Amazon announces a hyperscale data center. Regulators approve permits. Investors update forecasts. Utilities begin planning transmission upgrades.

But one question remains unanswered:

Is the construction actually happening?

How is the project actually developing once construction begins?

For much of the past century, this distinction mattered relatively little. Many infrastructure projects were local in both their benefits and their costs. Increasingly, however, large infrastructure investments interact with networks that extend well beyond the project site. Electricity, water, transport, and communications systems connect places that administrative boundaries separate. As a result, the economic consequences of infrastructure investments are not always experienced where the investment itself is located.

Data centers provide a useful illustration.

A proposed hyperscale data center in Alabama, for example, was projected to consume nearly two million gallons of water each day, approximately two-thirds of the daily water use of its host city1.

Electricity presents a similar challenge.

Under the traditional utility model, investments required to accommodate large new electricity demand are generally recovered through regulated rate structures. Whether existing approaches to cost allocation remain appropriate for exceptionally large electricity consumers, such as hyperscale data centers, has become an increasingly important regulatory question2.

The immediate economic benefits of a data center, including investment, employment, and local tax revenue, are concentrated where the facility is located. Some of the associated infrastructure costs, however, may extend across a much broader utility system.

The challenge is therefore not simply to understand these consequences after they occur. It is to observe infrastructure development while it is taking place.

This is where geospatial observation complements administrative information. A recent study by the Federation of American Scientists demonstrates how commercially available satellite imagery can independently track the physical construction of hyperscale data centers.

Comparing observable construction activity with publicly announced development timelines, the study shows that announcements and physical development do not always progress in parallel3.

The contribution of satellite imagery is not that it replaces administrative records or company disclosures. Instead, it provides an independent observation of physical development that can be compared with existing sources to verify how infrastructure projects evolve over time.

This distinction matters because many of the consequences of infrastructure investment emerge gradually. Electricity demand rises as facilities become operational. Supporting infrastructure expands as construction progresses. Administrative datasets, utility filings, and economic statistics eventually capture these developments, but often only after investment decisions have been made.

Geospatial observation cannot answer these questions on its own. Used alongside administrative records and economic analysis, however, it can reduce the interval between physical development and empirical observation.

Infrastructure Operates Through Networks

The reason is straightforward.

The economic effects of infrastructure are often assumed to be local because the infrastructure itself occupies a specific location. In practice, however, many infrastructure investments operate through interconnected systems whose boundaries differ from administrative jurisdictions.

Electricity is generated in one place, transmitted across regional networks, and priced through regulated utility systems. Water resources are shared across river basins and aquifers. Transport infrastructure influences economic activity well beyond the communities in which it is constructed.

Consequently, the geography of infrastructure investment does not necessarily coincide with the geography of its economic consequences.

Electricity illustrates this particularly well. Large data centers require substantial investments in generation, transmission, and distribution infrastructure before they become operational. Under traditional utility regulation, these investments are generally recovered through regulated rate structures that allocate costs across customers within a utility system. Whether existing regulatory frameworks remain appropriate for exceptionally large electricity consumers has therefore become an increasingly important policy question as electricity demand from hyperscale data centers continues to grow4.

The central issue is not that costs are necessarily shifted to other consumers, but that the economic consequences of new infrastructure depend on the structure of the utility network rather than the location of the facility alone.

Recent work by Lange, Bagwe, Murlidharan, and Rojas examines this question by mapping data-center development onto utility service territories rather than administrative boundaries. The study finds evidence consistent with residential electricity prices increasing in non-host counties connected through shared utility systems, while industrial electricity prices respond more strongly in host counties.

The authors emphasize that these estimates should be interpreted as evidence on electricity price incidence rather than as a full welfare analysis because the study does not directly observe utility rate cases, tariff provisions, or cost-allocation decisions. Their findings suggest that the geography of electricity price incidence may differ from the geography of infrastructure investment, reinforcing the importance of understanding the networks through which infrastructure operates.

The same principle extends beyond electricity prices. Muller estimates that the environmental damages associated with electricity consumption by U.S. data centers are geographically concentrated, shaped less by where a given facility is sited than by the emissions profile of the regional grid supplying it5. Although the outcomes are different, both studies point to the same conclusion. The consequences of major infrastructure projects frequently propagate through interconnected systems rather than remaining confined to the locations where projects are built.

This is precisely where geospatial intelligence becomes valuable. Satellite imagery identifies where physical infrastructure is emerging, while economic analysis explains how its consequences propagate through connected systems. Neither is sufficient on its own. Together, they provide policymakers with a more complete understanding of infrastructure development than either source could offer independently.

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From Observation to Decision

The examples discussed above point to a broader lesson.

Modern infrastructure projects generate information long before their full economic consequences appear in conventional statistics.

Administrative records document what has been approved. Satellite imagery observes what is physically happening on the ground. Economic analysis explains how those developments interact with electricity systems, supply chains, labor markets, or environmental outcomes. Each source answers a different question.

Viewed independently, each source also has clear limitations. Administrative records cannot verify whether announced projects are progressing as expected. Satellite imagery can observe physical development but cannot explain its economic significance. Economic indicators ultimately capture many of the consequences of infrastructure investment, but often only after substantial time has passed. Combining these sources provides a more complete picture than any one of them can offer individually.

This integrated approach becomes increasingly valuable as infrastructure projects grow larger and more interconnected. Whether the question concerns electricity systems, transport corridors, industrial facilities, logistics networks, or urban development, policymakers are increasingly required to make decisions before conventional datasets fully reflect changing economic conditions. Independent geospatial observation, interpreted alongside administrative information and economic analysis, can help reduce that information gap.

The value of geospatial intelligence therefore lies not simply in producing better images, but in improving economic understanding. Satellite imagery allows infrastructure to be observed as it develops. Economic analysis helps explain why those developments matter. Together, they provide policymakers with earlier, more comprehensive evidence for evaluating infrastructure investment and its broader consequences.

From Research to Practice

The questions discussed throughout this article are no longer purely academic.

At Atlas Analytics, we are in the early innings of applying geospatial intelligence to real-world infrastructure decisions alongside leading institutions in finance, consulting, and the public sector. Our work combines satellite imagery, administrative records, and economic modeling to help organizations monitor infrastructure development, understand its broader economic implications, and make better-informed decisions before conventional datasets fully reflect changing conditions.

We believe this is just the beginning. As satellite imagery becomes more frequent, computer vision continues to improve, and economic models become increasingly integrated with geospatial data, the opportunity to generate earlier, more actionable economic intelligence will continue to expand.

If your organization is evaluating major infrastructure investments, tracking industrial development, or exploring how geospatial intelligence can improve decision-making, we’d welcome the opportunity to start a conversation. We are actively partnering with a small number of organizations to develop new applications and would be delighted to explore how this approach could support your work.

Interested in collaborating? If your organization is exploring how geospatial intelligence can support infrastructure planning, economic analysis, or investment decision-making, we’d love to hear from you. Contact us at contact@atlasanalytics.com to discuss research collaborations, pilot projects, or bespoke geospatial intelligence solutions.

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Last quarter marked an important milestone for Atlas Analytics.

We publicly forecasted 2.0% annualized real GDP growth several weeks before the Bureau of Economic Analysis released its advance estimate. The government’s initial estimate also came in at 2.0%, and the final figure settled at 2.1% following subsequent revisions.

Atlas and the government ultimately arrived at nearly the same answer. The difference is that our forecast was available almost three months before the final estimate.

This is why we are building Atlas: to provide reliable economic intelligence while conditions are still unfolding, rather than months after the underlying activity has occurred.

So, what are we seeing for Q2?

The Q2 Forecast

Atlas Analytics is forecasting 1.95% annualized real GDP growth for the second quarter of 2026.

The headline appears soft, but the components tell a more balanced story.

Within our framework:

Core GDP + Net Exports + Private Inventories = Headline GDP

For the second quarter, Atlas estimates:

• Core GDP: +3.18 percentage points

• Private Inventories: +0.30 percentage points

• Net Exports: -1.52 percentage points

• Headline GDP: +1.95%

Core GDP and Private Inventories together contribute approximately 3.48 percentage points of growth. The difference between that figure and our headline forecast comes almost entirely from international trade.

Atlas estimates that Net Exports will subtract approximately 1.52 percentage points from second-quarter growth.

This composition is important. The forecast does not point to broad weakness across the domestic economy. Core activity remains healthy, and businesses continued rebuilding inventories. The primary source of weakness is the trade balance.

That distinction would be difficult to identify from the headline number alone.

What JACK Is Seeing

Over the past year, Atlas has been developing JACK, the Joint Algorithm for Containerized Knowledge.

As we discussed in our recent article, You Don’t Know JACK, JACK uses satellite imagery and artificial intelligence to monitor activity across major U.S. container ports.

The system identifies container ships and uses computer vision to count containers moving on and off vessels. These observations help Atlas estimate imports, exports, and Net Exports before the official trade statistics become available.

