Topic Tags: Artificial Intelligence

The Seller Becomes the Buyer

Most have traditionally viewed a successful exit for a venture-backed start-up as either an IPO or an acquisition by a larger strategic or public company. That long-standing dynamic is gradually shifting, as start-ups are now more active than ever as acquirers. Indeed, what was once a buyer landscape dominated by strategics and public corporations now increasingly includes venture-backed firms. According to PitchBook-NVCA data, VC-backed buyers accounted for more than 38% of total U.S. venture M&A activity last year, up from roughly 20% a decade ago, with 2025 marking seven consecutive years of increasing participation. Specifically, more than 387 start-ups were acquired by venture-backed companies last year, compared with 177 in 2015. Although overall exit volumes remain below 2021 peak levels, the steady rise in startup-led acquisitions reflects a structural shift toward internal consolidation within the venture ecosystem.

The drivers behind this shift are largely pragmatic, as capital remains available but far more selective. Growth equity investors are increasingly concentrated within perceived category leaders, while companies that fall slightly below that threshold face a more challenging fundraising environment. For scaling start-ups that have survived earlier rounds of capital selection, acquisitions can serve as an efficient strategic accelerant. Rather than depleting cash reserves to build adjacent features, expand geographically, or acquire customers organically, management teams can accelerate these objectives through M&A, adding revenue, product capabilities, or talent in a single transaction. At the same time, the bar for IPO readiness has risen materially in the last five years, as public investors are increasingly prioritizing profitability, operating leverage, and durable revenue growth. For venture-backed companies aiming to meet these standards, combining with a competitor or complementary platform can create scale and margin expansion more quickly than standalone execution. In some cases, consolidation represents the most rational path forward in a more disciplined capital cycle. This trend is visible at the upper end of the market as well. For instance, OpenAI completed five acquisitions across hardware design, experimentation tooling, fintech AI capabilities, and model infrastructure in 2025 alone. The fact that one of the world’s most valuable private companies is actively using M&A as a growth lever reinforces the idea that an acquisition is no longer solely a means of exit but increasingly a tool for expansion.

While it remains too early to declare a permanent transformation in venture markets, it is clear start-up-led consolidation is becoming more common and strategically meaningful. As companies remain private for longer and develop greater operational scale, their roles as acquirers may continue to expand.

Precious Metals Lose Their Luster… Perhaps

Precious metals have been going on a magnificent run in recent years. Specifically, gold moved from $1,898/ounce at the end of 2020 to $5,375/ounce on January 29 of this year, which represents a gain of 181%. During that same time, silver exhibited a more volatile but highly correlated return pattern, moving from $26/ounce to $116/ounce for a gain of 338%. Then came Friday, January 30. On that day, gold dropped more than 12%, its biggest intraday decline since the early 1980s. Silver plunged by a staggering 36%, a record intraday decline for the metal. The fall continued in February, with gold and silver falling to $4,661/ounce and $79/ounce, respectively. Markets have bounced back somewhat in recent days, with gold climbing by roughly 6% and 3% on Tuesday and Wednesday of last week, respectively. Silver advanced on those days as well. Despite this recent pop, many investors are asking the following question given the sharp decline in gold and silver: Have precious metals lost their luster?

To answer this question, it is worthwhile to first outline the reasons for the run-up in gold and silver over the last several years. A primary factor driving strong precious metal performance is global inflation and geopolitical instability (e.g., tensions between the U.S., Russia, China, and the Middle East) that has pushed investors to seek safety in more traditional stores of value. Tariffs and trade-related conflicts have exacerbated this flight to perceived safety. Additionally, developed economies continue to run significant budget shortfalls, leading investors to gold over bonds as governments continue to issue debt to fund deficits. Individual investors are not the only ones that are adding to their gold reserves, as central banks around the world have been purchasing record amounts of gold in recent years as part of a push toward tangible asset ownership. Finally, there have been tailwinds specific to silver, including a structural deficit, thinner trading markets, and its usage in AI infrastructure, data centers, electric vehicles, and solar panels.

After the rally came the fall on January 30, when the Trump administration tapped Kevin Warsh to lead the Federal Reserve. Traders viewed Warsh as the toughest inflation fighter among the finalists for the position, and his nomination increased expectations of U.S. dollar strengthening and weaker precious metals in dollar terms. The slide in precious metals may have been exacerbated by a gamma squeeze, in which dealers must sell positions as prices fall to maintain balanced portfolios.
Fast markets make commentary quickly obsolete, and it is possible that metals markets will exhibit additional volatility in the weeks ahead. This volatility, as well as potential storage costs and the speculative nature of the space, are drawbacks of precious metals investing, and investors should treat commodities like gold and silver with caution given these risks. Time will tell if gold and silver have indeed lost their luster.

