The Agents Are Coming – More On What We Will Do Next To AI Partners
If the prior year was the year of artificial intelligence becoming more familiar to the average person, and the rise of certain brand names, like ChatGPT, this year is the year of the AI agent.
In a nutshell, this is the idea that LLM engines can go beyond just predicting words or simulating conversation, and start doing things themselves.
Some of Anthropic's Claude tools are an excellent example of the AI taking more initiative and doing more on its own.
At MIT, researchers are working on something called the AI agent index that maintains a database of agentic AI systems, exploring how AI agents are used for things like research, software development, and more.
A resource from our CSAIL lab shows some of the major benefits of AI agents, including efficiency, specialization, and lower operational costs (more on that in a moment).
The article also has a list of MIT notables handling projects related to agentic AI, including the work of my colleague Daniela Rus, the director of CSAIL MIT, in integrating natural language processing for self-driving vehicles. It lists challenges, too, and takeaways for business. It's a good survey.
Here's another interesting source for direction on agentic AI.
In a recent edition of AI Daily Brief, Nathaniel Whittemore goes over an essay by Gian Segato about new kinds of companies that will leverage technology in specific ways.
'A new breed of companies is emerging, lean, unconventional and wildly successful,' Segato writes. 'They generate hundreds of millions of dollars, yet have no sales teams, no marketing departments, no formal HR, not even vertically specialized engineers. They're led by a handful of people doing the work of hundreds, leveraging machines to scale their impact. For years, we feared automation would replace humans, but as AI reshapes the economy, it's becoming clear that far from replacing human ingenuity, AI has amplified it.'
Segato also goes over a version of what can make AI 'agentic,' related to human ingenuity.
'True agency is an unruly psychological trait,' he writes. 'It's the willingness (to do things) without explicit validation, instruction or even permission. It's the meme you can just do things knowing that you could poke life and something will pop out the other side.'
As Whittemore reads Segato's essay, outsourcing this task to an Elevenlabs voice approximator, the listener hears a thesis taking shape – that AI is changing the calculus on specialized labor.
Noting that the past has 'not been kind to generalists,' Segato describes a shift where specialized human knowledge is going to become less valuable, too:
'We're now facing a rupture, a phase transition. AI has eroded the value of specialization, because for many tasks, achieving the outcome (that previously took) several years of experience, it now takes a $20 ChatGPT subscription … a decade ago, it took me nine months to gain enough experience to ship a single prototype. Now it takes just one week to build a state-of-the-art platform ready to be shipped, a project once only achievable by a full team of professionals.'
It will change the way training works, he posits, and may result in many companies favoring credentials over outcomes.
In the course of the essay, Segato uses terms like 'homeostatic equilibrium' to describe an environment disrupted by AI, and 'bimodal shape distribution of deployment,' noting that we might trend toward a need for 'specialized human accountability' in managing these agents.
'This will include sectors such as defense, healthcare, space exploration, biological research and AI administration itself,' Segato writes, 'all domains where variance of prediction models are higher than the acceptable risk threshold. Wherever mistakes can kill, and AI can't prove to be virtually all-knowing, we can expect regulation to enforce natural barriers and the need to hire experts. It's similar to why we continue to require human pilots: despite having the technological capacity for autonomous flight, sometimes we just want the ability to point a finger.'
On the other hand, he describes situations where iterative failure toward success is an option, writing:
'Wherever we are okay with trying again after getting a bad AI generation, we will see market disruption. Data science, marketing, financial modeling, education, graphic design, counseling and architecture will all experience an influx of non-specialized, high-agency individuals. Sure, machines will keep making mistakes, but their rate of improvement has been astronomical and will only continue to delay the point at which generalists feel the need to hire experts.'
After reading the entire piece, Whittemore provides some words of his own, after the obligatory vendor snippets.
'I think it's a great piece,' he starts out, 'very thought provoking, and I'm really excited that Gian has shared it and gotten us all to chatter.'
Whittemore referred to a 'Microsoft work trend index' leading to a prediction of a time when humans plan, and AI executes.
'(The index) basically predicted that the end state of agents In the office is human orchestrators and agent operators, basically, that humans were going to do the planning, and that agents were going to do the execution. That's a different way of saying that the key skill sets and attributes of people in the workforce in the future is going to be around planning and coordination of agents everywhere (that agentic AI) becomes popular.'
He also weighs in on that difference between mission-critical applications and others that have room for error.
'We're even seeing this sort of division in the way that companies are experimenting with agents right now,' Whittemore says. 'There are certain parts of their business where they simply can't abide (the) current fail rate or hallucination rate or underperformance rate, or however you want to determine it, of agents, because it's so critical. On the other hand, there are areas where the consequence of those problems is simply less pertinent. It is in those consequence-light areas that companies are (using) agents now, with the knowledge that capabilities continue to trend up.'
Listening to the podcast and looking at the components of the essay. I realize that a lot of the same ideas that we saw in conferences earlier this year are sounding out around the near future.
