Author: admin

Quantum computers may in the future break today’s widely used encryption. This paper provides a framework to support the financial system in the transition to quantum-safe cryptographic infrastructures. It emphasises the need to start the transition today – with broad awareness and cryptographic inventory as critical foundations. While post-quantum cryptography offers a viable near-term solution, implementation challenges – including performance trade-offs and system integration – require coordinated planning. We caution against regarding this change as simple algorithm replacement. Ensuring the continued security and resilience of the global financial system may involve cryptographic agility, defence in depth, hybrid models and phased migration. Quantum key distribution may hold long-term potential, but several national security agencies note that it still faces infrastructure challenges that limit its immediate applicability.

JEL classification: C19, C63, C8, M15, G1, G17

Keywords: central banking, quantum computing, quantum-safe cryptography, quantum-readiness, cryptographic agility, financial stability, financial system, cyber security

Wall Street really needs AI to live up to the hype.

A lot has been said about the emerging technology’s world-changing potential: Its ability to create stunningly realistic images and videos, ace the LSAT and the MCAT, and complete rote research tasks. You could argue it’s ready to augment — or even replace — entry-level jobs.

These features have investors up and down the Street very excited. Staunch supporters like Fundstrat’s Tom Lee and Wedbush’s Dan Ives say AI could revolutionize the human experience. Research desks from big banks like Goldman Sachs and Bank of America have given subtler nods to the prospect of AI as a productivity and profit booster, which could provide an undercurrent to stock market success over the next several years.

In fact, analysts are counting on the AI mania to fuel the market even as the White House’s chaotic trade policy eats into corporate America’s profit potential. Earnings for S&P 500 companies are projected to grow 8% this year, a fairly average showing for an anything-but-average year. What is notable is just how much of that growth relies on the tech sector: Silicon Valley companies are expected to boost their earnings by 21% — the highest growth of any sector. By contrast, profits for retailers are forecast to grow a measly 2.5%.

Within the tech sector, semiconductor companies — one of the most globally exposed industries on the stock market — are expected to supercharge profit this year, with a projected climb of 49%. This enthusiasm is a signal Wall Street is betting that demand for AI’s use cases will supersede tariff turmoil or job market wobbles.

AI’s growth has been incredible, and its adoption has been strong enough to leave its fingerprints on economic data sets like business investment and manufacturing spending. Yet no matter how rabid the world is about AI’s possibilities, the amount that investors are relying on the tech to fuel the market’s gains — especially in the face of rising economic uncertainty — feels short-sighted. Tech stocks helped the market recover from its April malaise, yet earnings expectations and economic momentum are even weaker than they were at the lowest point of the sell-off. This combination leaves the stock market in a precarious spot: Either AI needs to live up to the hype, or investors could be looking at a gnarly second half of the year.

One way to tell the story of human history is through our technology — the lightbulb, the calculator, the tractor, the computer all stand as markers to our societal progress and have helped drive the level of efficiency and productivity we enjoy.

Tech has perhaps played an equally prominent role in our investment portfolios. The Magnificent Seven stocks — Apple, Amazon, Alphabet, Meta, Microsoft, Tesla, and Nvidia — are collectively worth $18 trillion, or about 33% of the S&P 500’s total market value. Together, their stock prices have increased 330% over the past five years, compared with a 100% rise in the S&P 500.

It makes sense. Big Tech’s products have become deeply integrated into our daily lives, and that level of ubiquity has also captured Wall Street’s attention. Venture capital fundraising reached a record in the first quarter amid a huge appetite for AI investment, and S&P 500 companies mentioned AI more than tariffs on second-quarter conference calls.

The $65 trillion US stock market may be particularly gripped by Big Tech’s ups and downs these days, but it hasn’t always been this way. Tech has averaged about 20% of the S&P 500’s market value over the past decade, including 13% in the five years before COVID. The dominance is not set in stone, and while the wider market’s fortunes are tied to tech now, that may not always be the case.