Historically, our models inferred trade activity from related economic indicators. JACK allows us to begin measuring much of that activity directly.

This quarter, the signal is clear. International trade represents the primary drag on second-quarter GDP growth.

There is also some encouraging news. Our data suggest that trade conditions improved modestly during June, indicating that the drag from Net Exports may have started to ease toward the end of the quarter.

That improvement does not offset the weakness recorded earlier in Q2, but it may provide an early indication of where conditions are heading as the third quarter begins.

GDP Is the Score. The Economy Is the Game.

People often ask whether Atlas is a GDP forecasting company. GDP is an important part of what we do, but it is not the end product.

GDP is our benchmark. Economic intelligence is our business.

We forecast GDP because it gives us an objective scorecard. Every quarter, our models are tested against one of the world’s most closely watched economic statistics. That comparison allows our clients and our team to evaluate where the system is performing well and where it needs to improve.

Government statistics eventually capture these changes, but usually after a delay. Atlas is building the infrastructure to observe them as they happen.

Ab Astris ad Terram et Mare

From the stars to the land to the sea.

Atlas signals observe the physical economy across land and sea, using satellite imagery and artificial intelligence to measure activity before traditional data becomes available.

At sea, JACK monitors import and export activity at major U.S. ports. On land, Atlas measures economic conditions across states, metropolitan areas, counties, and neighborhoods, providing earlier visibility into growth, market conditions, and valuations.

We are now extending that capability to individual assets, including manufacturing facilities, data centers, logistics hubs, commercial properties, and critical infrastructure. By measuring changes in production, utilization, construction, and activity, Atlas can support investment decisions, underwriting, asset monitoring, and operational planning with intelligence grounded in what is physically happening.

What Comes Next

The second quarter illustrates why the composition of growth matters.

A headline forecast of 1.95% might suggest that the entire economy is losing momentum. Our data point to a different conclusion. Domestic activity remains healthy, inventories are contributing to growth, and international trade accounts for nearly all of the weakness in the forecast.

We can make that distinction because Atlas is moving beyond traditional forecasting inputs and directly observing more of the physical economy.

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ICYMI

Last week, Our Founder and CEO Jake Schneider and Aditya Murlidharan, Economic Analytics Fellow, examined whether elevated market valuations extend beyond the technology sector. Their analysis found that the disconnect between equity prices and current economic activity has become increasingly broad-based.

Full Analysis Here

Over the next two trading days, technology stocks (led by the XLK and QQQ) fell another 5%.

By the end of Q2 2026, our estimated fair value for XLK had fallen to $145.39, while the ETF closed at $180.35. The valuation gap widened to 24%.

That should have been the story.

It was not.

Irrational Exuberance Extends Into the Broader Market

We applied our same fair-value framework to the remaining fifteen ETFs tracked by Atlas. We expected technology to stand apart. Instead, we found that elevated valuations were visible across much of the market. Technology remained the most expensive sector, but it was no longer an outlier.

One interpretation is that markets are pricing a future economy rather than the one that exists today. Investors may expect stronger productivity growth, higher future earnings, or a structural improvement in economic performance. The rapid diffusion of generative AI and the ease with which it is being adopted across industries have strengthened those expectations. If investors believe the gains will be economy-wide rather than confined to technology, higher valuations should naturally extend beyond technology and technology-adjacent firms. Our findings are consistent with that possibility.

Alan Greenspan, our founder Jake Schneider’s first boss, famously asked whether financial markets were exhibiting “irrational exuberance.” We do not use that phrase lightly. Nor do we argue that current valuations are necessarily irrational. Our claim is narrower. Relative to today’s level of economic activity, equity markets appear to be pricing a considerably stronger economy than the one reflected in current economic activity.

That raises a simple question:

How far have market prices moved ahead of the economy that exists today?

Our framework begins with an equally simple proposition. Over the long run, corporate revenues, profits, and cash flows are constrained by aggregate economic activity. Gross Domestic Product (GDP), despite its imperfections, remains the broadest measure of that activity. If GDP can be estimated accurately before the official releases, it becomes possible to compare market prices with the level of economic output they implicitly assume. That comparison forms the foundation of this publication.

What the Numbers Show

Table 1 compares our estimated fair values with market prices for seven widely followed ETFs at the end of Q2 2026.

The results are difficult to dismiss as an isolated technology phenomenon.

XLK and QQQ remain the most expensive, trading 24.0% and 19.3% above fair value respectively. That is unsurprising.

What is more surprising is everything else. SPY and DIA, the two most widely followed large-cap benchmarks, trade roughly ~16% above fair value. IWV, representing almost the entire investable US equity market, remains more than 11.1% above fair value. Even IWM, despite representing smaller companies that have largely stayed outside the enthusiasm surrounding the largest technology firms, trades above the level implied by current GDP.

Only one ETF stands apart. XLV, representing the health care sector, trades below our estimated fair value. That’s particularly interesting given that the majority of recent hiring from the Bureau of Labor Statistics’ monthly Jobs Reports have been accrued to this single sector.

Technology therefore appears to be the extreme case, but not the only case. The difference is increasingly one of magnitude rather than direction.

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Is This Just One Quarter?

One quarter proves very little.

Markets often move ahead of the economy and, at other times, fall behind it. Short-term deviations from fundamentals are therefore neither unusual nor especially informative. The more relevant question is whether the current valuation gap reflects a temporary episode or whether it forms part of a broader pattern.

To examine this, we extended the analysis beyond the latest quarter and estimated GDP-implied fair values across the historical sample for all sixteen ETFs using the current Atlas valuation models. Rather than asking whether markets appear expensive today, we ask a simpler question: how has the relationship between market prices and underlying economic activity evolved over time?

Chart 1 reports the results for XLK. We focus on technology because it provides a useful illustration of the broader exercise.

The chart shows that the relationship between market prices and GDP-implied fair values is far from constant. Through much of 2022 and early 2023, XLK traded below its GDP-implied fair value as markets adjusted to higher interest rates, slowing growth expectations, and a broad repricing of technology stocks. As macroeconomic conditions evolved, the gap gradually narrowed. By early 2024, market prices had moved above the level implied by realised economic activity and have, with few interruptions, remained there since.

This evolution is perhaps more informative than the latest observation itself. The valuation gap does not move in one direction. Periods of undervaluation are followed by periods of overvaluation, with prices repeatedly moving around their GDP-implied fair values as economic and financial conditions change. The current quarter is therefore better viewed as one point in a longer sequence than as an isolated result.

We repeated the same exercise for the remaining ETFs in our sample. While the timing and magnitude of the valuation gaps differ across sectors, the exercise reveals a similar feature across the broader market. The relationship between prices and underlying economic activity evolves over time, making it possible to distinguish temporary departures from more persistent divergences.

Two ETFs deserve equal attention for the opposite reason.

Health care (XLV) and small-cap equities (IWM) remain much closer to their GDP-implied values. Both alternate between modest overvaluation and modest undervaluation rather than drifting consistently in one direction.

That distinction matters.

If elevated valuations reflected only enthusiasm surrounding the largest technology companies, broad market benchmarks would not display the same pattern. Nor would the divergence persist across sectors with very different exposures to technological change.

Instead, the evidence suggests that optimism has become considerably more widespread.

Why GDP?

The framework rests on a simple proposition.

Corporate earnings do not grow independently from the economy forever. Household consumption, business investment, government expenditure and exports ultimately determine the revenues firms can generate. GDP is the broadest measure of that aggregate activity.

Importantly, no single macroeconomic variable explains equity prices completely, not even GDP. Interest rates matter. Credit conditions matter. Financial conditions matter. These variables influence valuation around a common anchor: the economy’s capacity to generate income.

That is why GDP sits at the center of our framework.

For the historical analysis presented above, we use final GDP after all official revisions. The exercise therefore evaluates the pricing relationship itself rather than the quality of any forecast.

Current-quarter estimates present a different challenge. Official GDP is released quarterly and revised twice afterwards. Markets therefore spend much of their time trading without knowing actual current economic conditions.

To address that gap, we combine the same valuation framework with Atlas’s live GDP nowcast, generated from proprietary satellite-based measures of economic activity.

The objective is straightforward:

Estimate today’s economy using today’s information rather than waiting for tomorrow’s official data.

More on Market Intelligence

What This Suggests

There are at least two plausible interpretations of these findings:

1. The Market Is Correct: The first is that markets are correctly looking beyond today’s economy. Expectations of stronger productivity growth, broader technological diffusion and higher future earnings may justify valuations that appear elevated relative to current GDP. Under this interpretation, prices are discounting future fundamentals rather than departing from them.

2. The Return of “Irrational Exuberance”: Markets may simply have moved ahead of themselves. Expectations have risen faster than realised economic activity, leaving valuations increasingly detached from the economy that ultimately supports corporate earnings.