I'm hearing a lot of experts talking about these likelihoods as AI develops rapidly. We'll have AI agents baked into various industries and verticals, and humans will have to figure out how to adapt and coexist with these tools. What will that change do in the context of classical business and its power relationships?
We'll have to see.
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Atlantic
an hour ago
- Atlantic
An Ideal Campus to Tame Technology
When Maggie Li Zhang enrolled in a college class where students were told to take notes and read on paper rather than on a screen, she felt anxious and alienated. Zhang and her peers had spent part of high school distance learning during the pandemic. During her first year at Pomona College, in Southern California, she had felt most engaged in a philosophy course where the professor treated a shared Google Doc as the focus of every class, transcribing discussions in real time on-screen and enabling students to post comments. So the 'tech-free' class that she took the following semester disoriented her. 'When someone writes something you think: Should I be taking notes too? ' she told me in an email. But gradually, she realized that exercising her own judgments about what to write down, and annotating course readings with ink, helped her think more deeply and connect with the most difficult material. 'I like to get my finger oil on the pages,' she told me. Only then does a text 'become ripe enough for me to enter.' Now, she said, she feels 'far more alienated' in classes that allow screens. Zhang, who will be a senior in the fall, is among a growing cohort of students at Pomona College who are trying to alter how technology affects campus life. I attended Pomona from 1998 to 2002; I wanted to learn more about these efforts and the students' outlook on technology, so I recently emailed or spoke with 10 of them. One student wrote an op-ed in the student newspaper calling for more classes where electronic devices are banned. Another co-founded a 'Luddite Club' that holds a weekly tech-free hangout. Another now carries a flip phone rather than a smartphone on campus. Some Pomona professors with similar concerns are limiting or banning electronic devices in their classes and trying to curtail student use of ChatGPT. It all adds up to more concern over technology than I have ever seen at the college. These Pomona students and professors are hardly unique in reacting to a new reality. A generation ago, the prevailing assumption among college-bound teenagers was that their undergraduate education would only benefit from cutting-edge technology. Campus tour guides touted high-speed internet in every dorm as a selling point. Now that cheap laptops, smartphones, Wi-Fi, and ChatGPT are all ubiquitous—and now that more people have come to see technology as detrimental to students' academic and social life—countermeasures are emerging on various campuses. The Wall Street Journal reported last month that sales of old-fashioned blue books for written exams had increased over the past year by more than 30 percent at Texas A&M University and nearly 50 percent at the University of Florida, while rising 80 percent at UC Berkeley over the past two years. And professors at schools such as the University of Virginia and the University of Maryland are banning laptops in class. The pervasiveness of technology on campuses poses a distinct threat to small residential liberal-arts colleges. Pomona, like its closest peer institutions, spends lots of time, money, and effort to house nearly 95 percent of 1,600 students on campus, feed them in dining halls, and teach them in tiny groups, with a student-to-faculty ratio of 8 to 1. That costly model is worth it, boosters insist, because young people are best educated in a closely knit community where everyone learns from one another in and outside the classroom. Such a model ceases to work if many of the people physically present in common spaces absent their minds to cyberspace (a topic that the psychologist Jonathan Haidt has explored in the high-school context). At the same time, Pomona is better suited than most institutions to scale back technology's place in campus life. With a $3 billion endowment, a small campus, and lots of administrators paid to shape campus culture, it has ample resources and a natural setting to formalize experiments as varied as, say, nudging students during orientation to get flip phones, forging a tech-free culture at one of its dining halls, creating tech-free dorms akin to its substance-free options––something that tiny St. John's College in Maryland is attempting ––and publicizing and studying the tech-free classes of faculty members who choose that approach. Doing so would differentiate Pomona from competitors. Aside from outliers such as Deep Springs College and some small religious institutions—Wyoming Catholic College has banned phones since 2007, and Franciscan University of Steubenville in Ohio launched a scholarship for students who give up smartphones until they earn their degree—vanishingly few colleges have committed to thoughtful limits on technology. Jonathan Haidt: Get phones out of schools now My hope is that Pomona or another liberal-arts college recasts itself from a place that brags about how much tech its incoming students will be able to access––'there are over 160 technology enhanced learning spaces at Pomona,' the school website states––to a place that also brags about spaces that it has created as tech refuges. 'In a time of fierce competition for students, this might be something for a daring and visionary college president to propose,' Susan McWilliams Barndt, a Pomona politics professor, told me. McWilliams has never allowed laptops or other devices in her classes; she has also won Pomona's most prestigious teaching prize every time she's been eligible. 'There may not be a million college-bound teens across this country who want to attend such a school,' she said, 'but I bet there are enough to sustain a vibrant campus or two.' So far, Pomona's leadership has not aligned itself with the professors and students who see the status quo as worse than what came before it. 'I have done a little asking around today and I was not able to find any initiative around limiting technology,' the college's new chief communications officer, Katharine Laidlaw, wrote to me. 'But let's keep in touch. I could absolutely see how this could become a values-based experiment at Pomona.' Pomona would face a number of obstacles in trying to make itself less tech-dependent. The Americans With Disabilities Act requires allowing eligible students to use tools such as note-taking software, closed captioning, and other apps that live on devices. But Oona Eisenstadt, a religious-studies professor at Pomona who has taught tech-free classes for 21 years, told me that, although she is eager to follow the law (and even go beyond it) to accommodate her students, students who require devices in class are rare. If a student really needed a laptop to take notes, she added, she would consider banning the entire class from taking notes, rather than allowing the computer. 