LeyesX: How CEO Kevin Leyes is Merging AI, Cybersecurity, and PR Strategy for Global Impact
At 25, Argentinian founder Kevin Leyes is building a very specific kind of modern company: one that treats reputation as a security surface, content as an asset class, and AI as core infrastructure rather than a novelty. Through LeyesX, his Miami-based holding, and its flagship agency Leyes Media, Leyes is fusing machine intelligence, brand-safety tooling, and narrative strategy for clients that range from high-growth founders to public figures and institutions.
From a floor mattress to a data-driven holding
Leyes grew up in González Catán and was raised in Pontevedra, Argentina. He cut lawns, flipped sneakers he sourced hours away by bus, and taught himself to program on a borrowed computer. At 17 he was selected as a Youth Ambassador by the U.S. Embassy, visiting Virginia, Seattle, and Washington D.C., and later trained in Silicon Valley through TrepCamp. Those early experiences set a simple thesis: skill plus systems can beat circumstance.
Back in Argentina he started Team Leyes in 2017, a custom jewelry line that serviced the country's emerging trap scene, including artists like Khea and Ecko. Profits seeded Leyes Media in 2019. By 2021 he relocated to Miami and consolidated everything under LeyesX, a holding that now spans AI, media, fintech education, and creative IP.
The stack: AI analytics, cybersecurity, and PR that measures
LeyesX and Leyes Media invest in three pillars that work together.
AI for foresight and measurement. Internal models track sentiment, spot narrative inflection points, and forecast campaign outcomes so teams can make timely adjustments. The same stack powers revenue projections and creator analytics where relevant.
Cyber and privacy by design. Identity protection, dox mitigation, leak detection and takedown, threat modeling for public figures, and data-minimization practices are embedded in client workflows. "If reputation is an asset, you protect it like one," says Leyes.
Strategy and editorial execution. Leyes Media couples data with relationships across U.S. and Latin media. The agency focuses on press placement, SEO-driven storytelling, and issues management for founders and public figures who need measurable outcomes, not vanity clips.
Advisory for institutions and governments
As LeyesX scaled its security-first playbook, Leyes began advising leaders on media risk, digital trust, and AI policy. In 2025 he received official recognition from the Government of El Salvador as an "International Entrepreneur of Distinction," a nod to his work advancing digital projection and strategic alliances in the region. That institutional layer now informs LeyesX's consulting for public entities seeking practical frameworks on AI adoption, brand safety, and crisis readiness.
Building creators safely, not loudly
Las Babys, formed within the LeyesX ecosystem, is a private, invite-only creator agency that prioritizes structure and protection over hype. Members receive brand development, global press via Leyes Media where appropriate, and privacy tooling that helps remove leaks quickly and harden accounts against harassment and doxing. The focus is long-term, values-aligned careers for women working in entertainment and the broader creator economy, with a clear emphasis on compliance, safety, and sustainable monetization.
Culture still matters: VVS returns
The jewelry brand that started it all is relaunching as VVS, produced in Miami with a tighter design language and selective collaborations. The line connects back to Leyes' earliest cultural roots and long-standing relationships in music, while operating with the operational discipline learned in tech and media.
Recognition and community roles
Since 2023, Leyes has served as a judge for the Davey Awards, the w3 Awards, and the Communicator Awards under the Academy of Interactive & Visual Arts. He participates in editorial and community forums such as Rolling Stone Culture Council, contributing on the intersection of culture, technology, and brand safety.
Roadmap, 2025–2028
LeyesX is preparing a Miami headquarters that combines an AI analytics lab, training spaces, and a broadcast studio, with U.S. hiring focused on data science, creative production, and account strategy. Leyes Media is expanding its U.S. footprint and client portal with live sentiment dashboards. The company is also formalizing credit-education initiatives through 800 Club, with strict safeguards and compliance, and growing a selective real-estate portfolio of short-term rentals designed to attract high-spend tourism in Florida before expanding to other states. Philanthropy is built in, with a defined profit share to STEM scholarships and entrepreneurship programs for underserved youth.
Why this matters
Entrepreneurs today operate in an environment where one breach, one deepfake, or one mispriced narrative can erase years of work. Leyes' answer is not louder messaging, it is defense-in-depth paired with creative excellence. The through-line from a bedroom studio in Buenos Aires to a data-led holding in Miami is discipline: measure what matters, protect the downside, and ship quality at speed.
"I do not confuse noise with traction," says Leyes. "AI helps us see around corners, cybersecurity keeps the doors locked, and smart storytelling earns trust. That mix is how you build brands that last."