While the stock market may seem like one big proxy for the tech sector’s explosive growth right now, there is one deeper connection that should draw investors’ attention. Over time, the S&P 500 has been attached at the hip to the fate of the broader US economy. Eight of the past 12 market crashes — S&P drops of 20% or more — overlapped with recessions. No matter how high-flying an industry is, recessions tend to pull stock prices and business hopes back to Earth. The internet revolutionized the world in the late 1990s, and the explosion in social media dominated the 2010s, but the information sector has shed employees and seen share prices fall in the past three recessions.

Given that setup, we’ve set the stage for a portfolio smackdown of the ages. Economists are worried a recession is coming, yet investors are surprisingly upbeat about AI’s prospects — so upbeat that they’ve bid S&P 500 tech stocks to nearly a record high. Sell-side analysts who evaluate company-level trends are similarly optimistic. But in the real economy, layoffs are growing, and hiring has ground to a halt. The sharply diverging views between economists and stock analysts mean someone has to be wrong. The freight train that is AI adoption — a three-year story of rapid innovation and progress — could collide with a massive wall from historic tariffs, high interest rates, and low consumer confidence.

What’s particularly rich about this is that tech companies are the most exposed sector to global tariffs. They gather the highest percentage of revenue internationally, plus they have the most suppliers and factories outside US borders. In fact, semiconductor companies — the firms providing chips for AI technology — are expected to hit that aforementioned 49% earnings growth despite generating 67% of their revenue abroad and sourcing 70% of their supplies from overseas.

Some analysts believe that if AI hopes can keep the stock market chugging along, maybe it can do the same to the economy. After all, companies invested an inflation-adjusted $2.2 trillion on computers and other processing equipment last quarter, about one-seventh of the $16 trillion Americans spent on goods and services. Investing more in AI does ultimately help boost the economy, but that $2 trillion is peanuts compared with the real engine of the US economy. Americans’ spending accounts for about 70% of GDP — by far the biggest driver of output — and spending has dropped in each of the past nine recessions. If tariffs intimidate consumers and lead to layoffs that decimate American incomes, then the economy is probably bound for a crisis — whether the robots pan out or not. And based on history, an economic crisis could topple the stock market.

The math shows us that AI isn’t much of a match for some effects of tariffs and may not logistically be enough to save the economy from ruin. Your portfolio’s outcome may be a different story, though.

This is when I have to introduce you to one of the most frustrating adages of investing: The stock market is not the economy.

The economy is the value that we create — the hard assets, the cash spent, the paychecks we get. Stocks are an expression of that value, but they use the present reality to project future expectations. AI’s impact on the economy may not bear out through numbers. But in your portfolio, AI’s influence depends on how willing we are to collectively dream up better days ahead in terms of what AI is capable of and how much money AI-dominant firms will amass in the years to come. Dreaming is already a big part of the AI trade. S&P 500 tech companies’ estimated earnings grew about 50% in 2023 and 2024, yet their share prices jumped 112%.

Nothing is cut-and-dried when it comes to the stock market. It is the ultimate tangled web of logic, psychology, and mixed incentives. The stock market’s future depends on investors’ ability to dream, and people are willing to dream when they feel confident in the present moment.

The problem is, investors are awfully confident about tech stocks right now. S&P 500 tech companies made up about 23% of total index profits in the first quarter, yet their shares account for 32% of the S&P 500’s value. To close that gap, tech profits would have to grow 40%, or tech stocks would have to drop 29% from their end-of-June levels.

Stocks can thrive when expectations are higher than reality, but in these conditions, they require reasons to stay hopeful. The problem arises when investors aren’t willing to dream. When they’re too focused on present issues to give compelling stories the benefit of the doubt. Or in big market drops, crushed by financial strain.

Then, the numbers matter. People claw for any concrete evidence of AI’s value. They demand proof of profits, even though companies are spending money on a pivot to the next big thing. Stock prices adjust, and if you hold a swath of US stocks or index funds, your money is probably heavily exposed to this reality check.