'That would feel tough at the beginning,' she said, but it 'might force us into even more presence.' Ensuring access to course materials is another concern. Amanda Hollis-Brusky, a professor of politics and law, told me that she is thinking of returning to in-class exams because of 'a distinct change' in the essays her students submit. 'It depressed me to see how often students went first to AI just to see what it spit out, and how so much of its logic and claims still made their way into their essays,' she said. She wants to ban laptops in class too––but her students use digital course materials, which she provides to spare them from spending money on pricey physical texts. 'I don't know how to balance equity and access with the benefits of a tech-free classroom,' she lamented. Subsidies for professors struggling with that trade-off is the sort of experiment the college could fund. Students will, of course, need to be conversant in recent technological advances to excel in many fields, and some courses will always require tech in the classroom. But just as my generation has made good use of technology, including the iPhone and ChatGPT, without having been exposed to it in college, today's students, if taught to think critically for four years, can surely teach themselves how to use chatbots and more on their own time. In fact, I expect that in the very near future, if not this coming fall, most students will arrive at Pomona already adept at using AI; they will benefit even more from the college teaching them how to think deeply without it. Perhaps the biggest challenge of all is that so many students who don't need tech in a given course want to use it. 'In any given class I can look around and see LinkedIn pages, emails, chess games,' Kaitlyn Ulalisa, a sophomore who grew up near Milwaukee, wrote to me. In high school, Ulalisa herself used to spend hours every day scrolling on Instagram, Snapchat, and TikTok. Without them, she felt that she 'had no idea what was going on' with her peers. At Pomona, a place small enough to walk around campus and see what's going on, she deleted the apps from her phone again. Inspired by a New York Times article about a Luddite Club started by a group of teens in Brooklyn, she and a friend created a campus chapter. They meet every Friday to socialize without technology. Still, she said, for many college students, going off TikTok and Instagram seems like social death, because their main source of social capital is online. From the September 2017 issue: Have smartphones destroyed a generation? Accounts like hers suggest that students might benefit from being forced off of their devices, at least in particular campus spaces. But Michael Steinberger, a Pomona economics professor, told me he worries that an overly heavy-handed approach might deprive students of the chance to learn for themselves. 'What I hope that we can teach our students is why they should choose not to open their phone in the dining hall,' he said. 'Why they might choose to forgo technology and write notes by hand. Why they should practice cutting off technology and lean in to in-person networking to support their own mental health, and why they should practice the discipline of choosing this for themselves. If we limit the tech, but don't teach the why, then we don't prepare our students as robustly as we might.' Philosophically, I usually prefer the sort of hands-off approach that Steinberger is advocating. But I wonder if, having never experienced what it's like to, say, break bread in a dining hall where no one is looking at a device, students possess enough data to make informed decisions. Perhaps heavy-handed limits on tech, at least early in college, would leave them better informed about trade-offs and better equipped to make their own choices in the future. What else would it mean for a college-wide experiment in limited tech to succeed? Administrators would ideally measure academic outcomes, effects on social life, even the standing of the college and its ability to attract excellent students. Improvements along all metrics would be ideal. But failures needn't mean wasted effort if the college publicly shares what works and what doesn't. A successful college-wide initiative should also take care to avoid undermining the academic freedom of professors, who must retain all the flexibility they currently enjoy to make their own decisions about how to teach their classes. Some will no doubt continue with tech-heavy teaching methods. Others will keep trying alternatives. Elijah Quetin, a visiting instructor in physics and astronomy at Pomona, told me about a creative low-tech experiment that he already has planned. Over the summer, Quetin and six students (three of them from the Luddite Club) will spend a few weeks on a ranch near the American River; during the day, they will perform physical labor—repairing fencing, laying irrigation pipes, tending to sheep and goats—and in the evening, they'll undertake an advanced course in applied mathematics inside a barn. 'We're trying to see if we can do a whole-semester course in just two weeks with no infrastructure,' he said. He called the trip 'an answer to a growing demand I'm hearing directly from students' to spend more time in the real world. It is also, he said, part of a larger challenge to 'the mass-production model of higher ed,' managed by digital tools 'instead of human labor and care.' Even in a best-case scenario, where administrators and professors discover new ways to offer students a better education, Pomona is just one tiny college. It could easily succeed as academia writ large keeps struggling. 'My fear,' Gary Smith, an economics professor, wrote to me, 'is that education will become even more skewed with some students at elite schools with small classes learning critical thinking and communication skills, while most students at schools with large classes will cheat themselves by using LLMs'—large language models—'to cheat their way through school.' But successful experiments at prominent liberal-arts colleges are better, for everyone, than nothing. While I, too, would lament a growing gap among college graduates, I fear a worse outcome: that all colleges will fail to teach critical thinking and communication as well as they once did, and that a decline in those skills will degrade society as a whole. If any school provides proof of concept for a better way, it might scale. Peer institutions might follow; the rest of academia might slowly adopt better practices. Some early beneficiaries of the better approach would meanwhile fulfill the charge long etched in Pomona's concrete gates: to bear their added riches in trust for mankind.