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Yahoo
28 minutes ago
- Yahoo
Anthropic says some Claude models can now end ‘harmful or abusive' conversations
Anthropic has announced new capabilities that will allow some of its newest, largest models to end conversations in what the company describes as 'rare, extreme cases of persistently harmful or abusive user interactions.' Strikingly, Anthropic says it's doing this not to protect the human user, but rather the AI model itself. To be clear, the company isn't claiming that its Claude AI models are sentient or can be harmed by their conversations with users. In its own words, Anthropic remains 'highly uncertain about the potential moral status of Claude and other LLMs, now or in the future.' However, its announcement points to a recent program created to study what it calls 'model welfare' and says Anthropic is essentially taking a just-in-case approach, 'working to identify and implement low-cost interventions to mitigate risks to model welfare, in case such welfare is possible.' This latest change is currently limited to Claude Opus 4 and 4.1. And again, it's only supposed to happen in 'extreme edge cases,' such as 'requests from users for sexual content involving minors and attempts to solicit information that would enable large-scale violence or acts of terror.' While those types of requests could potentially create legal or publicity problems for Anthropic itself (witness recent reporting around how ChatGPT can potentially reinforce or contribute to its users' delusional thinking), the company says that in pre-deployment testing, Claude Opus 4 showed a 'strong preference against' responding to these requests and a 'pattern of apparent distress' when it did so. As for these new conversation-ending capabilities, the company says, 'In all cases, Claude is only to use its conversation-ending ability as a last resort when multiple attempts at redirection have failed and hope of a productive interaction has been exhausted, or when a user explicitly asks Claude to end a chat.' Anthropic also says Claude has been 'directed not to use this ability in cases where users might be at imminent risk of harming themselves or others.' When Claude does end a conversation, Anthropic says users will still be able to start new conversations from the same account, and to create new branches of the troublesome conversation by editing their responses. 'We're treating this feature as an ongoing experiment and will continue refining our approach,' the company says. 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
Yahoo
33 minutes ago
- Yahoo
This Artificial Intelligence (AI) Stock Is Dirt Cheap Compared to Its Growth
Key Points Chip stocks have been some of the biggest beneficiaries throughout the artificial intelligence (AI) revolution. While companies like Nvidia and AMD fetch the most attention, they rely heavily on the foundry services of TSMC. Despite notable valuation expansion, Taiwan Semiconductor remains dirt cheap based on one overlooked metric. 10 stocks we like better than Taiwan Semiconductor Manufacturing › One stock that has consistently outperformed the S&P 500 and Nasdaq Composite throughout the artificial intelligence (AI) revolution is the foundry and fabrication specialist Taiwan Semiconductor Manufacturing (NYSE: TSM). While its share price has posted monster gains of 174% over the last three years, there's still a good argument to be made that TSMC (as it's known for short) remains attractively valued. Let's dig into the catalysts fueling such epic growth at TSMC and then assess some lesser-understood valuation techniques that may help investors see why the stock still looks attractive at its current price point. TSMC's growth is off the charts... Before diving into TSMC's financial profile, it's worth reviewing how the company fits into the broader AI picture. Companies such as Nvidia, Advanced Micro Devices, and Broadcom have enjoyed record growth over the last few years thanks to booming demand for their GPU clusters and data center networking equipment. At the same time, hyperscalers such as Microsoft, Amazon, and Alphabet have experienced surging growth across their integrated AI ecosystems -- including applications in cloud computing infrastructure, cybersecurity, workplace productivity software, and more. While rising capital expenditures represent strong tailwinds for GPU and custom ASIC businesses, the trend is arguably even more favorable for foundry services such as TSMC. Why is that? Simply put, it actually manufactures many of the chipsets and systems equipment sold by the companies referenced above. Budget increases for chips and infrastructure represent a hidden -- and often overlooked -- tailwind for TSMC, regardless of whose chips are in demand. TSMC's mission-critical fabrication solutions provide the company with significant pricing power. These dynamics can be seen from the financial profile above, underscored by the company's steepening revenue growth trend in parallel with improving gross profit margins. ... and it appears it can sustain this growth One of the interesting aspects of TSMC's investor materials is that the company publishes revenue growth reports on a monthly basis rather than solely in a quarterly report. In the table below, I've summarized the company's monthly revenue growth throughout 2025: Category January February March April May June July Revenue growth YoY 35.9% 43.1% 46.5% 48.1% 39.6% 26.9% 25.8% Data source: TSMC Investor Relations. During the second quarter, TSMC generated $30 billion in sales thanks to continued demand for highly coveted 5nm and 3nm chip