Seventy-Five Horses and Two Pieces of Plastic

Anyone who has gone snowmobiling knows it can be simultaneously exhilarating and terrifying. Throttling across snow and through a forest powered by a 75-horsepower engine with two plastic skis to steer makes it hard to feel like one has complete control; 30 mph in the open air feels more like 100!

Nonetheless, operating a snowmobile is pretty straightforward: The throttle is a right-thumb button, the brake is a left-hand squeeze lever. Beyond those two controls, it’s up to the driver to effectively navigate the trail, with the critical concession that the terrain is out of anyone’s complete control. Which brings me to our 2026 market outlook.

The “throttles” for portfolios are the usual constituents: equities, below investment grade credit, and private markets. The “brakes” are investment grade fixed income, particularly Treasuries which can slow a portfolio’s losses if the market tumbles. The terrain is naturally the actual path that each of these asset classes will follow in 2026. Since 2022 the equity market ride has been mostly exhilarating, save for some of the terrifying moments like the market dip after Liberation Day. But that’s in the rearview mirror, and the focus is what is around the bend. Will the thrill continue, or should we ease up on the throttle?

Concentrating on Market Concentration

Last week, Alphabet joined NVIDIA, Microsoft and Apple as the only companies to ever reach a market capitalization of $4 trillion. The growth of these and other U.S. mega-cap technology companies has completely changed the composition of indices that measure the domestic equity market. Indeed, the weight of the top 10 constituents of the MSCI United States Index (which is comprised of large- and mid-cap stocks) sat at roughly 23% just three years ago. At the end of last year, however, that figure sat closer to 38%. As can be seen above, this concentration has resulted in a handful of stocks driving a significant share of overall index returns in recent periods. Interestingly, the theme of market concentration is not exclusive to domestic indices. For instance, companies in China, Taiwan, and South Korea have helped provide the materials required for the artificial intelligence boom, and the growth of these businesses has led to higher levels of concentration for the MSCI Emerging Markets index. The top 10 constituents now represent slightly less than one-third of this index, and TSMC, the largest producer of semiconductors in the world, notably comprises roughly 12% of the benchmark. Similar to trends within domestic markets, these top constituents had an outsized impact on the return of the MSCI Emerging Markets Index in 2025.

Interestingly, the MSCI EAFE Index, which is comprised of non-U.S. developed markets large- and mid-cap stocks, has not followed these same trends, with the weight of its top 10 constituents actually decreasing in recent years. While its largest holding is ASML, a supplier for the semiconductor industry, this benchmark is not nearly as heavily tilted towards the AI boom as domestic and emerging markets indices. For this reason, developed international markets could be a stronger source of diversification for investors moving forward.

Brains Over Brawn?

The development of artificial intelligence is advancing along two largely distinct paths. The first centers on generative AI powered by large language models, with the long-term objective of creating systems that can reason across domains at levels superior to those of human beings. The second focuses on embodied intelligence (i.e., robotics). In this space, the objective is not abstract reasoning but rather the deployment of capable machines that can operate effectively in the physical world. Over the last five years, capital and attention have overwhelmingly gravitated toward companies involved in generative AI, with the Bloomberg Artificial Intelligence Index up a staggering 276% in that time. Robotics, by comparison, has been widely viewed as a longer-dated theme, with the Bloomberg Robotics Index up only 77% over that same period (even less than the S&P 500 Index return of 134%). These dynamics can be observed in this week’s chart.

Going forward, there are reasons to believe that this performance trend may shift in the years ahead. For instance, human-level general intelligence could be far more distant than markets currently assume, and language models may not prove sufficient to reach it. At the same time, practical robots (e.g., warehouse automation, humanoid assistants, etc.) appear closer to commercial reality than previously believed, particularly in aging societies facing persistent labor shortages. One possible accelerant for robotics companies in the years ahead is the use of advanced simulation. By training in virtual environments, robots can acquire motor skills and coordination far more rapidly than through physical trial and error alone, potentially pulling forward adoption timelines relative to current investor expectations. Importantly, transformative impact does not require robots to achieve artificial general intelligence but rather functional capability (i.e., the ability to move objects, operate safely, and sustain useful work with sufficient battery life). Commercial momentum in robotics is already building. In 2024, for example, Agility Robotics opened a manufacturing facility in Oregon with capacity to produce up to 10,000 humanoid units annually, and Amazon has now begun testing Agility’s robots in its warehouses. Additionally, companies like Tesla are showcasing humanoid prototypes performing increasingly fluid physical tasks, and BYD has signaled interest in future household robotics. While price points remain prohibitive for mass adoption today, several structural forces are converging to improve the economics of robotics. Manufacturing costs are declining as scaling drives down prices for components like sensors and actuators, while improvements in AI models are enhancing robotic perception and control. Taken in tandem with the fact that generative AI leaders are currently investing heavily in costly, power-hungry data centers, it is fair to say that a once slower-moving, less glamorous segment of the AI ecosystem may now benefit from relative capital efficiency.

