time), (iii) high switching costs (mission critical or highly differentiated products drive retention and pricing power).”
“AI changes the equation/exposes flaws in the accepted wisdom: (i) marginal costs of reproduction gets worse (still code but now with inference behind it), (ii) switching costs collapse (less differentiated and easier to swap), (iii) depreciation accelerates (new stuff gets better faster). The result: Switching costs decline precipitously and software likely re-equilibrates from 80-90% margins to something much lower. Whether that’s 10% or 60%… who knows.”
“If the typical drivers of switching costs -> pricing power -> margins deteriorate, you have to find them elsewhere: (i) go deeper vertically, (ii) build for network effects and (iii) role hybrid models combining software with services or hardware.”
AI is making consumer apps grow much faster. Usage-based pricing and users bringing tools into work push revenue retention above 100%, so cohorts expand instead of churn as it was the case in previous generation of consumer apps. AI companies are implementing three different strategies to expand their revenue retention above 100%: (i) investing in an enterprise sales motion from day one, (ii) reducing the barrier to expand the use of the product into its workplace and (iii) sophisticated pricing with price segmentation & usage based billing. - a16z
“For consumer subscription products, cohorts with 30-40% user and revenue retention at the end of year 1 were considered “best in class”.”
“The fastest growing consumer AI companies are now seeing revenue retention above 100%. The fastest growing consumer AI companies are now seeing revenue retention above 100%.”
“At 50% revenue retention, a company must replace half its base every year just to stay even. At more than 100%, every cohort is expanding – growth compounding on top of growth.”
“The transition from price-sensitive consumers to price-insensitive enterprise buyers creates major expansion opportunities.”
“This may feel counterintuitive, but consumer companies should now think about hiring a Head of Sales within their first one to two years. Grassroots adoption can only take a product so far; securing broad organizational use requires navigating enterprise procurement and closing high-value contracts.”
ElevenLabs reached $200m in ARR and raised $100m in secondary at a $6.6bn valuation from Iconiq and Sequoia in an employee tender. Offering liquidity to employees while being a private company is becoming a must and it’s noteworthy that top European AI companies are also starting to offer this benefit to their employees. ElevenLabs is also a perfect example of a prosumer AI product that is successful expanding into the enterprise segment. - Mati Staniszewski
The tender is available to employees who have been at the company for at least a year. ElevenLabs’ valuation doubled from its series C raised 9 months ago. The company is also forecasting to reach $300m in ARR by year end with enterprise revenue that has grown over 200% in the past year approaching a 50-50 split with self serve revenue.
Entropy
Oura raised a $875m series E at a $11bn valuation. It sold 2.5m devices in 2024 and expects to generate $1bn in sales in 2025. - The New York Times, Techcrunch
“The latest-generation of rings starts at $350, and the app to gauge sleep, activity and “readiness” is another $70 per year.”
“The business model is to propose that people become the C.E.O.s of their own health journeys. One of the ways is to give their body a voice by putting on a wearable that gives them information and insights about their health that they might not otherwise get. We do that through a combination of a ring wearable, a very elegant software application and A.I. in the background.”
“If you measure from the finger, you’re getting a more accurate signal compared with a wrist.”
“The company is expected to generate more than $1 billion in revenue this year, doubling the $500 million it made in 2024. As for 2026, the company forecasts sales to exceed $1.5 billion” - Stratecherry
Meta released new smart glasses called Meta Ray-Ban Display at a $799 price point with a control system based on hands gestures and with a built-in screen. It’s a step forward in Meta’s ambition to challenge Google’s Android and Apple’s iOS as ubiquitous mobile platforms.
“When you are wearing the Ray-Ban Displays you can see reality, and that vision of reality can be augmented with little bits of information and UI.”
“A low price is, needless to say, critical for building that product beachhead that gives you the opportunity to build an ecosystem. $799 is certainly not cheap, but it’s accessible, and the fact that it gives you the input controller of the future is very impressive.”
“The best way to bend the cost curve on devices like this is to actually ship devices; the best way to improve software quality is to actually have it used for critical functionality; the best way to figure out killer use cases is to actually let people try things in the real world.”
Snap is restructuring its team to create five to seven small squads of 10-15 people working on big bets to re-accelerate usage and revenue growth. - Snap, Techcrunch
“Snap currently occupies a unique position, with significantly more scale and engagement than smaller players, but with less scale and market power than our larger competitors. Squeezed between the tech giants and smaller competitors, on the verge of greatness, we find ourselves in a crucible moment.”
“In an effort to sharpen our focus and increase clarity, we established working groups across the “key issues” we are facing as a company.”
“At the enterprise level, our strategy is moving from experimentation with generative AI tools to scaled, outcome-driven adoption that transforms the way we work. While team use of tools like ChatGPT and Gemini has taken off and we’ve seen early wins with workflow-specific pilots such as code assist, the true opportunity lies in embedding automation, agentic workflows, and AI-driven augmentation into the core of our business. Our immediate priority is to double down on four key functions—Engineering, Sales, Trust & Safety, and Customer Support—where AI is already delivering measurable impact.”
“We’re going to try a new way of organizing around a handful of our big, new bets. Five to seven teams—squads of 10 to 15 people—will run like startups inside Snap, with single-threaded leaders accountable for outcomes. Weekly demo days, 90-day mission cycles, and a culture of fast failure will keep us moving. Our platform teams will be enablers, handing squads the toolkits and guardrails to ship safely at speed.”
“Snapchat+ started as a small, cross-functional squad that moved with urgency and launched fast. In under three years, it scaled from zero to ~15 million paying subscribers and nearly $700M in ARR, now driving more than half of our incremental revenue growth.”