nodes. Revenue growth seems to have stalled a bit in June and July, but I do not see this as a long-term trend. Keep in mind that new GPU architectures such as Nvidia's Blackwell and AMD's MI350 and MI400 series are still in early stages of rollout and development. As infrastructure spending continues to accelerate across the AI landscape, TSMC is in position to benefit from such robust secular themes. Why I think TSMC stock is dirt cheap Common valuation methodologies often include ratios such as price-to-sales (P/S) or price-to-earnings (P/E). These metrics can be helpful when benchmarking a company against a set of peers, but they can be misleading when these ratios begin to expand meaningfully. For example, if you take a look at the chart below, you'll notice that TSMC's P/S and P/E multiples have risen throughout the AI revolution. Such a degree of valuation expansion might lead investors to believe that the stock is overbought and has become pricey. While such logic has merit, it does not always apply. A more nuanced way to value the chipmaker is by using its price/earnings-to-growth ratio (PEG), a metric popularized by legendary fund manager Peter Lynch. Essentially, it accounts for the P/E ratio as well as the earnings growth over a period of time. A good rule of thumb is that a PEG ratio below 1.0 signals that the stock is undervalued. Per the chart above, the stock has a PEG ratio based on next year's earnings of 0.6. I think the PEG ratio compression illustrated above can be attributed to a few factors. Wall Street's bullish view calls for the anticipation of accelerating earnings from TSMC supported by ongoing AI infrastructure spend. However, increased earnings revisions are likely outpacing appreciation in Taiwan Semi stock -- basically normalizing the company's PEG ratio without a sell-off as the primary driver. In addition, I think the market might be underpricing TSMC due to broader macro uncertainty surrounding geopolitical tensions with China or general cyclicality of the chip market. The combination of PEG ratio compression and a robust financial outlook could make the stock a textbook candidate for investors seeking growth at a reasonable price. To me, the stock is dirt cheap at its current price point relative to its growth. Investors with a long-term time horizon may want to take advantage of this rare opportunity to own a chip stock positioned to ride and dominate the AI infrastructure wave. While many semiconductor and AI stocks continue to trade at a premium, TSMC appears to be an undervalued opportunity anchored amid a sea of frothy valuations. Should you invest $1,000 in Taiwan Semiconductor Manufacturing right now? Before you buy stock in Taiwan Semiconductor Manufacturing, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the for investors to buy now… and Taiwan Semiconductor Manufacturing wasn't one of them. The 10 stocks that made the cut could produce monster returns in the coming years. Consider when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you'd have $663,630!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you'd have $1,115,695!* Now, it's worth noting Stock Advisor's total average return is 1,071% — a market-crushing outperformance compared to 185% for the S&P 500. Don't miss out on the latest top 10 list, available when you join Stock Advisor. See the 10 stocks » *Stock Advisor returns as of August 13, 2025 Adam Spatacco has positions in Alphabet, Amazon, Microsoft, and Nvidia. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Amazon, Microsoft, Nvidia, and Taiwan Semiconductor Manufacturing. The Motley Fool recommends Broadcom and recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy. This Artificial Intelligence (AI) Stock Is Dirt Cheap Compared to Its Growth was originally published by The Motley Fool
Yahoo
40 minutes ago
- Yahoo
The Great Shrinking of corporate America
In June, Amazon CEO Andy Jassy had a blunt message for his 350,000 corporate employees: There were going to be fewer of them in the near future, thanks to the "efficiency gains" he expected from AI. The proclamation generated big headlines and an uproar from staff. But it struck me as merely honest. He was acknowledging something that pretty much every CEO who sits atop a large white-collar workforce is quietly hoping to achieve sooner or later. After all, Jassy hasn't been the only executive to hint at a future of lower headcount. The head of JPMorgan's consumer and community business predicted in May that AI will reduce the number of employees in its operations division by 10%. That same month, the CEO of Klarna said that the company's investments in AI has already driven the company's headcount to shrink by 40%. And the CEO of Ford — a company that employs tens of thousands of white-collar professionals — declared that AI will wipe out "literally half" of all white-collar jobs. Meanwhile, Kian Katanforoosh, the CEO and founder of the software startup Workera, tells me that he never wants to have much more than the 80 or so employees he has today, no matter how successful his business ends up becoming. "I truly believe we can go super super far without growing more," he says. "I'm an engineer. I don't want to have to manage so many people if I don't need to." It's not like CEOs ever enjoyed shelling out for the salaries or navigating the personnel headaches that come with the sprawling bureaucracies they employ. But for more than a century, armies of office workers were a necessary cost of doing business. To grow from tiny upstarts into titans of industry, companies needed an ever-multiplying number of HR reps, accountants, marketers, engineers, analysts, and