Our analysis does not distinguish between these explanations.

It establishes something narrower, but also more measurable.

Key Takeaway

The gap between equity prices and current economic activity has become persistent, broad-based and larger over time. Technology remains the most expensive corner of the market, but it no longer appears exceptional. The same pattern is now visible across much of the market.

We do not know whether today’s valuations will ultimately prove justified. We do know that markets are pricing an economy considerably stronger than the one reflected in current GDP. Whether that gap reflects foresight or irrational exuberance is a question only time can answer.

Want to know our Atlas ETF estimates in-real time? Contact us to subscribe to an enterprise license and get the power of satellite-derived economic and equity estimates delivered through our proprietary portal to your inbox every week.

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What if investors could understand the economy as it unfolds, rather than months after the fact?

That was the central question Atlas Analytics, Founder & CEO, Jake Schneider explored on The Blunt Dollar, where he joined Ignacio Ramirez to discuss how satellite imagery, artificial intelligence, and macroeconomic modeling are changing the way economic activity can be measured.

For decades, markets have relied on official economic data that is delayed, revised, and often backward-looking. GDP is one of the most important measures of economic performance in the world, but by the time official releases arrive, investors, policymakers, and business leaders are often reacting to conditions that have already changed.

As Jake put it:

“GDP and other key economic indicators come out with a lag. Satellites continue overhead in real time.”

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Atlas Analytics algorithms were built around a different idea: the physical economy leaves visible traces in real-time.

Construction sites expand. Ports fill and empty. Land use changes. Containers move through supply chains. Factories, infrastructure, farmland, and transportation networks all generate observable signals that can be measured from above. At Atlas, those signals are transformed into real-time economic intelligence.

“We ingest satellite imagery from outer space and turn raw images of Earth into usable data for forecasting the economy in real time.”

The company’s core algorithm, ROY, short for Remote Orbital Yield, focuses on broad economic activity, including construction, land use, vegetation, and other physical signals tied to core GDP. By comparing decades of historical satellite imagery with official GDP data, Atlas has built models designed to identify changes in economic momentum as they happen.

One of the clearest examples came in Q2 2025. At the time, consensus expectations suggested the U.S. economy was slowing. Atlas saw something different. The company forecasted 4.3% GDP growth, well above consensus estimates near 2%. Months later, the Bureau of Economic Analysis revised GDP to 3.8%, much closer to Atlas’ original forecast.

“The consensus was 2%. We predicted 4.3%, and the final answer was 3.8% three months later.”

That time advantage matters. In markets, being directionally right is valuable. Being directionally right months early can be transformative.

The conversation also highlighted JACK, Atlas’ trade intelligence platform and second major algorithm. JACK, short for Joint Algorithm for Containerized Knowledge, uses satellite imagery over major U.S. ports to monitor container activity and estimate trade flows in near real time. By tracking TEUs, or twenty-foot equivalent units, JACK is designed to improve visibility into imports, exports, and net exports before official data is available.

A Global Forecast

Jake highlighted Atlas’ work with the International Monetary Fund and Johns Hopkins University to explore how satellite-based economic measurement can be applied beyond the United States, particularly in regions where official data can be delayed, incomplete, or released with long lags. In parts of Sub-Saharan Africa, for example, economic data can arrive up to a year late. Real-time measurement tools could help investors, policymakers, and institutions better understand economic conditions and allocate capital more effectively.

The conversation was also candid about what Atlas has learned. Jake discussed a recent quarter where Atlas’ headline GDP forecast diverged from the official release, particularly because of net exports. Rather than avoid the miss, Atlas has used it to sharpen its methodology and accelerate development of JACK.

“We are transparent about our wins and our losses — and that’s how you know you can trust us.”

That transparency is central to how Atlas thinks about trust.

The broader vision is simple but ambitious: move macroeconomic analysis from delayed reporting to real-time measurement. By observing the physical economy directly, Atlas is building tools that help investors and institutions see economic turning points sooner, understand regional and global activity more clearly, and make decisions with a faster, more grounded view of the world.

The economy moves in real time, economic intelligence can too with Atlas Analytics.

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On June 25th, the Bureau of Economic Analysis released its third estimate of U.S. GDP growth for Q1 2026: 2.1%.

For most economists, that’s the end of the story.

For us at Atlas Analytics, it was confirmation of something we already knew.

We published essentially the same forecast nearly three months ago.

The Problem with Economic Data

The U.S. economy moves in real time.

Economic statistics do not.

By the time official GDP is released:

• Investors have already repriced markets.

• Businesses have already made capital allocation decisions.

• Policymakers are already reacting to conditions that no longer exist.

GDP is arguably the single most important measure of economic activity in the world, yet it remains fundamentally backward-looking.

The advance estimate arrives roughly one month after the quarter ends.

The second estimate another month later.

The third estimate another month after that.

By the time the “final” number is published, nearly an entire quarter has passed.

In fast-moving markets, that’s an eternity.

Atlas Predicted It Months Earlier

Using Atlas Analytics’ ROY (Remote Orbital Yield) framework, which combines satellite imagery, geospatial intelligence, and economic modeling, we estimated first-quarter GDP long before the BEA released its official figures.

Our published estimate:

2.0%

BEA Third Estimate:

2.1%

A difference of just 0.1 percentage points.

That isn’t luck.

It’s the product of continuously measuring the real economy rather than waiting for surveys and delayed government reporting.

Real-Time Intelligence Changes Decisions

The goal isn’t simply to forecast GDP.

The goal is to understand the economy while it’s happening.

Because once you know where growth is today, you can begin answering much more valuable questions:

• Which industries are accelerating?

• Which regions are slowing?

• Which assets are becoming mispriced?

• Which companies are outperforming what investors currently believe?

Instead of reacting to history, decision-makers can respond to the present.

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Introducing the Atlas Portal

Today we’re also excited to share a preview of the next evolution of Atlas Analytics.

Our new client portal delivers our economic intelligence directly to customers through an interactive dashboard.

The executive overview provides continuously updated macroeconomic indicators, including Core GDP, inventories, net exports, and headline GDP, in a single interface.

Clients can also explore economic performance geographically through our new state-level forecasting dashboard.

Every U.S. state receives its own GDP forecast, allowing investors, corporations, and policymakers to identify regional divergence long before official statistics become available.

This is only the beginning.

Future releases will include:

• Historical forecasting revisions

• Weekly economic updates

• Asset-level intelligence

• Security-level insights

• Client-specific custom dashboards

Our objective is simple:

Move macroeconomic intelligence from quarterly reports to continuous decision support.

The Future of Economics

For decades, economists have accepted a simple constraint:

You cannot know the economy until after it has already happened.

Satellite imagery changes that.

Machine learning changes that.

Geospatial intelligence changes that.

The future of economics isn’t faster spreadsheets.

It’s observing economic activity directly.

Official statistics will always remain important.

But increasingly, they will become confirmation—not discovery.

At Atlas Analytics, that’s the future we’re building.

And this quarter, it arrived three months early.

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This week, the world lost a giant of economics.

As a young man, I had the extraordinary privilege of working directly for Dr. Alan Greenspan for nearly two years. Rather than write about his career, his legacy, or, quite frankly, his genius, I thought I’d simply share a few stories from our time together and the lessons they left with me.

Frequent readers of this Substack may know that I worked for Dr. Greenspan, but few know how I came to have the job.

Like many aspiring economists, I admired him from afar. One afternoon, while he was delivering a speech at the Brookings Institution, I waited in line afterward with a worn copy of The Age of Turbulence, hoping to have it signed. His Chief of Staff couldn’t arrange the signing that afternoon, but took my information.

A few weeks later, I arrived at his office carrying that same book. I assumed I would leave it with the receptionist for an autograph.

Before I could even introduce myself, the office doors opened.

Out walked Dr. Greenspan.

What I expected to be a thirty-second exchange became an hour-long conversation. We talked not about the econometric models or the Federal Reserve, but about what I considered one of the greatest accomplishments of his career: engineering the soft landing of 1994. I had studied that episode extensively and peppered him with questions about monetary policy, inflation expectations, and the delicate balance between acting too early and too late.

About a week later, my phone rang.

It was his Head of Research.

“Dr. Greenspan would like to know if you’d be interested in joining him as his research assistant.”

It remains one of the most unexpected and consequential phone calls of my life.

More important than how I was fortunate enough to land the job, however, was what I learned from him.

Search for today’s economy, not yesterday’s

Dr. Greenspan had enormous respect for official statistics, but he also understood their limitations. By the time GDP, employment, or inflation figures reached policymakers, the economy had often already changed.

He was constantly searching for signals that could tell him what was happening now, not what had happened three months ago.

That philosophy left an indelible mark on me.

Years later, it became the intellectual foundation for Atlas Analytics. Our work using satellite imagery to estimate GDP is, in many ways, an attempt to answer the same question he asked every day:

“What is the economy telling us today?”