Elon Musk’s SpaceX is buying $17bn in wireless spectrum from EchoStar to expand Starlink into mobile connectivity. - WSJ
“Starlink’s entry into the sector is likely to put new pressure on major U.S. mobile carriers to deliver better service to rural customers, analysts say, whether through a tie-up with Starlink or a competing technology.”
“Starlink says its upcoming cellphone service will provide “full 5G cellular connectivity with a comparable experience to current terrestrial LTE service” in “most environments”.”
“Starlink is unlikely to be able to go head-to-head with Verizon or AT&T in crowded urban areas anytime soon, because of limited spectrum, and will probably remain an add-on to traditional mobile plans.”
“Starlink had begun testing a text-messaging service last year through a partnership with T-Mobile, using spectrum owned by the mobile carrier. The service launched this year as “T-Satellite” for $10 a month. Starlink now has the bandwidth to upgrade from texting alone. The company’s website says cellular data service will begin this year and voice calls will begin “soon.””
“Starlink has more than 8,000 satellites in orbit, according to astronomer Jonathan McDowell, making it far and away the largest satellite system orbiting Earth. It has steadily added users, reaching an estimated 8.5 million subscribers compared with 4.7 million last year.”
“SpaceX is on track to generate about $15.9 billion in revenue this year, about 70 percent of which is expected to come from Starlink.”
George Mack wrote a post on characteristics to identify high agency people. - George Mack
“Weird teenage hobbies. Teenage years are the hardest time to go against social pressures. If they can go against the crowd as a teenager, they can go against the crowd as an adult.”
“Quit something of prestige. The miserable management consultant who breaks free from their golden handcuffs to become a stand-up comedian has to overcome momentum, social shame and sunk cost fallacy. The high agency person lives many lives and isn’t afraid to reinvent themselves — regardless of the perceived social cost.”
“Self-taught learning machines. Whether it’s learning to play their favourite song on the Saxophone or deconstructing how 3D printers work — they start from zero and use agency to climb up the knowledge ladder. Tesla, Da Vinci and Darwin didn’t ask for permission from institutions to just do things.”
“They pet the elephant in the room. If they sense an elephant in the room, they don’t avoid it. They talk to the elephant, feed it, and ask it why it’s there. They know the elephant in the room gets smaller every time they interact with it. In five minutes, they help shrink an enormous elephant you’ve been avoiding for five years into a cute baby elephant calf you can control.”
The hiring market is frozen: payroll growth has stalled, hiring rates have dropped, and job searches now average 10 weeks. Online platforms make it easy to apply but hard to get noticed, as AI filters screen résumés while applicants increasingly rely on AI to write them. - The Atlantic
“At the same time, the process of getting a job has become a late-capitalist nightmare. Online hiring platforms have made it easier to find an opening but harder to secure one: Applicants send out thousands of AI-crafted résumés, and businesses use AI to sift through them. What Bumble and Hinge did to the dating market, contemporary human-resources practices have done to the job market. People are swiping like crazy and getting nothing back.”
“In a recent survey, chief HR officers told the Boston Consulting Group that they are using AI to write job descriptions, assess candidates, schedule introductory meetings, and evaluate applications. In some cases, firms are using chatbots to interview candidates, too. Prospective hires log in to a Zoom-like system and field questions from an avatar. Their performance is taped, and an algorithm searches for keywords and evaluates their tone.”
I read a post on British retirement villages in which elderly residents tend to be exploited. - Unherd
“As the manager says, there’s a 12-18 month waiting list for rentals, while sales can take up to a year.”
““Regardless of if you own it or rent it, it always comes back to us,” says the manager of a care home’s business model, explaining that for every year someone lives in the property, 1% of the original purchase price is taken off what the company will eventually buy it back from you (or your estate) for. If, for example, you buy a flat for £100,000, then live in it for a decade, you’d see a return of just £90,000. That errant 10% is chalked up to maintenance and bills — and excludes a range of other fees — with the money passed back to your next of kin if you die.” “The buy-back price excludes any allowance for inflation.”
“Over their six-year study, the researchers reported a 38% reduction in NHS costs, a 64% drop in depression, a 75% increase in physical activity, with 86.5% of residents reporting they “never or hardly ever” felt lonely.”
“John argues that the place was marketed as a “thriving, active community”. Now, though, “it’s a miracle if you can get a couple coming in and one of them can walk”.”
“Some residents [are] beginning to sense that places like this aren’t built to care. They’re built, rather, to farm, with elderly residents harvested for their savings and extracted for maximum yield.”
“Despite the threshold being 55, he says “people moving in are more like 82-83”. But he pours cold water on the idea that there’s a conspiracy afoot. Rather, he blames the disparity of wealth between generations. Emerging as the wealthiest age group in the UK, Baby Boomers are simply more able to afford life in a village compared to their younger peers.”
“The UK is facing a shortfall of over 487,000 homes for pensioners, with The Times reporting in 2022 that more than 3,000 retirement villages will be needed to meet demand.”
“As a society, we turn away from those in their last years, glamorise them in glib novels, perhaps because the truth about ageing is so hard to stomach. Looking an old person in the eye, as they lie on the floor, unable to stand, begging to be held, is too uncomfortable. So those who can afford it outsource the problem, outsource the care, and outsource their responsibility. Independent living schemes are here to stay, but without urgent reform, vulnerable older people will continue to be exploited, and that’s not a problem we should simply step over.”
Thanks to Julia for the feedback! 🦒 Thanks for reading! See you next week for another issue! 👋
Thanks for writing this, it clarifies a lot. Your point about AI augmenting, not replacing, humans, as you often highlight, is spot on. The nuance around human-in-the-loop dynamics and the true total cost of ownership for AI-generated code is so important. Realy insightful, Alex.
Great post ! Looks like the full Avenir deck is gated ... unsure if it is a public information we can look / download...