project managers. In recent months, that 100-year-trend is starting to come undone. Everywhere you look, AI appears to be helping leaner teams take on work that used to require more people. And executives are talking about their large workforces — once their greatest competitive advantage — as if they're an unfortunate holdover from a bygone, bloated era. If today's corporate giants shrink their ranks, and if tomorrow's giants never need to bulk up in the first place, we may well be witnessing the end of a defining feature of corporate America: the mega-employer. That could give rise to a whole new generation of nimble companies that innovate faster — but also leave workers navigating a world of diminished career paths and fewer jobs. Before the Industrial Revolution, most Americans worked for themselves as farmers or craftsmen. And those who didn't worked for very small operations — say, a few journeymen training under a master shoemaker. The resulting economy was a patchwork of all these tiny businesses. That started to change with the advent of capital-intensive industries like textile manufacturing, which required organizing larger groups of people under a single employer. Then came railroads in the late 19th century. With projects that took many years to realize and stretched over thousands of miles, vast numbers of workers needed to be on the same page. "If you mess it up, there's a big explosion," says Louis Hyman, a political economy historian at Johns Hopkins. "You needed to really coordinate your mechanisms and make sure that people are doing things exactly the same way." As mass production developed, Hyman says, many of the most consequential innovations during this time weren't so much technical breakthroughs: They were social inventions to coordinate the labor of all the people it took to get the most out of the new machines. The assembly line broke down complicated work into simple, repeatable, standardized tasks; scientific management emphasized the importance of monitoring, measuring, and optimizing everyone's performance; and the M-form corporate structure created a blueprint to manage sprawling bureaucracies through a clear chain of command. In the 1930s, about a tenth of the labor force worked for businesses that employed at least 10,000 people. By the end of World War II, that share had surged to about a third. By the 1970s, some of that bigger-is-better ideology started to change. A new management philosophy set in, normalizing layoffs that took aim at bloat. And as robots automated many blue-collar jobs, IBM mainframes and word processors eliminated a whole set of white-collar clerical roles as well. Still, there was plenty of work that technology couldn't automate, which meant that companies needed large teams of college-educated professionals to keep them going. Even the most tech-forward companies saw their people — especially their coders — as mission-critical to their success. "Hiring great people — especially engineers — is one of the biggest challenges that any technology company has," Mark Zuckerberg lamented in 2013. "Our country doesn't produce the volume of engineers that the companies would want to hire." Tech giants often hired more than they needed to make sure they had a steady supply of talent, and to attract and retain the best of the best, they treated their employees like gods. If you were to pinpoint one moment the gods turned mortal, it would probably be November 9, 2022 — the day Meta laid off more than 11,000 employees. From there, virtually every tech company followed suit, with employers across other industries close behind. At first, the cuts were chalked up to overhiring in the pandemic. But two and a half years later, the layoffs haven't stopped and hiring is still down. More and more, AI appears to be driving those austerity measures. In an industry that once hoarded talent like gold, the shift is striking. CEOs no longer seem to view the bulk of their workforce as indispensable, and they say as much: A common refrain among tech leaders from Mark Zuckerberg to Elon Musk to Dara Khosrowshahi now is some version of "If you don't like it here, you should leave." Companies like Microsoft, Meta, Google, and Salesforce had reliably increased their headcounts year after year. Now, according to the workforce analytics provider Live Data, all of them employ fewer people than they did at their 2022 peak. J. Scott Hamilton, Live Data's CEO, says this is probably just the beginning. To gauge how much deeper the cuts could go, his team recently analyzed the detailed responsibilities of most roles at Microsoft to estimate the share of tasks that could, in theory, be done by AI. Their conclusion: If Microsoft were to offload all of those automatable tasks to AI, it would eliminate 36% of the work currently done by employees. That would mean the company could lay off some 80,000 employees. On the one hand, that's an aggressive scenario: Companies are rarely able to overhaul their workflows to take full advantage of a new automation technology's capabilities. If they do, that transition takes a very long time. And besides, some work is simply too high-stakes to entrust to error-prone AI — even if it's technically possible. On the other, the estimate may prove conservative: Live Data's predictions assume that AI will remain at 2025-level capabilities. Given how much better the leading large language models have become over the last two years, the best tools will almost certainly be able to handle more than what they can today. "The optimists are saying that the good companies will simply redeploy the assets