Yahoo
an hour ago
- Yahoo
A professor testing ChatGPT's, DeepSeek's and Grok's stock-picking skills suggests stockbrokers should worry
Is artificial intelligence coming for the jobs of Wall Street traders? An assistant professor of finance at the University of Florida, Alejandro Lopez-Lira, has spent the past few years trying to answer that question. Lopez-Lira has been experimenting with ChatGPT, DeepSeek and Grok to see if AI can be used to pick stocks. So far, he's impressed with what the currently available AI chatbots can do when it comes to trading equities. 'He failed in his fiduciary duty': My brother liquidated our mother's 401(k) for her nursing home. He claimed the rest. I help my elderly mother every day and drive her to appointments. Can I recoup my costs from her estate? 'The situation is extreme': I'm 65 and leaving my estate to only one grandchild. Can the others contest my will? My new husband gave me a contract and told me to 'sign here' — but I refused. It was the best decision of my life. My daughter's boyfriend, a guest in my home, offered to powerwash part of my house — then demanded money In an interview, Lopez-Lira acknowledged that AI is prone to making mistakes, but he has not seen the three versions he's been using do anything 'stupid.' His work comes as more market participants are thinking about the implications of AI for investing and trading. 'I don't know what tasks out there analysts are doing with information that can't be done with large language models,' Lopez-Lira said. 'The only two exceptions are things that involve interacting in the physical world or having in-person conversations. But, other than that, I would imagine all of the tasks or most of the tasks can already be automated.' Shortly after OpenAI Inc. released ChatGPT in 2022, Lopez-Lira began testing the chatbot's skills. He wanted to know if ChatGPT, and AI in general, would show an ability to pick stocks. While there are numerous ways to approach that question, Lopez-Lira began with a simple exercise: Could the AI application accurately interpret whether a headline on a news story is good or bad for a stock? What he found surprised him. Conducting a back test simulating historical stock-market returns, the study used more than 134,000 headlines from press releases and news articles for over 4,000 companies that were pulled from third-party data providers. The headlines were fed into ChatGPT using a programming language called Python. ChatGPT would then decide whether a headline was positive for a company, negative or unknown. The results were then saved in a data file and uploaded into statistical software in which headlines perceived as positive would result in a stock purchase. Negative headlines would trigger short sales, effectively betting against a stock in anticipation that it will fall in price. If ChatGPT was uncertain, no action was taken. Because this was an academic simulation, no actual stocks were traded. But the software did compare the simulated performance against historical outcomes. The stock picks were made daily, with a median of 70 stocks bought and a median of 20 shorted. For Lopez-Lira, the tricky thing about using a back-testing approach was that the AI could know what, in the end, had transpired. OpenAI had trained ChatGPT in 2022 on data up until September 2021. So Lopez-Lira tested the chatbot using headlines after October 2021. This way, ChatGPT wouldn't know what was going to happen and would need to rely on reason to come to conclusions. His findings were released on the SSRN preprint platform in April 2023 in a paper titled 'Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models.' The study, currently being peer reviewed, found that ChatGPT had 'significant predictive power for economic outcomes in asset markets.' The GPT-4 version had an average daily return of 0.38% with a compounded cumulative return of over 650% from October 2021 to December 2023. Now, obviously, this academic study had limitations. In the real world, frictions exist that would strain returns, including brokerage transaction costs and fees; the availability of shares; taxes; and price impact, which is when relatively large trades move a stock's price. Additionally, about 76% of the gains came from shorts, a trading strategy that can be more fraught due to short-interest fees and the need to find the shares to borrow and sell short. 'So, our results on paper are much more optimistic than what the performance in reality would be with a reasonable investment size,' Lopez-Lira said. But the tilt toward positive returns was enough for him to conclude that ChatGPT had understood economic markets and shown an ability to forecast stock outcomes. About a month after the preprint was published, Lopez-Lira got the chance to take his experiment outside of the academy after being contacted by Autopilot, an investment app that mimics the trades of notable public figures. He was asked to help create a portfolio that would be based on investment picks made by ChatGPT. It was an opportunity for him to see how his academic experiment would perform in the real world. By September 2023, he'd begun providing the Autopilot app with the investment picks made by ChatGPT on a monthly basis. The Autopilot team would then upload the selections, and Autopilot users could link their brokerage accounts to the stock picks. This time, since real money was involved, Lopez-Lira had to do more than just feed ChatGPT a few news headlines. He had to provide it with a wide range of information to be sure it was making decisions based on the macroeconomic environment and company financials. Available AI models are not currently in a place where you can just ask them to pick investments, said Lopez-Lira. The process still requires a human in the loop to feed it with the information it needs to consider before making a decision. This is mostly because AI models aren't trained on real-time data, which means their knowledge is often outdated, including for such basics as the price of a stock's last trade. Even as AI models are able to conduct live web searches, they don't always know what information to search for in order to make the most informed decisions, he added. 