Hold your convictions with humility

Despite being one of the most influential economists in history, Dr. Greenspan never pretended the economy could be known with certainty.

He held strong views, but lightly.

He understood that forecasting was not about proving yourself right. It was about continuously updating your beliefs as new information emerged.

Economics often rewards confidence.

Reality rewards intellectual honesty.

Never lose your sense of humor

The public knew Alan Greenspan as the famously reserved Chairman of the Federal Reserve.

I came to know someone with an unexpectedly dry wit.

One afternoon, while we were working on a research project together, I told him I’d make sure everything was “spick and span.”

Without looking up from his papers, he smiled and replied,

“I’m Greenspan. I’ll handle the span—you handle the first part.”

It was classic Greenspan: understated, clever, and perfectly timed.

On another occasion, during a trip to New York, the hotel pastry chef surprised Dr. Greenspan with an elaborate Star Wars-themed chocolate sculpture depicting him as Luke Skywalker. He admired it for a few moments, laughed, and then handed it to me.

That was him.

He never seemed particularly interested in the attention that came with being Alan Greenspan. To him, it was just chocolate.

When people think of Dr. Greenspan, they think of interest rates, inflation, and monetary policy.

I think of the young economist who nervously walked into an office carrying a book for an autograph and walked out with a mentor.

I think of the man who challenged me to look beyond the data everyone else was looking at.

I think of his quiet generosity, his relentless curiosity, and his wonderfully understated sense of humor.

Dr. Greenspan was a brilliant economist, but he was much more than that.

He represented an ideal that feels increasingly rare: intellectual curiosity, humility before uncertainty, respect for evidence, and a lifelong commitment to public service.

History will remember the Chairman.

I’ll remember Dr. Greenspan.

Rest in peace, Mr. Chairman.

We’re pleased to announce that Atlas Analytics’ economic intelligence is now available through Macrobond, one of the world’s leading platforms for macroeconomic research and analysis.

Macrobond is trusted by thousands of economists, investment professionals, central banks, government agencies, and research institutions around the world. By integrating Atlas’ data into the platform, institutional users can now access our real-time economic estimates alongside many of the world’s most widely used economic and financial datasets.

We’re also honored that Atlas Analytics was featured in Macrobond’s latest “Most Insightful Additions of the Past Two Weeks,” highlighting our satellite-derived GDP forecasts as one of the platform’s notable new data releases.

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Bringing Real-Time Economic Intelligence into Institutional Workflows

For decades, economists and investors have relied primarily on government statistical releases to understand the state of the economy. While these datasets remain indispensable, they are often published with meaningful delays and are subject to subsequent revisions.

Atlas Analytics takes a different approach.

Our proprietary Remote Orbital Yield (ROY) model combines satellite imagery with machine learning to estimate economic activity in near real time, providing a forward-looking view of the economy before many traditional indicators become available.

Through Macrobond, users can now incorporate Atlas’ economic intelligence directly into the research environment they already use every day.

Current Data Availability

The initial Macrobond release includes Atlas’ precision GDP forecasts for the United States, including several state-level economic forecasts such as:

• New York

• Illinois

• Arizona

These datasets provide timely, geographically granular measures of economic activity that complement traditional government statistics and may help investors identify changing economic conditions earlier.

More on State-level Forecasts

Looking Ahead

Our partnership with Macrobond represents an important step in Atlas’ mission to modernize macroeconomic measurement.

Over the coming months, we expect to expand our offerings with additional U.S. states, broader geographic coverage, and new datasets that leverage satellite imagery to measure economic activity, trade, and financial market intelligence.

We’re grateful to the Macrobond team for their partnership and excited to make Atlas’ economic intelligence available to a broader community of investors, economists, policymakers, and researchers.

As always, we’ll continue building the next generation of macroeconomic intelligence from outer space.

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On Friday, Atlas Analytics Founder & CEO, Jake Schneider, joined Tom Keene and Paul Sweeney on Bloomberg Surveillance to discuss how real-time economic intelligence is changing the way investors understand markets.

For decades, investors have relied on government economic data that is often delayed, revised, and backward-looking. Atlas Analytics was built around a different idea: that satellite imagery, computer vision, and machine learning can help measure the physical economy closer to real time. By observing patterns in construction, transportation, infrastructure, and other activity on the ground, Atlas aims to give investors a faster read on where growth is accelerating, slowing, or being mispriced by markets.

Jake’s conversation with Tom and Paul comes at an important moment. Markets are wrestling with questions around growth, inflation, interest rates, productivity, and whether traditional macro indicators are still moving fast enough to capture today’s economy. Atlas’ work sits directly at that intersection, using alternative data to help investors identify turning points before they appear in consensus forecasts.

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Across the interview, Jake returned to one central idea: the economy is constantly changing, but the tools used to measure it have not kept pace. Atlas is working to close that gap by translating observable activity in the physical economy into real-time macro signals for investors, economists, and market participants.

The conversation centered on three key takeaways:

1. Why satellite data matters: Jake framed Atlas as a new way to measure the economy: not by waiting for delayed reports, but by observing real-world activity as it unfolds.

2. Why real-time GDP matters: The segment underscored a core Atlas argument: markets move continuously, but the economic data investors rely on often arrives months late.

3. Why macro mispricing matters: Jake framed Atlas not simply as a data platform, but as a way to detect disconnects between market expectations and underlying economic activity. By using satellite imagery and AI to observe physical activity directly, Atlas is helping close that gap for investors, economists, and market participants.

We’re grateful to Bloomberg Surveillance and their broadcast team for the opportunity to share Atlas’ perspective with a broader audience. For those interested in real-time GDP signals, market valuation work, and the future of macroeconomic intelligence, get in touch below.

Get in Touch

This article was written with valuable assistance from Morgan Reppert, Chief of Staff for Atlas Analytics.

At Wednesday’s highs, the Technology Select Sector SPDR ETF (XLK) traded just shy of $199 per share.

By Friday’s close, it had fallen to approximately $180, erasing nearly $20 per share in less than two trading sessions.

For readers of Atlas Analytics, the pullback should not come as a surprise.

Two weeks ago, we warned clients that technology equities had become increasingly disconnected from the pace of underlying economic growth. Friday’s selloff does not invalidate that thesis. If anything, it may represent the first stage of a broader repricing.

Even after the decline, Atlas estimates the fair value of the XLK at approximately $151 per share. In other words, despite one of the sharpest technology selloffs of the year, we still estimate that investors are paying roughly 20% above what current economic fundamentals justify.

The question is no longer whether technology stocks can decline.

The question is whether the market has fully reconciled expectations with reality, or whether Friday was simply the beginning.

A Growing Disconnect Between Markets and Fundamentals

Big Tech has rallied 35% over the past 6 months through Wednesday’s highs, the majority of which has happened since April.

Although economic growth has improved, it remains far from extraordinary.

Recall that Q4 2025 GDP ultimately clocked-in at a somewhat dismal 0.5% growth, and Q1 2026 is currently in the books at 1.6% (recall that we predicted 2.0% approximately two weeks before the first release, which was 2.0%). Privately with our clients, we are currently forecasting growth around 2.5% for Q2, suggesting that economic activity is improving, but is hardly gangbusters.

In contrast, the XLK (State Street’s Tech ETF), VGT (Vanguard’s Tech ETF), and the QQQ (the NASDAQ-tracking ETF) were all up by huge margins:

• XLK: +35.7% YTD (as of COB Wednesday)

• VGT: +31.6% YTD (as of COB Wednesday)

• QQQ: +21.1% YTD (as of COB Wednesday)

The divergence between economic growth and technology equity performance is difficult to ignore.

We believe the market is currently closer to 2001 than 1999.

Not because valuations are as extreme as they were during the Dot-Com Bubble, but because investor expectations increasingly appear detached from the pace of economic growth.

The Correlation Between GDP and Tech, Again

As we’ve written about extensively throughout this periodical, equity prices do not exist in a vacuum. In fact, there is what we have termed “the invisible tether” that relates economic fundamentals (as measured by GDP) to macro-exposed ETFs.

In the chart above, the invisible tether is between GDP and the XLK, State Street’s tech-based, macro-exposed ETF.

As the chart above shows, GDP (as measured by the seasonally adjusted annualized rate, the same formulation used by the Bureau of Economic Analysis to calculate it) is nearly 50% correlated with the quarterly price change in the XLK.

In plain English, periods of stronger economic growth tend to coincide with stronger technology-sector returns, while weaker growth tends to coincide with weaker returns.

Thus, if you know GDP ahead of the government releases (as Atlas believes we do), one can use the past association with macro-exposed ETFs to predict the fundamental value of that security.

And what is the fundamental value of the XLK currently?

Using our proprietary GDP forecasts and financial factor models, we believe the fair-value price of the XLK should be around $150.73.

As of COB on Friday, the XLK was trading at $180.30.