elsewhere now that they can be more efficient," Hamilton says. "But I think an equal argument can be made that they'll just say, 'We're going to do the same amount with fewer people.'" If that sounds like a far-off hypothetical future, consider what's already happening today at startups. OpenAI CEO Sam Altman says he's making bets with his friends on when we'll get the first "one-person billion-dollar company." And Arthur Kaneko, a general partner at Coreline Ventures, tells me he's noticed that early-stage founders are raising their initial rounds of funding with fewer employees than they would have had in the past — among the AI-fluent founders, perhaps with less than half. "The way companies are being built is just fundamentally changing right now because of AI," Kaneko says. "Through the use of AI coding, AI marketing, AI sales, people are able to do a lot more work with way fewer people." And he thinks these startups will stay lean as they scale into successful businesses. "They just won't hire the people that Meta and Microsoft had to hire to get to where they are," he says. "I do think per-company headcount will permanently be depressed in startups." There are reasons to be hopeful about a new era of smaller employers. If AI makes it cheaper and easier to launch companies, we'll probably see more of them — and that would be great for the long-term health of the economy in all kinds of ways. New businesses tend to employ people with less experience and fewer credentials who get passed up by the bigger companies. They're more willing to try new things, which drives innovation. And they create more competition for the established giants, which is good for consumers. Smaller companies may also be good for the workers inside them. There's a lot that people hate about working at big organizations: the constant turf wars, the endless layers of approval, the meetings before the meetings, the sense that you're just one tiny inconsequential cog in a giant machine. Smaller bureaucracies would minimize that, which is one reason why people often feel more motivated in leaner workplaces. According to Gallup, employees at small companies report the highest engagement, with scores dropping below the national average once organizations hit 500 employees. On the same stage where Altman made his one-person unicorn prediction, Reddit co-founder Alexis Ohanian raved about the benefits of this possibility. "CEOs and founders are going to be so excited to get up and go to work with much smaller, much more performant, much more culturally strong teams," he said. But a world of shrunken employers could also rob workers of something essential: the long-term career paths that big companies used to offer. With so many roles under one roof, big companies made it possible for workers to try new things, move up, and build careers. Smaller firms don't offer the same range of opportunities, which means people will likely need to switch companies a lot more in the future. Smaller firms are also less likely to invest in on-the-job training — a shift that would hit early-career professionals hard, just as their roles face the greatest risk from AI. The big question is what this all means for college-educated workers. If enough startups emerge, they might create new jobs to offset the ones disappearing from big companies. But that would require an unprecedented boom in entrepreneurship — one enormous enough to make up for the retrenchment of the giants. In 2022, 29% of the American workforce worked for an organization that employed at least 10,000 people. Meanwhile, the country's education system is churning out ever more college grads, who studied hard with the expectation of a stable future in white-collar work. If big companies hire less, and small companies also hire less, where will they all go? The usual reassurance is that AI, like every disruptive technology before it, will eventually create more jobs than it destroys. That glosses over an important detail, according to Carl Benedikt Frey, an economist at Oxford. In the early stages of the Industrial Revolution, most innovations simply made existing work faster and cheaper — like the loom, which automated the work of skilled weavers but still produced more or less the same fabric. That made a handful of industrialists very rich, but for the average worker, wages barely budged for the first 80 or so years of industrialization. It was only later — with inventions like electricity and the automobile that gave rise to entirely novel industries — that economic growth surged and better, high-paying jobs emerged. Had that second wave never arrived, we'd remember the Industrial Revolution very differently. "Most productivity gains over the long run," Frey says, "come from doing new and previously inconceivable things." Right now, corporate America seems stuck in that first phase. So many executives are laser-focused on using AI to do the same work with fewer people, rather than applying it to problems we couldn't solve before — the kind of breakthroughs that would open up new lines of business and generate more demand for labor, not less. "A real risk is that we're getting leaner organizations, but they're not really creating that much new," Frey says. "That would be a bleak future, and I do worry we're moving in that direction." Correction: August 11, 2025 — A previous version of this story incorrectly stated Louis Hyman teaches at Cornell, his former employer. He now teaches at Johns Hopkins. Aki Ito is a chief correspondent at Business Insider. Read the original article on Business Insider 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