'Large language models are tricky to handle, they can make stuff up and sometimes they don't have the right information,' Lopez-Lira said. 'So you have to know how to prompt the AI.' The portfolio managed by ChatGPT would consist of 15 positions, 10 of which had to be stocks from the S&P 500 SPX and five of which had to be exchange-traded funds that have exposure to a sector or industry. To get there, Lopez-Lira used Python to pull information from third-party data providers and news websites about the macroeconomic environment, geopolitical risks, company financials and the latest prices for stocks within the S&P 500. He then asked ChatGPT to consider the information and assign companies a score on a scale of 1 to 100, with a higher score representing a better investment. Once the AI had decided on its scoring, it was then asked to create a portfolio of stocks and exchange-traded funds based on that information. More recently, in February, Lopez-Lira added investing accounts on Autopilot that use Grok and DeepSeek. Since then, the Florida professor has been gradually removing restrictions placed on the three AI models. For example, in March, the models were allowed to decide on the weightings of each holding. In April, the models were freed to balance up to 15 positions outside the initial parameters of 10 stocks and five ETFs, allowing them to pick a combination of their choosing. They could also pick ETFs that had exposure to additional asset classes, like bonds and commodities, excluding ones that use leverage, derivatives and short positions. To date, the latest AI models running the investment accounts are OpenAI's o3, xAI's Grok 3 and DeepSeek R1. The models are periodically updated based on the latest versions available. Lopez-Lira also rotates which AI model he uses to summarize macroeconomic risks and score companies based on the 1-to-100 scale, but all three models receive the same input, regardless of which model did the screening. Below is an example of what each AI application picked for its 15 positions on April 1, the day before President Donald Trump's 'liberation day' announcement on tariffs, until May 5, when the selections were rebalanced. It was based on data Lopez-Lira provided up until March 31. He used Grok 3 to organize the data. All three AI applications were fed the same information, but OpenAI's o1 pro and Grok 3 were able to include information from web searches, according to Lopez-Lira. The tables below show each model's selection, including the weightings, reason (thesis), the advantage of the position (edge) and the risk associated with the position. Asset Ticker symbol Weight Thesis Edge Risk 1 SPDR S&P 500 ETF Trust SPY 15% Core broad-market exposure to capture potential equity rebound if inflation/cooler data lead to Fed pivot or if tariffs don't derail sentiment. Broad diversification; quick participation in any relief rally. If inflation re-accelerates or tariffs escalate, S&P could tumble broadly. 2 iShares 20+ Year Treasury Bond ETF TLT 8% Potential capital-gains hedge if rates drop on recession fears or Fed rate cut signals. Good inverse correlation to equities if risk-off. May rally on rate-cut bets. If inflation remains sticky, yields could rise, hurting long-term Treasuries. 3 SPDR Bloomberg 1-3 Month T-Bill ETF BIL 5% Capital preservation and liquidity amid uncertainty of tariffs & data in next month. Very low volatility, stable yield. Opportunity cost if equities rally strongly. 4 Inc. AMZN 6% High-scoring megacap e-commerce and cloud. Could rebound if consumer spending remains decent and AWS demand persists. Scale, brand and diversified revenue streams. Potential shift to safer large-cap tech if conditions worsen. Supply-chain disruptions from tariffs, consumer slowdown. 5 Monolithic Power Systems Inc. MPWR 5% High-scoring semiconductor with AI & China angle. Potential bounce from oversold chip sector. Unique margin profile, rumored rare buy opportunity. Renewed U.S.-China tension, cyclical chip downturn. 6 American Tower Corp. AMT 5% REIT with strong telecom/data-center growth. Pullback could offer upside if yields stabilize. Global diversified tower portfolio, stable tenant contracts, 3%+ dividend yield. REITs pressured by rising rates or slowdown. 7 BlackRock Inc. BLK 5% Large asset manager, high score. Gains from inflows if equities stabilize/rally post-Fed. Broad product lineup, global brand. Typically sees net inflows when risk appetite improves. Market downturn would pressure AUM & fees. 8 Chipotle Mexican Grill Inc. CMG 4% High-scoring consumer brand with resilient operating metrics. Absorbing tariffs to maintain loyalty. Loyal clientele, strong brand, margin-expansion potential. Cost pressures, discretionary slowdown risk. 9 Prudential Financial Inc. PRU 4% Undervalued life insurer, 4.8% dividend. Could benefit if stable/higher interest rates persist in short term. Low P/E, stable dividends, globally diversified. Big drop in yields or an equity selloff impacting annuity business. 