Therefore, Atlas Analytics estimates that even after Friday’s rout where it dropped by nearly 7%, the XLK is still overvalued by approximately 20% relative to current economic fundamentals. We reach similar conclusions for the QQQ, while most other major ETFs remain much closer to fair value.

So is this a bubble?

Fortunately, investors do not have to guess. Markets have confronted similar episodes before.

To understand where we may be headed, it is useful to revisit the last great technology mania.

An Imperfect Historical Analog

When investors think about the Dot-Com Bubble, they often remember the spectacular collapse that followed. What is less remembered is how compelling the underlying story appeared at the time.

The internet was transforming commerce. Communication was becoming instantaneous. Entire industries were being reinvented. The productivity gains that followed were very real and ultimately reshaped the global economy.

The problem was not the technology.

The problem was valuation.

By the late 1990s, investors had become convinced that the future would be dramatically different from the present. Technology stocks increasingly became priced not on current earnings or economic fundamentals, but on expectations of future growth.

Eventually, reality caught up with those expectations.

Economic growth slowed. Corporate profits failed to keep pace with investor optimism. Investors began asking a question they had largely ignored during the boom:

“How much of this future growth has already been priced into today’s prices?”

The result was severe. From its peak in March 2000 to its trough in October 2002, the NASDAQ Composite lost nearly 80% of its value.

However, we believe investors should be cautious about drawing a direct parallel between that period and today.

Unlike many of the speculative internet companies of the late 1990s, today’s technology leaders are highly profitable businesses generating enormous amounts of revenue and free cash flow. Collectively, the Magnificent Seven currently trades at approximately 28 times forward earnings, elevated relative to the broader market, but nowhere near the extreme valuations observed during the height of the Dot-Com Bubble.

Moreover, the U.S. economy today remains fundamentally healthy. Atlas currently estimates second-quarter GDP growth of approximately 2.5%, a far cry from the deteriorating economic backdrop that accompanied the 2000 to 2002 bear market.

For these reasons, we do not believe the most likely outcome is an 80% collapse in technology equities.

What concerns us is something more subtle.

Investors appear to be pricing years of future AI-driven productivity gains into today’s valuations before those gains have meaningfully appeared in the underlying economic data.

If those expectations prove too optimistic, or simply take longer to materialize than investors currently expect, the result may not be a crash, but rather a prolonged period of disappointing returns as fundamentals gradually catch up to prices.

The lesson of 2001 is not that transformative technologies fail.

The lesson is that investors can be right about the technology and still wrong about the price.

How We’re Trading It With Capital-At-Risk

If our analysis is correct, a natural question follows:

What causes the market to recognize the disconnect between technology valuations and economic fundamentals?

The honest answer is that we do not know.

Markets rarely announce their turning points in advance. In hindsight, every correction appears obvious. In real time, the catalyst is almost always unexpected.

There are, however, several plausible scenarios:

1. The first is that investors simply become less optimistic about the geopolitical outlook. A significant portion of the recent rally appears to have been driven by improving sentiment surrounding the Trump administration and the possibility of a de-escalation of tensions with Iran. If that optimism proves premature, risk assets could quickly reprice.

2. The second is inflation.

   Although inflation has moderated considerably from its post-pandemic highs, it remains above the Federal Reserve’s long-run target. Any renewed acceleration in price pressures could force markets to reconsider expectations for monetary policy and interest rates, placing pressure on some of the market’s highest-valued growth assets.

3. The third is earnings.

   Technology valuations ultimately depend upon extraordinary future profit growth. If AI-related revenues, margins, or adoption rates fail to meet investor expectations, even modest earnings disappointments could trigger a reassessment of valuations.

Atlas is not betting against technology, artificial intelligence, or innovation.

Rather, we are betting that economic fundamentals ultimately matter.

The timing of any correction remains unknowable. What can be observed, however, is a growing divergence between technology valuations and the economic activity that has historically supported them.

As a result, Atlas has reduced exposure to the most richly valued segments of the technology sector and maintains a cautious stance toward assets whose prices appear increasingly dependent upon future expectations rather than present fundamentals.

Earlier this week, Atlas established bearish positions through XLK and IWM put options. Those positions benefited from the recent pullback. Nevertheless, we have only partially reduced exposure and continue to maintain downside positioning because our models suggest technology equities remain meaningfully above fair value.

We may ultimately prove wrong. AI may generate a wave of productivity growth that exceeds even today’s optimistic assumptions.

But if history teaches anything, it is that investors are often willing to pay too much for even the best ideas.

For now, Atlas remains positioned for a narrowing of the gap between technology valuations and economic fundamentals. Whether that adjustment occurs through slower prices, stronger growth, or some combination of both remains to be seen.

Get in Touch

Atlas Analytics is a satellite-based macroeconomic forecasting company that provides real-time macro intelligence for decisions that move markets. ROY (Remote Orbital Yield) and JACK (Joint Algorithm for Containerized Knowledge) are Atlas’s proprietary algorithms powered by satellite imagery, AI/ML, and computer vision.

This article was written with valuable assistance from Morgan Reppert, Chief of Staff, and Aditya Murlidharan, Economic Analytics & Executive Operations Fellow for Atlas Analytics.

Each year, trillions of dollars in global trade move through ports before that activity appears in official economic data.

Containers are unloaded, sorted, stored, and shipped onward weeks before economists, investors, and policymakers have a complete read on what those movements mean for the economy and the market.

Ports are more than container distribution hubs. They are physical gateways into the global economy, supporting containerized trade, energy transport, industrial supply chains, tourism, and offshore operations. Making maritime activity one of the clearest places to observe economic demand as it is happening, rather than after it has already been reported.

At Atlas Analytics, this raised a simple question: if satellites can help measure the physical economy in real time, can they also measure the maritime economy through port activity?

That is why we built JACK.

Meet JACK

Our proprietary algorithm, ROY, gave Atlas a way to measure Core GDP by observing the domestic physical economy from space. JACK extends that approach to the maritime and trade economy working in tandem with ROY.

Net Exports are a critical component of GDP, but they remain difficult to measure in real time. Official trade data often arrives after the underlying activity has already happened, leaving you with a delayed view of one of the economy’s most important moving pieces.

JACK, short for Joint Algorithm for Containerized Knowledge, was built to close that gap.

Using AI/ML, satellite imagery and computer vision, JACK monitors container activity across major U.S. ports in real-time and translates those observations into earlier estimates of imports, exports, trade value, and the Net Exports contribution to GDP. For decades, economists have attempted to estimate international trade using surveys, customs reports, shipping manifests, and delayed government releases.

But trade is fundamentally a physical process:

• Ships depart and arrive

• Containers move

• Ports expand and contract

These movements are observable phenomena.

How JACK Works

To understand the value of port-level measurement, consider the combined exports moving through Los Angeles and Long Beach, two of the most important container gateways in the U.S.

The chart above compares reported export activity with Atlas’s JACK-derived estimates. The goal is not simply to count containers. It is to determine whether visible port activity can be translated into an economic signal: how much trade is moving, in what direction, and with what implied value.

That relationship is the foundation of JACK.

The shipping container, an engineering accomplishment in and of itself, has allowed for widespread, efficient, standardized transport of goods over the last 50 years.

Shipping activity is commonly measured in 20ft Equivalent Units, or TEUs, the standardized unit used to describe container volume. Because containerized trade is standardized across ports, changes in container density and movement can become useful signals for estimating trade activity.

This leaves two important pieces of information for JACK:

1. Physical signatures through the number of containers loading and unloading on the docks;

2. Real dollar value of goods every week, quarter, and year.

Every port is different, whether its vehicles through Baltimore, electronics through LA and Long Beach, or appliances and airplane components through Charleston. JACK builds a statistical profile of dollars moving through ports, first by counting the number of TEUs coming and going and second by translating those containers full of goods into dollars. Each port tells us something about the nation as a whole. And when we look at the largest contributors, we can model the underlying value of exports and imports.

Because shipping containers are internationally standardized, JACK’s approach can extend beyond U.S. ports over time. As more ports come online, Atlas can build a broader real-time picture of trade activity across countries and regions.

And from there, JACK is designed to estimate the Net Exports contribution to GDP in near real time.

What Port-Level Signals Unlock

The U.S. is one of the world’s largest trading nations, with nearly $6 billion in annual exports and imports of goods and services. A meaningful share of that activity moves through ocean ports, where containers, vessels, and terminal operations create a real-time record of economic demand before that demand appears in official data.

Most maritime data tells users where ships are. JACK is designed to answer a different question: what is happening to the cargo, what impact does that cargo have in supply chains, and what does that activity imply for trade flows, trade value, and GDP?

That distinction matters. A ship’s location can tell you that goods are moving. Container activity can help reveal whether imports are accelerating, exports are weakening, inventory is building, or trade patterns are shifting across ports and regions.