This is what happened in 2000. Investors were willing to dream about this brave new technology called the World Wide Web until interest rates climbed too high and the reality of how much computing was needed for Y2K was found to be way overblown. Suddenly, the dream died, and tech stock prices came back down to reality. These days, we all know that dream wasn’t completely off base. Yet share prices took an 80% crash before the promise of the new tech came to fruition.

This is what I worry about the most in the clash between AI and the economy. We’re somewhere between AI saving the world and being an overhyped bust of a technology that can be ripped off by another country. I’m not foolish enough to call this a bubble, and I think AI will eventually deliver benefits for our economy.

We’re not there yet, though, even though investors like to think so. It takes years for big technological trends to take hold, and productivity usually shines through when workers feel empowered and companies feel comfortable expanding. That’s far from the case right now — business confidence is in the dumps, so we’re in the opposite scenario.

When the economy is getting weaker, it’s best to grasp onto what’s real in your portfolio. And there’s a striking gap between AI and reality.

Callie Cox is the chief market strategist at Ritholtz Wealth Management and the author of OptimistiCallie, a newsletter of Wall Street-quality research for everyday investors. You can view Ritholtz’s disclosures here.

He’s not an executive, a company spokesperson, or a world-class researcher. But he might be Google’s secret weapon in winning the AI race.

If you’re an AI developer, you’ve likely heard of Logan Kilpatrick. As Google’s head of developer relations, Kilpatrick, 27, runs AI Studio, the company’s AI developer software program.

He has also become Google’s delegate for speaking to the AI community and — intentionally or not — a one-man marketing machine for the company’s AI products. He’s a prolific poster on X, where he’ll sometimes hype Google’s latest Gemini releases or tease something new on the horizon.

Above all, he is one of the people tasked with translating Google’s AI breakthroughs to the global developer community. It’s a crucial job at a time when the search giant needs to not just convince developers to use its products, but capture a new generation of builders entering the fray as AI makes it easier for anyone to make software.

“If you want AI to have the level of impact on humanity that I think it could have, you need to be able to provide a platform for developers in order to go and do this stuff,” he told Business Insider in an interview. “The reality is there’s a thousand and one things that Google is never going to build, and doesn’t make sense for us to build, that developers want to build.”

Company insiders say Google has recognized Kilpatrick’s strength and given him more responsibilities and visibility. He could be seen onstage at this year’s Google I/O conference and even had a fireside chat with Google cofounder Sergey Brin.

“People really crave legitimacy, authenticity, and competency, and Logan combines all three,” Asara Near, a startup founder who has occasionally contacted Kilpatrick with development questions, told BI.

LoganGPT

In 2022, OpenAI was preparing to launch ChatGPT and fire the starting gun on one of history’s most profound technological shifts. Kilpatrick, who has a technical background and worked at Apple and NASA, saw an online job ad for OpenAI and was soon facing a tricky decision: to work at what was then Sam Altman’s little-known startup, or take a gig at IBM.

He decided that OpenAI was worth a shot — and within a few months, found himself at the center of the biggest tech launch since the debut of the iPhone in 2007.

“The OpenAI experience was a startup experience for about six months and then it became basically a hyperscaler,” he told BI. It was chaotic, but it helped Kilpatrick learn how to build an ecosystem and cut his teeth as the developers’ go-to guy. There, developers nicknamed him “LoganGPT.”

When he left OpenAI in 2024 for Google, developers and peers made clear it was a huge loss for the ChatGPT maker, and a big win for Google in the AI talent transfer window. AI Studio was then still a project inside Google’s Labs division, and Kilpatrick and his team were tasked with migrating it into a fully-fledged product inside Google’s Cloud unit. It was again like going from zero to one: AI Studio was pre-revenue with no customers, but with a long tail of developers ready to jump on board.

“It has felt oddly almost like the same exact experience I’ve lived through at two different companies and two different cultures,” he told BI.

In May this year, Kilpatrick was promoted, and his team running AI Studio was moved from the Cloud unit to Google DeepMind, bringing them closer to the researchers working on the underlying models and the employees working on its Gemini chatbot.