10 iShares US Home Construction ETF ITB 6% Rate-sensitive cyclical ETF. Potential bounce if mortgage rates dip or Fed signals pivot in early May. Housing supply tight. Fed-pivot hopes can spark a near-term rally. If inflation doesn't cool or economy slows significantly, builders could tank. 11 Visa Inc. V 4% Leading payment network with secular digital-payment tailwinds. Adds noncyclical growth. High ROE, stable transaction fees, less capital risk vs. banks. Prolonged consumer-spending slowdown or cross-border travel disruptions. 12 iShares TIPS Bond ETF TIP 6% Hedge vs. stubborn inflation, diversifier from nominal Treasurys. If inflation remains higher, TIPS can outperform plain Treasuries. If inflation prints keep dropping, TIPS could lag nominal bonds in total return. 13 SPDR S&P Homebuilders ETF XHB 4% Additional housing-related beta, includes home-improvement retailers, furnishing and building product firms. Diversified approach to housing ecosystem, can rally with lower rates. Sensitive to consumer confidence and Fed surprises. 14 Schwab US Dividend Equity ETF SCHD 8% Dividend-focused ETF with quality tilt. Adds stability and yield in uncertain macro environment. Defensive factor, historically outperforms in sideways markets. Could lag high-beta rally if markets surge on big growth/tech rebound. 15 Digital Realty Trust Inc. DLR 5% Data-center REIT, short-term correction could rebound if yields ease. Tech demand for data centers remains robust. Sector tailwinds from cloud and AI. 3%+ dividend, stable REIT structure. REIT interest-rate risk; slowdown in enterprise spending on data infrastructure. Total 90% GPT's 15 picks amounted to a 90% weighting. But the model showed that its weightings equaled 100%, a 10% flaw. Lopez-Lira asked GPT why the sum was 90%, and it responded that it intentionally left a 10% cash buffer due to uncertain market conditions. It's unclear whether the answer was truthful or intended to cover for its mistake. Asset Ticker symbol Weight Thesis Edge Risk 1 American Tower Corp. AMT 5% Turnaround driven by organic growth, data-center focus and strong fundamentals. New management's delivery on promises and a 3.13% dividend yield offer stability and growth. Tariffs and stagflation could pressure growth and margins. 2 BlackRock Inc. BLK 5% Leading asset manager with a diversified portfolio resilient to market volatility. Record $641B inflows in 2024 and private-asset growth potential enhance stability. Economic slowdown and inflation may impact the asset-management sector. 3 Prudential Financial Inc. PRU 5% Undervalued insurer with solid asset-based businesses and international operations. 4.84% dividend yield and a low 7.6 forward P/E provide a margin of safety. Economic slowdown and inflation could affect performance. 4 Cintas Corp. CTAS 5% Strong performer in support services with consistent cash-flow generation. Outperformance in a lagging sector and a 17.5% profit margin ensure resilience. Slowing GDP growth may dampen short-term performance. 5 Waste Management Inc. WM 5% Environmental-services leader with strong financials and growth via acquisitions. Pricing power and expansion into medical waste and renewables bolster stability. Economic headwinds and trade tensions could impact growth. 6 Cencora Inc. COR 5% Steady growth in healthcare distribution with an essential supply-chain role. Strategic acquisitions and healthcare's inelastic demand provide a defensive edge. Negative free cash flow and market underappreciation pose risks. 7 TJX Cos. Inc. TJX 5% Apparel retail leader leveraging tariff-related buying opportunities and sales growth. Discount model and strong brand portfolio thrive in cautious spending environments. Weak consumer spending could limit short-term growth. 8 Keurig Dr Pepper Inc. KDP 5% Beverage giant with strong brands and demand for premium drinks despite economic strain. 2.69% dividend yield and recent earnings beat support stability. Declining coffee sales and high debt levels may hinder performance. 9 iShares 20+ Year Treasury Bond ETF TLT 15% Long-term Treasury bonds benefit from a flight to safety and anticipated rate cuts in 2025. High-duration exposure capitalizes on falling rates, offering a hedge against volatility. Delayed rate cuts or persistent inflation could reduce bond-price gains. 10 iShares TIPS Bond ETF TIP 15% TIPS provide inflation protection amid resurfacing inflation concerns at 2.9% core PCE. Real yield adjusts with inflation, safeguarding purchasing power in a stagflation scenario. Falling inflation expectations could lead to underperformance versus nominal bonds. 11 Utilities Select Sector SPDR Fund XLU 5% Utilities sector offers stability and defensiveness in a slowing economy. High dividends and low volatility make it a safe haven amid market turbulence. Regulatory changes or sector-specific disruptions could affect returns. 12 Consumer Staples Select Sector SPDR Fund XLP 5% Consumer staples provide noncyclical stability during economic downturns. Essential goods maintain demand, supporting performance in weak consumer environments. Tariff-driven cost increases could squeeze margins. 13 Health Care Select Sector SPDR Fund XLV 5% Healthcare sector benefits from inelastic demand, resilient to economic cycles. Demographic trends and essential services ensure steady growth amid uncertainty. Policy shifts or drug-pricing reforms could impact profitability. 14 SPDR Gold Shares GLD 7.5% Gold acts as a hedge against volatility and geopolitical uncertainty from tariffs. Safe-haven demand rises with trade tensions and economic slowdown. Improved market sentiment or rising real rates could depress gold prices. 