Despite these developments, markets continue to assign a significant valuation premium to generative AI over robotics, which can also be observed in the chart above. Factor analysis helps explain part of the gap, as AI-heavy indexes skew toward momentum and growth while robotics-oriented benchmarks exhibit greater exposure to value, quality, and, in some cases, even dividend income. Further, the generative AI complex is dominated by large technology platforms including Alphabet, Microsoft, and NVIDIA, whereas robotics companies tend to be more industrial in nature (e.g., automation specialists, automakers, and emerging consumer-robotics firms). This valuation disconnect suggests that investors may be overemphasizing long-term breakthroughs in cognition while underappreciating near-term progress in physical automation, especially as physical robots transition from research environments into factories, homes, and hospitals. Indeed, while much of today’s excitement centers on artificial brains, it may ultimately be robotic brawn that drives the next leg of growth within the technology sector.

Big “Issues” for Big Tech

While technology-oriented firms have made their presence known in equity markets for several years, these companies have made waves in the fixed income space recently as well. Companies such as Alphabet, Meta, and Oracle, which in the past have funded initiatives via balance sheet cash, have increasingly turned to the bond market to finance the buildout of AI-related infrastructure. Specifically, a total of nearly $240 billion worth of investment-grade bonds have been sold by technology giants on a year-to-date basis through the end of November. Some notable deals in 2025 include Meta’s $30 billion bond sale, the largest in the U.S. high-grade market this year, Oracle’s $18 billion issuance in September, and Alphabet’s deal that raised $17.5 billion in the U.S. and another €6.5 billion (roughly $7.5 billion) in Europe.

This surge in supply carries meaningful implications for the broader investment-grade corporate market, which is one of the most heavily traded areas of fixed income. For instance, the sheer volume of new issuance from technology companies can put upward pressure on corporate spreads as investors demand slightly higher yields (despite the strong balance sheets and generally low leverage of these firms). There is also the question of the potential return on AI-related spending (or lack thereof). Indeed, a recent MIT study found that around 95% of companies have yet to see any meaningful payoff from their generative AI efforts. At the same time, investors and creditors are growing more cautious, increasing their use of derivatives designed to pay out if specific technology firms fail to meet their debt obligations. That said, investment in AI-related infrastructure seems likely to continue at full speed in the years ahead, meaning technology firms may continue to tap the investment-grade market for financing.

Small Caps: Unprofitables Lead, Active Managers Lag, But Can it Last?

At the start of 2025, very few could have predicted the wild ride that awaited equity markets. After a volatile period that culminated on April 8, U.S. equities achieved several new all-time highs, with small-cap equities reaching a first all-time high since November 2021. Absolute returns have been substantial, as the Russell 2000 rose nearly 42% off the market bottom through October 31. Despite renewed volatility in November as expectations for another Federal Reserve rate cut fluctuated, small-cap equities have led large-cap equities since April 8. As is expected in the first six months of a bull market, low quality, including residual volatility, short interest, non-earners, and beta, propelled the small-cap market. Conversely, active managers favor high quality companies, typically characterized by high returns on equity, strong balance sheets, and low leverage. As a result, this factor backdrop is a known headwind for many active managers across the small-cap universe, and this bull market is no different.

The Asymmetry of Unemployment

A fundamental characteristic of U.S. labor markets is the pronounced asymmetry in unemployment dynamics, as joblessness rises anywhere from three to five times faster during recessions than it falls during recoveries. This “sawblade” pattern has important implications for economic forecasting, monetary policy, and investment portfolio positioning. Amid recessionary conditions in the early 1980s, unemployment surged from 7.0% to 10.8% in just 16 months (an average increase of more than 0.2% per month). The subsequent recovery took 54 months, with unemployment declining at a rate of less than 0.1% each month on average. The Global Financial Crisis of 2008 exemplifies this pattern even more dramatically, as unemployment jumped from 5.0% to 10.0% in 22 months and normalized over a period of more than six years, during which time millions of workers faced extended joblessness. Most striking was the COVID-19 pandemic of 2020, when unemployment exploded from 3.5% to 14.7% in just two months (the sharpest spike in modern American history). While the initial recovery was faster than historical norms due to unprecedented fiscal and monetary stimulus, the unemployment rate still took 33 months to return to pre-pandemic levels. This illustrates that even with extraordinary policy support, labor market normalization remains gradual. The pattern described above reflects fundamental labor market frictions. On one hand, companies can execute mass layoffs within weeks when facing existential threats or demand shocks. At the same time, hiring is usually carried out with caution, as firms slowly restaff as confidence improves, workers require time to locate appropriate positions, and many require retraining for structural shifts in demand. Indeed, this friction is not a policy bug but rather a feature of how the labor market functions.