• For macro investors, that can provide an earlier read on growth, inflation pressure, and trade-driven changes in GDP.

• For logistics and supply-chain teams, it can improve visibility into cargo movement and port-level congestion.

• For enterprises, asset owners, and diligence teams, it can offer a faster view into physical economic activity in markets where reported data is delayed, fragmented, or incomplete.

This is where JACK and ROY become especially powerful together. ROY measures the domestic physical economy. JACK measures the trade economy moving through ports.

Together, they give Atlas a fuller real-time view of GDP, built from observable changes in the physical world.

From Signal to Actionable Insights

In our recent Q1 2026 GDP release, when Atlas forecasted 2.0% GDP growth exactly in line with the first official estimate, we introduced JACK, our second algorithm.

Today, JACK analyzes satellite imagery from three of the largest U.S. ports by volume: Los Angeles, Long Beach, and Newark. Built on Atlas’s patent-pending computer vision and machine learning system, JACK detects and counts TEU activity moving through major port infrastructure.

If your organization needs a faster way to understand trade flows, port activity, or the Net Exports contribution to GDP, we would welcome the opportunity to connect.

Get in Touch

This article was written with valuable assistance from Morgan Reppert, Chief of Staff for Atlas Analytics.

Five months ago, we wrote about an emerging change to the macroeconomic regime.

At the time, the overwhelming market consensus was straightforward: the economy was slowing, inflation was fading, and the Federal Reserve would soon be forced to lower interest rates.

We believed this conventional thesis was wrong.

Instead, Atlas argued that the U.S. economy remained significantly stronger than consensus expectations and that inflation would likely prove far stickier than markets anticipated. In our view, real economic growth was simply too resilient to justify the aggressive rate-cut expectations embedded in long-duration bonds.

Within the Atlas model portfolio, we established a short position on long-duration U.S. Treasuries through put options on the iShares 20+ Year Treasury Bond ETF (TLT). Specifically, we initially purchased 25 July 2026 $86 puts in January before later rolling the position higher into July 2026 $88 puts as the thesis continued to strengthen.

Since then, long-duration Treasuries have repriced sharply, reinforcing the broader macro signal Atlas identified earlier in the year.

Atlas Analytics TLT Positioning (January – May 2026)

The 30-year Treasury yield has now surged to its highest level in approximately twenty years, while our remaining TLT put position is currently up roughly 100% with nearly two months remaining before expiration. We have already realized gains on a portion of the position while maintaining exposure to what we believe may still be an ongoing structural repricing in the bond market.

Importantly, however, this was never merely a tactical trade.

What we are witnessing may instead represent something much larger: a fundamental shift in the macroeconomic regime itself.

The Market Expected a Slowdown

To understand why this move has been so significant, it is important to recall where consensus expectations stood only a few months ago.

Markets entered the year heavily positioned for economic weakness. Investors broadly expected slowing growth, rapidly declining inflation, and multiple Federal Reserve rate cuts.

This narrative seemed intuitive.

After all, interest rates had risen at one of the fastest paces in modern history. Conventional macroeconomic thinking suggested that such aggressive tightening would inevitably “break” the economy, forcing policymakers to reverse course.

Long-duration bonds rallied aggressively as investors priced in this outcome.

Yet beneath the surface, the underlying economy continued to exhibit remarkable resilience.

Consumption remained firm. Labor markets stayed tight. Fiscal spending continued supporting nominal activity. And many of the real-economy indicators we monitor at Atlas suggested that economic momentum remained substantially stronger than traditional sentiment indicators implied.

The market was pricing a recessionary slowdown.

But the physical economy was signaling something very different.

What Atlas Forecasted

That signal mattered because it had direct implications for rates. If nominal growth was more resilient than consensus believed, then inflation would likely prove harder to extinguish, rate-cut expectations were probably too aggressive, and long-duration bonds were vulnerable to repricing.

Atlas expressed that view through a short position in long-duration U.S. Treasuries via TLT puts. The trade was not the thesis itself. It was one expression of a broader macro framework built on stronger physical-economy signals, sticky nominal growth, and a likely repricing of long-term rates.

Rather than relying exclusively on lagged surveys or sentiment data, our proprietary algorithms use satellite imagery and alternative datasets to track physical economic activity in near real-time. Construction activity, industrial development, land use changes, infrastructure expansion, and trade flows all provide signals about the true underlying pace of the economy.

And critically, those signals remained far stronger than consensus expectations throughout the beginning of 2026.

In fact, as early as January 2nd (when our first satellite collections of the year began orbiting) our internal Atlas models were pointing toward 2.0% real GDP growth for Q1 2026, well above the increasingly pessimistic market narrative that was taking hold at the time.

Over the following months, many of these physical-economy signals remained persistently firm. Consumption activity, infrastructure expansion, industrial development, and broader measures of nominal activity continued suggesting that the economy was not weakening nearly as rapidly as markets believed.

This distinction proved critical.

If nominal growth remains resilient, inflation can become harder to fully extinguish, and long-term rates may need to reprice. That repricing can reflect several forces at once: stronger growth, stickier inflation, fiscal deficits, Treasury supply, and a higher term premium.

Four months later, when the Bureau of Economic Analysis officially released the Q1 GDP figure, the economy printed at exactly 2.0% growth, precisely in line with our forecast generated months earlier from satellite-based economic monitoring.

In our view, markets had become anchored to the macroeconomic assumptions of the 2010s: low inflation, low growth, low interest rates, and abundant global liquidity.

But the post-pandemic economy increasingly appears governed by a very different set of dynamics.

Why Long-Duration Bonds Became Vulnerable

Long-duration bonds are extraordinarily sensitive to changes in interest rates.

When yields rise, bond prices fall. And the longer the duration of the bond, the more severe that price decline becomes.

This is precisely why TLT became such an attractive expression of our thesis.

The ETF holds long-dated U.S. Treasury securities whose valuations are highly sensitive not only to Federal Reserve policy expectations, but also to long-term assumptions about inflation, economic growth, fiscal sustainability, and term premium.

As the market gradually realized that inflation might remain elevated and that economic activity was not slowing as dramatically as anticipated, long-term yields began repricing aggressively upward.

The result was a sharp decline in long-duration bond prices.

Importantly, this repricing may not simply reflect short-term Federal Reserve dynamics.

It may instead represent the bond market beginning to adjust to a structurally different economic environment altogether.

The End of the Post-2008 Regime?

For nearly fifteen years following the Global Financial Crisis, markets operated within an unusually stable macroeconomic regime.

Inflation remained subdued. Globalization exerted persistent disinflationary pressure. Central banks maintained extraordinarily accommodative policies. And interest rates remained historically low across much of the developed world.

That environment may now be changing.

Today’s economy increasingly appears characterized by:

• Larger fiscal deficits

• Persistent government spending

• Deglobalization and supply-chain restructuring

• Industrial policy and reshoring

• Higher structural inflation volatility

• Tight labor markets

• Elevated geopolitical fragmentation

Collectively, these forces may produce a world in which nominal growth and inflation remain structurally higher than investors became accustomed to during the 2010s and what Larry Summers termed “secular stagnation.”

If so, long-term interest rates may also need to remain structurally higher.

This would carry profound implications not only for bonds, but for virtually every major asset class.

Importantly, a structurally higher-rate environment does not necessarily imply economic collapse or runaway inflation. It may simply reflect a world characterized by stronger nominal growth, persistent fiscal expansion, and higher equilibrium interest rates than investors became accustomed to during the post-2008 era.

Why This Matters Beyond Bonds

The long end of the Treasury market serves as the foundation for the global financial system.

When 30-year Treasury yields rise meaningfully, the effects ripple throughout the broader economy.

Mortgage rates increase. Corporate borrowing costs rise. Equity valuation multiples compress. Commercial real estate faces additional pressure. Venture capital and private equity become more constrained. Government financing costs expand rapidly.

In many ways, higher long-term yields represent a repricing of the future itself.

And this is precisely why we believe the current bond market move deserves close attention.

The market may no longer be operating under the assumptions that defined the post-2008 era.

Critical Questions Remain:

• Will inflation continue proving stickier than expected?

• Can the Federal Reserve tolerate structurally higher long-term yields?

• Will fiscal deficits continue exerting upward pressure on term premium?

• Is the bond market beginning to recognize that the macroeconomic regime itself has fundamentally changed?

These questions will define the next phase of the macro cycle. And as this year has shown, the market narrative can move in one direction while the physical economy points in another.

Investors need faster ways to understand whether the economy is actually weakening, or whether consensus is once again looking in the wrong place.

That is the core of Atlas’ work: using satellite imagery, alternative data, and machine learning to measure real-world economic activity as it is happening, not months later.

If your team is thinking through what a changing macro regime means for portfolios, policy, or capital allocation, we would welcome the conversation.

Get in Touch

This past week, Our Founder and CEO, Jake Schneider, joined the Balerion Space Ventures podcast to discuss the idea at the heart of Atlas Analytics: using satellites, computer vision, and AI to measure the economy in real time.