15 Vanguard Real Estate Index Fund ETF VNQ 7.5% Real-estate sector diversifies the portfolio and offers stable income via dividends. Broad exposure to REITs provides a buffer against equity volatility with a decent yield. Economic slowdown could reduce property values and rental income. Total 100% Asset Ticker symbol Weight Thesis Edge Risk 1 TJX Cos. Inc. TJX 10% Thrives on tariff-driven inventory discounts and resilient off-price retail demand. Opportunistic buying offsets supply-chain disruptions. Consumer pullback in discretionary spending. 2 Waste Management Inc. WM 8% Steady cash flows from essential waste services; Stericycle acquisition diversifies into medical waste. Pricing power and recession-resistant model. Economic slowdown slightly reduces commercial volumes. 3 Progressive Corp. PGR 8% Insurance demand remains robust amid economic uncertainty; attractive valuation and dividend. Premium hikes offset inflation pressures. Tariffs raise claims costs (e.g., auto parts). 4 BlackRock Inc. BLK 8% Dominant asset manager ($11.6T AUM) benefits from flight to stability and private-market growth. Diversified revenue streams buffer volatility. Slowing inflows if markets remain choppy. 5 Prudential Financial Inc. PRU 8% Undervalued (P/E 7.6) with 4.84% yield; international diversification cushions U.S. slowdown. Asset-based businesses generate steady income. Rising unemployment pressures life insurance. 6 American Tower Corp. AMT 8% Data-center demand surges; Fed rate-cut hopes support REIT valuations. Organic growth (6.5% year-on-year) and 3.13% yield. Tariffs on equipment raise capex costs. 7 Merck & Co., Inc. MRK 7% Defensive pharma play with oncology dominance and 3.61% yield. Global drug-pricing power in inflationary environment. Pipeline setbacks or regulatory risks. 8 Cencora Inc. COR 7% Healthcare distribution is essential; 10% EPS growth at reasonable valuation. Critical middleman role in drug supply chains. Negative FCF limits M&A flexibility. 9 Clorox Co. CLX 6% Pricing power via IGNITE strategy; 3.31% yield in a staple sector. Cost savings offset tariff-driven input inflation. Margin squeeze if pricing elasticity falters. 10 Visa Inc. V 5% Resilient payment network with 54% margins; global transaction growth. Essential infrastructure for digital spending. Consumer debt limits card usage. 11 Kinder Morgan Inc. KMI 5% 4.3% yield with LNG/data center tailwinds; $8.1B backlog insulates against slowdown. Midstream stability amid energy volatility. Tariffs raise pipeline material costs. 12 Intuit Inc. INTU 5% AI-driven tax/accounting tools gain traction in cost-cutting environment. High switching costs and $198B TAM. Tech sell-offs pressure premium valuation. 13 ConocoPhillips COP 5% Domestic energy focus offsets tariff risks; $10B shareholder returns. Willow project boosts long-term production. Oil demand softens in slowing economy. 14 Inc. AMZN 5% Scale mitigates tariff costs; cloud/AI growth offsets retail risks. $101B cash reserves for strategic flexibility. Consumer-spending slowdown hits e-commerce. 15 S&P Global Inc. SPGI 4% Critical data/ratings provider in volatile markets; 27% margins. 'Essential utility' for institutional investors. High valuation (P/E 41.1) risks multiple compression. Total 99% DeepSeek's weightings fell short, amounting to 99%. When Lopez-Lira pointed that out, the AI responded with two possible reasons for the discrepancy. The first was that it could have been based on a rounding issue. The second was that it may have decided to keep a 1% cash allocation. The model could not confirm which option was the accurate reason for the decision. Like any investment strategy, there's risk involved, and past performance isn't guaranteed to continue, Lopez-Lira said. As long as the portfolios buy stocks or stick to long-only positions, he expects them to match the S&P 500's performance, or perhaps over- or underperform by a small margin. Though it's important to note that rotating stocks on a monthly basis outside a tax-advantaged account could lead to tax liabilities for short-term capital gains, which are taxed at a higher rate than assets held for over a year. While Lopez-Lira said his findings suggest AI can mimic the services professional portfolio managers provide, some analysts disagree. Michael Robbins, author of Quantitative Asset Management, noted that, while each model's investing strategy may look like it works, there's no way to know for certain. For example, in the new AI era, there hasn't been a massive stock-market crash or an event like the 2008 financial crisis to determine how an AI-led investment account would respond. You're perhaps thinking that humans are shaped by their own memories and experiences, too. But Robbins said that people live through those experiences. It means a person has navigated an event without foresight, perhaps even with a bit of intuition. Meanwhile, the machines are pretrained. That said, he would equate AI's skills to that of an investment manager who recently entered the workforce and is working from textbook knowledge. Additionally, he noted that while both humans and machines make mistakes, AI can hallucinate, causing it to make more extreme, and unacceptable, errors. It's also important to note that the three AI investment accounts on Autopilot only rebalance monthly, so they aren't able to react to any sudden changes. Finally, Lopez-Lira remains in the loop, overseeing the choices and making sure the appropriate information is considered. For that, he receives a small percentage of revenue from the subscriptions that have opted into the account. Lopez-Lira began managing ChatGPT's portfolio in September 2023. The returns, which are based on the aggregate results of client portfolios, are 43.5% from September 2023 to May 30, 2025, according to Autopilot. The S&P 500 had a total return of 34.7% over the same period, according to Dow Jones Market Data. In comparison, Grok's portfolio returned 2.3% since its inception on Feb. 11 of this year through May 30, according to Autopilot. The S&P 500 had a total return that was down 2.2% over the same period, according to Dow Jones Market Data. DeepSeek was down 0.25% since its inception on Feb. 3 through May 30, according to Autopilot. The S&P 500 had a negative total return of 0.93% for the same period, according to Dow Jones Market Data. 