Understanding unemployment asymmetry is critical for investors today as the Federal Reserve navigates an increasingly complex challenge related to its dual mandate of stable prices and maximum employment. Specifically, the Fed faces an unprecedented data vacuum due to the recent government shutdown, and traditional labor market indicators are sending mixed signals. For instance, payroll growth has moderated but remains positive, initial jobless claims are elevated but have not reached recessionary levels, and the unemployment rate has risen yet remains relatively low. Some have also linked the rise of artificial intelligence to recent hiring trends, though it remains unclear whether these trends represent a meaningful secular shift in labor demand. Complications are intensified by inflation that remains stubbornly above the Fed’s 2% target. In short, looser monetary policy could lead to even higher price levels, while restrictive policy could trigger higher unemployment if actual labor market conditions are worse than available data points suggest.

Going forward, the Fed will likely be forced to prioritize one side of its dual mandate over the other, as interest rate policy is too blunt an instrument to fine-tune both price and employment levels simultaneously. The current environment represents precisely the knife-edge scenario in which an understanding of asymmetric labor dynamics becomes essential for economic forecasting.

The Impact of Artificial Intelligence on Markets

Over the last several decades, artificial intelligence (“AI”) has evolved from a theoretical concept into a transformative force across a variety of industries. The 1940s saw the advent of the digital computer, which was followed years later by the first artificial neural network, a computational model inspired by the structure of the human brain that consists of algorithms that attempt to recognize relationships in data. In more recent years, researchers have developed “deep learning” systems (i.e., neural networks with many layers) capable of increasingly complex tasks including image recognition, reading comprehension, and predictive reasoning. Given the advances in the space, it should not come as a surprise that the use cases of artificial intelligence are now vast, with AI tools now implemented across fields including health care, retail, finance, and entertainment. Researchers and corporate executives are not the only ones to have noticed the remarkable potential of AI, however, as investors have flocked to the space in droves over the last several years.

This newsletter outlines the growth of AI as an investment theme, including performance, valuations, and earnings growth of AI-related companies and equities, other segments of the market that may stand to benefit from advances in AI, and potential risks for investors.

Back to Back!

This week’s chart details each calendar year return for the S&P 500 Index dating back to 1928, with consecutive 20%+ returns highlighted in orange. Despite a slight pullback over the last few weeks, the index posted a return of more than 20% in 2024, which represents only the fifth time in history that the benchmark has recorded such a figure in consecutive years (note that the five straight years of 20%+ returns in the 1990s are counted as one instance). As investors look ahead to 2025 and beyond, many are asking the following question: How have markets performed after such strong periods?

In the years following the first three of these instances (1937, 1956, and 1984), the S&P 500 Index notched a significantly lower return, with an average of -1.1%. Interestingly, each of these years was marked by either tighter monetary policy, inflation, decreased industrial production, higher unemployment, or some combination of these trends. As mentioned above, the late 1990s saw a staggering five consecutive years of 20%+ returns for the S&P 500 Index, fueled by a boom in investor interest in e-commerce, software, and telecommunications companies. The so-called “Dot-Com Bubble” led to widespread speculation related to unprofitable companies and a rapid expansion in market valuations, and the bursting of this bubble caused the S&P 500 Index to decline sharply in the first three years of the new millennium.

In the last two years, performance of the S&P 500 index has been largely driven by investor interest in artificial intelligence and the Information Technology sector. The Magnificent Seven stocks (Apple, Microsoft, Amazon, Alphabet, NVIDIA, Meta, and Tesla) have led the charge, accounting for over 50% of the total return for the benchmark since the beginning of 2023. As artificial intelligence becomes increasingly integrated into the global economy, these and other similar companies are expected to attract more investment and drive additional index returns. While there are some similarities between the current environment and the Dot-Com Bubble, the U.S. economy continues to show resilience and most of the winners from the last two years are well-established businesses with healthy profits. Still, history has shown us that periods of robust equity market performance do not continue forever. As the calendar changes to 2025, investors should keep this idea in mind as it relates to expectations for near-term stock returns.