The conversation was a natural fit with Balerion’s focus on emerging companies shaping the space economy. Atlas fits that thesis directly by combining satellite imagery, AI, and financial market intelligence to measure real-world economic activity as it happens. At its core, Atlas is tackling one of macro’s most outdated questions:

How do we measure the economy as it is happening, rather than waiting for delayed government reports and traditional economic forecasting?

The idea has roots in a simpler use case. More than a decade ago, hedge funds began using aerial imagery to count cars outside Walmart stores. More cars meant more shoppers. More shoppers meant stronger sales. Stronger sales meant the stock might move.

It was a simple idea, but a powerful one: physical activity leaves observable economic signals.

Jake first came across that story while working for former Federal Reserve Chairman Alan Greenspan. At the time, he was helping build macroeconomic forecasts using government data, anecdotal inputs, and real-time observations from across the economy. The work was rigorous, but the data was still delayed.

That raised a bigger question:

If satellite imagery could help investors understand activity at a single retailer, why couldn’t similar techniques be used to measure broader economic activity in real time?

Welcome Atlas Analytics to the Stage

Today, Atlas uses proprietary algorithms combining satellite imagery, computer vision, and AI/ML to forecast GDP ahead of official government releases. Satellites circle the planet roughly every 90 minutes, capturing changes in the built environment: construction activity, port movement, land use, vegetation, logistics hubs, and other physical signals of economic activity.

Those signals are then translated into a weekly GDP estimate.

Not quarterly. Weekly.

That matters because GDP remains one of the most important numbers in the global economy, but it arrives late. The first official estimate comes after the quarter ends, with revisions continuing over the following months. Subsequent revisions follow over the next several months. By the time the final number is available, markets, investors, and policymakers have already moved on.

The Gap

On the podcast, Jake described this as a shift from traditional macro forecasting toward real-time economic measurement. The goal is not simply to improve a model. It is to change the timing of the information itself.

That distinction is important.

Most macro data tells us what already happened. Atlas is focused on what is happening now.

The practical implications are significant. For investors, earlier GDP signals can inform portfolio tilt, ETF exposure, fixed income positioning, foreign exchange trades, and broader macro regime analysis. For companies, the same signals can help with inventory planning, capital expenditure decisions, and market expansion strategy. For governments, they can support economic development, bond issuance, and policy decisions.

Atlas’ recent forecasts provide an early example of what this approach can deliver. Four days before the official government release, Atlas published its GDP forecast publicly on Substack: 2%. The Bureau of Economic Analysis later reported 2%.

As Jake put it on the podcast:

“Whereas macroeconomic forecasting was in the Middle Ages, it is now at a renaissance, a golden era where we finally have the tools to see the economy as it actually is, in real time.”

The Core of the Atlas Thesis

Better measurement does not just mean better forecasts. It means better decisions.

Faster signals can help investors allocate capital more intelligently, help policymakers respond earlier, and eventually help countries with limited statistical capacity understand their own economies with more precision.

Watch the full conversation to hear how Atlas is helping investors generate alpha and using satellite intelligence to rethink macroeconomic measurement in real time.

Learn More

Balerion Space Ventures is an early-stage venture firm focused on the New Space Economy, investing in companies building the infrastructure behind launch, Earth observation, propulsion, defense, communications, and other dual-use space technologies. They are based in Dallas, Texas and you can subscribe to their podcast here.

The U.S. dollar has quietly held firm in 2026.

Markets entered 2026 expecting a softer U.S. dollar driven by slowing growth and eventual Federal Reserve easing. But that macro setup has not materialized.

Instead, the dollar has remained resilient, and Atlas’ real-time macro signals suggest the divergence between market expectations and underlying economic conditions is growing.

If growth stabilizes while inflation remains structurally elevated, the current dollar strength may not be temporary. It may be the early stage of a broader breakout.

A Different Lens on FX: Policy, Not Just Price

Foreign exchange is often framed through spot movements, positioning, or technicals. But at its core, currency valuation is driven by macro fundamentals, particularly the interaction between growth, inflation, and policy.

Growth, inflation, and policy are the three core inputs into FX and increasingly, we’re focused on how real-time signals can improve that framework.

One way to capture this relationship is through an “optimal policy rate” framework—a Taylor Rule-style approximation that combines inflation and GDP growth into an implied policy stance.

This provides a simple but powerful lens:

• When the implied policy rate rises → currencies tend to strengthen

• When it falls → currencies tend to weaken

In other words, FX is not just about where rates are—but where they should be.

Why This Matters Now

Atlas’ real-time GDP signals suggest that:

• U.S. growth is stabilizing rather than deteriorating

• Inflation, while moderating, remains structurally elevated relative to pre-2020 norms

Put together, this implies:

• The “true” policy stance may not ease as quickly as markets expect

• The relative macro backdrop continues to favor the U.S. over Europe

Historically, this combination has been supportive of a stronger dollar.

What the Model Is Saying

Using a Taylor Rule approximation of U.S. macro conditions, we can construct an implied policy path and map that to EUR/USD.

• The blue line represents the actual EUR/USD spot rate

• The red line reflects the model-implied level based on optimal U.S. policy

What stands out in the chart above is the growing divergence between current EUR/USD pricing and where macro fundamentals imply it should trade based on U.S. growth and inflation conditions.

The model suggests that:

• The dollar remains modestly undervalued relative to macro fundamentals

• More importantly, the direction of the optimal policy signal is turning higher

This is a subtle but critical shift, and one that typically precedes meaningful moves in FX.

Timing the Move

The key takeaway from the model is not just valuation, but timing.

Based on current trajectories:

• The optimal policy signal points to a breakout in the dollar later this summer into early fall

• The current YTD strength may represent the early stages of that move, rather than its conclusion

What we are seeing now may not be a peak, but the beginnings of a base.

Revisiting the USD–EUR Trade

We’ve highlighted the USD–EUR dynamic before, and the core logic remains intact:

• The U.S. continues to exhibit relative growth resilience

• Policy expectations may be too dovish relative to underlying data

• Europe faces a more constrained growth backdrop

What’s changed is that the macro signal is beginning to re-accelerate in favor of the dollar.

Market Implications

If U.S. growth stabilizes while inflation remains above pre-2020 norms, the Federal Reserve may be forced to keep policy tighter for longer than markets currently expect.

That matters for FX because currencies respond less to absolute rate levels and more to changes in relative policy expectations. A repricing toward higher-for-longer U.S. rates would likely increase global demand for dollar-denominated assets, supporting further dollar appreciation against the euro.

If this view plays out, a stronger dollar would have broad implications:

• Commodities → potential headwind

• Emerging markets → tighter financial conditions

• Multinationals → earnings pressure via FX translation

• Risk assets → modest drag at the margin

As always, the dollar sits at the center of the global macro system.

Final Thought

The dollar doesn’t need a dramatic catalyst to move: just a shift in expectations.

Right now, markets appear to be leaning toward a softer policy path and a stable-to-weaker dollar. But if growth holds and inflation proves sticky, that narrative may need to adjust.

The dollar has already begun to move. The question is whether this is simply resilience, or is the early stages of a broader breakout.

The setup suggests that the next meaningful move in the dollar may not be lower.

It’s higher.

Stay Ahead of the Macro Curve

At Atlas Analytics, we track the economy in real time using satellite imagery and machine learning to anticipate shifts in growth, inflation, and policy before they appear in traditional data.

Each week with our clients, we share:

• Real-time GDP updates through ROY

• Trade and net export signals through JACK

• Actionable macro insights across equities, rates, and FX

If you’re interested in understanding where the macro environment is heading, not where it’s been, subscribe below to stay ahead of the curve.

For institutional access or partnership inquiries, feel free to reach out directly.

Contact Us

Atlas Analytics is a satellite-based macroeconomic forecasting company. ROY (Remote Orbital Yield) is Atlas’s proprietary GDP measurement signal. JACK (Joint Algorithm for Containerized Knowledge) is currently in active development.

On Friday, the Bureau of Economic Analysis released its advance estimate for Q1 2026 U.S. GDP.

At Atlas Analytics, we finalized our forecast on April 12th, nearly three weeks ahead of the release, using our satellite-based models, and we’ve been providing this estimate to our clients weekly beginning on January 2.

The results:

Headline GDP

Atlas Forecast: +2.0% | BEA Release: +2.0%

Core GDP

Atlas Forecast: +2.8% | BEA Release: +2.9%

Private Inventories Contribution

Atlas Forecast: +0.3pp | BEA Release: +0.4pp

Net Exports Contribution

Atlas Forecast: -1.1pp | BEA Release: -1.3pp

The Forecast — Published Before the Official Release

Three weeks ago, we published our Q1 GDP estimate using Atlas’ satellite-based models.