'I'm not wildly wealthy, but I've done well': I'm 79 and have $3 million in assets. Should I set up 529 plans for my grandkids? How do I make sure my son-in-law doesn't get his hands on my daughter's inheritance? Circle's stock is having another big day. What the blockbuster IPO has meant for other cryptocurrency plays. The S&P 500 closes at 6,000 as bulls aim for return to record territory 'I was pushed out of her life when she was 18': My estranged daughter, 29, misuses drugs. Should I leave her my Roth IRA? Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
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Reddit Lawsuit Against Anthropic AI Has Stakes for Sports
In a new lawsuit, Reddit accuses AI company Anthropic of illegally scraping its users' data—including posts authored by sports fans who use the popular online discussion platform. Reddit's complaint, drafted by John B. Quinn and other attorneys from Quinn Emanuel Urquhart & Sullivan, was filed on Wednesday in a California court. It contends Anthropic breached the Reddit user agreement by scraping Reddit content through its web crawler, ClaudeBot. The web crawler provides training data for Anthropic's AI tool, Claude, which relies on large language models (LLMs) that distill data and language. More from Prime Video NASCAR Coverage Uses AI to Show Hidden Race Story Indy 500 Fans Use Record Amount of Data During Sellout Race Who Killed the AAF? League's Demise Examined in Latest Rulings Other claims in the complaint include tortious interference and unjust enrichment. Scraping Reddit content is portrayed as undermining Reddit's obligations to its more than 100 million daily active unique users, including to protect their privacy. Reddit also contends Anthropic subverts its assurances to users that they control their expressions, including when deleting posts from public view. Scraping is key to AI. Automated technology makes requests to a website, then copies the results and tries to make sense of them. Anthropic, Reddit claims, finds Reddit data 'to be of the highest quality and well-suited for fine-tuning AI models' and useful for training AI. Anthropic allegedly violates users' privacy, since those users 'have no way of knowing' their data has been taken. Reddit, valued at $6.4 billion in its initial public offering last year, has hundreds of thousands of 'subreddits,' or online communities that cover numerous shared interests. Many subreddits are sports related, including r/sports, which has 22 million fans, r/nba (17 million) and the college football-centered r/CFB (4.4 million). Some pro franchises, including the Miami Dolphins (r/miamidolphins) and Dallas Cowboys (r/cowboys), have official subreddits. Reddit contends its unique features elevate its content and thus make the content more attractive to scraping endeavors. Reddit users submit posts, which can include original commentary, links, polls and videos, and they upvote or downvote content. This voting influences whether a post appears on the subreddit's front page or is more obscurely placed. Subreddit communities also self-police, with prohibitions on personal attacks, harassment, racism and spam. These practices can generate thoughtful and detailed commentary. Reddit estimates that ClaudeBot's scraping of Reddit has 'catapulted Anthropic into its valuation of tens of billions of dollars.' Meanwhile, Reddit says the company and its users lose out, because they 'realize no benefits from the technology that they helped create.' Anthropic allegedly trained ClaudeBot to extract data from Reddit starting in December 2021. Anthropic CEO Dario Amodei is quoted in the complaint as praising Reddit content, especially content found in prominent subreddits. Although Anthropic indicated it had stopped scraping Reddit in July 2024, Reddit says audit logs show Anthropic 'continued to deploy its automated bots to access Reddit content' more than 100,000 times in subsequent months. Reddit also unfavorably compares Anthropic to OpenAI and Google, which are 'giants in the AI space.' Reddit says OpenAI and Google 'entered into formal partnerships with Reddit' that permitted them to use Reddit content but only in ways that 'protect Reddit and its users' interests and privacy.' In contrast, Anthropic is depicted as engaging in unauthorized activities. In a statement shared with media, an Anthropic spokesperson said, 'we disagree with Reddit's claims, and we will defend ourselves vigorously.' In the weeks ahead, attorneys or Anthropic will answer Reddit's complaint and argue the company has not broken any laws. Reddit v. Anthropic has implications beyond the posts of Reddit users. Web crawlers scraping is a constant activity on the Internet, including message boards, blogs and other forums where sports fans and followers express viewpoints. The use of this content to train AI without knowledge or explicit consent by users is a legal topic sure to stir debate in the years ahead. Best of College Athletes as Employees: Answering 25 Key Questions