“He’s kind of all over the place, and that’s his superpower,” said one senior employee who requested anonymity because they were not permitted to speak to the media. They said that Google has put Kilpatrick in charge of more products as leaders have recognized his ability to engage so effectively with the developer community. “Logan is 90% of Google’s marketing,” they said.

Helping Google win

On paper, Google is an AI winner. The reality is more complicated.

Its latest Gemini 2.0 Pro model ranks top of multiple leaderboards across a range of testing areas, but this hasn’t always been reflected in the number of users. Google’s CEO, Sundar Pichai, said in May that the company’s Gemini app has more than 400 million monthly active users. That’s well behind the 500 million weekly active users for ChatGPT, according to figures shared by Altman in April.

“DeepMind doesn’t get nearly as much credit and attention as they deserve, and that’s because comms is vastly underperforming capabilities,” communications executive Lulu Meservey posted on X in May. Responding to another person, she wrote: “Logan is like 90% of their comms.”

Some of the struggle, insiders say, is due to Google owning multiple products that aren’t always clearly distinct. Developers can build using Vertex in Google Cloud or AI Studio. Meanwhile Google has a consumer-facing app simply called Gemini. The same models aren’t necessarily always available across all three places at the same time, which can get confusing for users and developers.

There’s also the problem of being a quarter-century-old tech behemoth with more nimble startups nipping at its heels. “OpenAI can put all their messaging arrows behind one thing, while Google has messaging arrows behind 10,000 things,” former Google product manager Rajat Paharia told BI.

Kilpatrick recognizes that Google has work to do. “I think Google on a net basis is doing so much in the world right now, and AI is around everything that we’re doing, and I think a lot of narrative doesn’t capture innovation is happening,” he said.

A big part of Kilpatrick’s job is trying to cement that narrative among the global developer base. At OpenAI, Sam Altman’s Jobsian showmanship has made him a highly effective salesman both for his company’s products and his vision for the future of this technology. Or, as Paharia described Altman to BI, a “showman with rizz.”

Google may have found its equivalent in Kilpatrick. He told BI that he often posts on X because it has become something of a town square for AI developers and enthusiasts, all champing at the bit for the latest crumb of news. It’s a community filled with hype, AI “vagueposting”, and steeped deeply in lore (what did Ilya see?).

On a day that OpenAI’s latest release sucking is grabbing everyone’s attention, Kilpatrick may log on and post a single word — “Gemini” — just to rev the hype engine a little.

Kilpatrick often has “a thousand” emails from developers that need responding to, he told BI. “I spend probably as much time as I physically can responding to stuff these days,” he said. And that’s between the numerous product meetings (he had 22 meetings scheduled on the day we spoke in early July, 23 the day before). He once posted on X: “I am online 7 days a week, ~8+ hours a day. If you need something as you build with Gemini, please ping me!”

Developers say they like that Kilpatrick takes the time to engage and listen to their feedback. “The few times I’ve emailed him to get help with something, they near-instantly responded and helped resolve the issue,” said Near, the startup founder. “This is the opposite of my experience through normal support channels.”

Andrew Curran, an AI commentator who frequently posts to X, wrote last month that Kilpatrick had been “an incredible hire” for Google. “To a lot of people he is now the face of Gemini, I bet most people don’t even remember his OAI days,” he wrote.

Kilpatrick told BI that because he is a developer himself, he finds it easy to understand the core target user. He said this has helped in building out Google’s AI Studio, and that engaging with developers comes naturally. “It’s just the obvious thing to do if you want to build a product for developers, is like, go talk to your users,” he said.

But the definition of developer is changing with approaches like vibe coding, which lets non-technical people create software by describing what they’d like to an AI tool.

“What it means to be a developer right now looks a little different than it did two years ago or three years ago, and I think it’s going to look fundamentally different in 10 years,” said Kilpatrick. He believes the developer group will “massively expand” in the next five years. His job at Google is to make the next generation believe Google is where they should be developing, but that job is also evolving in this new era of artificial intelligence.

“Our mandate is actually AI builders, already encompassing this group of people who maybe don’t identify as developers and don’t write code, but they build software using AI, and I think that’s going to accelerate in the next few years,” he said.

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