Here is the original video:

At the time, Atlas forecasted:

• +2.0% Headline GDP

• +2.8% Core GDP

On Friday, the BEA release came in at:

• +2.0% Headline GDP

• +2.9% Core GDP

We will continue publishing forecasts ahead of official data releases, with transparency and consistency in real time.

The Big Picture

This was a strong validation of the Atlas approach.

Not just because we got close to the final number, but because we captured the structure of the economy:

• Where growth was coming from

• Where it was being offset

• And how those components ultimately shaped headline GDP

That is the broader objective:

Not just forecasting the number itself, but understanding the economic forces driving it in real time.

Core GDP: The Signal Was Clean

Our Core GDP estimate (defined as consumption, government spending, and fixed investment) came in at +2.8% vs. 2.9% actual.

This continues a pattern we’ve seen repeatedly:

When you strip out the noise, the underlying economy tends to be more stable, and more predictable, than headline GDP suggests.

ROY, our core GDP model, performed as intended by:

• Tracking underlying economic activity

• Filtering out volatility from inventories and trade

• Providing a cleaner signal for markets

The Drag: Net Exports

The primary source of weakness in Q1 was net exports, which subtracted -1.3 percentage points from growth.

Atlas estimated -1.1pp, directionally consistent with the final release, though still the largest source of forecast variance.

This is consistent with prior quarters:

Net exports remain the hardest component of GDP to forecast in real time.

They are influenced by:

• Global demand

• Supply chain dynamics

• Currency movements

• High-frequency trade flows that are difficult to observe directly and consistently

This challenge is precisely why Atlas has been developing JACK, our model focused on port activity.

Inventories: Small but Meaningful

Private inventories contributed +0.4pp to Q1 GDP growth, slightly above our +0.3pp estimate.

Inventories are:

• Volatile

• Frequently revised

• And capable of shifting headline GDP in meaningful ways

Encouragingly, the Atlas estimate remained well within range despite that volatility.

What This Means for Markets

Stepping back, this release reinforces a few key themes:

1️⃣The U.S. economy remains stable

Core growth near ~3% is consistent with a moderate expansion, not a slowdown.

2️⃣Headline GDP can mislead

A 2.0% print masks a stronger underlying economy once you adjust for trade drag.

3️⃣Real-time data is closing the gap

Forecasting GDP weeks in advance with this level of precision was not possible a decade ago.

Why This Matters

GDP is one of the most important indicators in global markets, yet it also has meaningful limitations.

It is:

• Delayed

• Subject to several revisions

• Often misinterpreted in real time

At Atlas, our objective is straightforward:

bring GDP forward,

make it more interpretable,

and make it more actionable for investors and decision-makers.

This release represents another step in that direction.

Looking Ahead

We are currently tracking Q2 2026 GDP at approximately +2.3%, with:

• Stable core growth

• Continued noise from trade

• Evolving sector-level dynamics

We’ll continue to update our forecasts weekly for our clients.

Final Thought

Real-time macroeconomic forecasting is not about achieving mathematical perfection.

It is about generating earlier, clearer, and more actionable insight into the direction and structure of the economy before official data becomes available. This release was another meaningful validation of that approach.

If you are interested in accessing Atlas forecasts, data, or learning more about our work, we welcome the conversation.

Connect Now

Atlas Analytics is a satellite-based macroeconomic forecasting company. ROY (Remote Orbital Yield) is Atlas’s proprietary GDP measurement signal. JACK (Joint Algorithm for Containerized Knowledge) is currently in active development.

The U.S. economy isn’t breaking. But it is changing.

Beneath the surface, the composition of growth is shifting. And from space, that shift is visible in real time.

Last quarter, Atlas saw a wider-than-usual gap between our estimate and the BEA release. We’ve reviewed it closely with clients and are sharing the same perspective here.

These moments matter. They show where the model is working, and where it needs to adapt.

What Happened

Our core system, ROY, held up directionally.

Atlas estimated Core GDP at +2.35 percentage points versus +0.57 from the BEA.

A meaningful portion of that gap, roughly one percentage point, came from a government spending drag tied to the federal shutdown. This introduced a temporary distortion that the current model does not fully capture.

But the real story was Net Exports.

The BEA reported roughly -0.2 percentage points. Atlas estimated +2.1.

That’s a gap of more than two percentage points, and it’s where we’ve focused our work.

What We’re Fixing

Two changes are now underway.

First, we’re upgrading ROY to better handle macro shocks, things like shutdowns or abrupt fiscal changes that can distort the signal.

Second, we’re introducing something new: JACK.

JACK (the Joint Algorithm for Containerized Knowledge) uses computer vision on satellite imagery to track container movement across major U.S. ports. Nearly 80% of U.S. trade flows through those ports.

For the first time, we’re not inferring trade. We’re measuring it directly, from space.

JACK will scale further in Q2, running alongside our existing Net Exports framework.

The Q1 Forecast

Recent quarters have been volatile.

Q3 came in at +4.4%.
Q4 slowed to around +0.5%.

For Q1, Atlas sees a more balanced picture:

Within our framework:
Core GDP + Net Exports + Inventories = Headline GDP.

What the data shows is stabilization, but with a shift underneath.

Domestic activity is holding up.

Net Exports remain a drag, the component we’re now measuring more directly.

What Comes Next

With new investors onboard, we’re accelerating across three fronts:

Technology — A beta client portal is in development, providing real-time access to Atlas data.

Product — JACK is expanding. More ports. Better coverage. Higher-frequency trade signals.

Platform & Team — We’re scaling both the system and the team behind it.

From day one, the goal has been simple:

We’re not forecasting the economy.

We’re measuring it.

Directly, From space.

And we’ll keep refining that measurement, quarter by quarter.

Let's Connect

Atlas Analytics is a satellite-based macroeconomic forecasting company. ROY (Remote Orbital Yield) is Atlas’s proprietary GDP measurement signal. JACK (Joint Algorithm for Containerized Knowledge) is currently in active development.

At Atlas, we focus on where the economy is going next, not where it’s been.
That’s the edge.

Most investors are reacting to the macro environment. They are not anticipating it.

They wait for:

• Official GDP releases

• Lagging economic indicators

• Consensus forecasts

By the time the regime is “confirmed,” markets have already moved.

This is why macro investing often feels frustrating:

You’re right… just too late.

The problem is not understanding the macro environment.
It’s timing it.

Where Atlas Fits In

This is where Atlas Analytics changes the equation.

Atlas is a data and signal platform designed to measure economic activity in real time, not months after the fact.

Our models, particularly ROY, translate those signals into shifts in growth and inflation in real time.

That allows us to answer a much more valuable question:

Not “what regime are we in?”
But “what regime are we entering?”

That distinction is everything.

In practice, this means focusing on changes in the trajectory of growth and inflation, not just their levels.

From Signal to Trade

What does this actually look like in practice?

Frequent readers will recall that in January, we flagged a shift in the macro environment.

Using our forward-looking GDP forecasts, we saw:

• Economic activity was accelerating relative to consensus

• Inflation was proving structurally stickier than expected

This combination pointed to a clear conclusion:

This was not an environment for rate cuts.

It was an environment consistent with higher-for-longer, or even hikes.

And we positioned accordingly.

Those signals translated into positioning:

• Short long-duration bonds

• Long the U.S. dollar

The result:

• iShares 20+ Year Treasury Bond ETF (TLT) has fallen ~5% since its late-February peak

• The U.S. dollar has appreciated ~1% YTD

The Atlas Model Portfolio, which reflects how these signals translate in practice, is up approximately +40% YTD, while the NASDAQ is up just ~3.5%.

This wasn’t a discretionary call. It was a direct output of the model’s forward-looking signal.

This is the essence of trading the macro regime:
Identify the shift before it’s visible in official data, position ahead of consensus adjusts, and let the market reprice around you.

The Bigger Game: The Macro Regime

Markets aren’t just mispriced. They also move through regimes.

Periods where the underlying economic environment creates consistent winners and losers.

Think:

• Strong growth vs. weak growth

• Inflationary vs. disinflationary

• Expansion vs. slowdown

Each of these environments rewards a different set of assets.

And crucially:

These regimes persist long enough to be traded.

A Quick Reminder

As a refresher, arbitraging the deviation between fundamental value and market sentiment means identifying when markets are mispriced relative to the underlying economy.

In short, we use our forward-looking GDP estimates to determine where assets should be trading and then compare that to where they are trading.

When there’s a gap, we step in.

But that’s only half the story.

Bringing It Together

The Atlas Model Portfolio is built on two distinct sources of alpha:

• Arbitraging the deviation between fundamental value and market sentiment

• Trading the macro regime

The combination of these two approaches, exploiting mispricing and anticipating the macro environment, is what has driven that performance.

One is about valuation.
The other is about direction.

Together, they allow investors to generate alpha across environments, not just when markets are obviously wrong.

Macro doesn’t have to be reactive. It can be traded proactively, if you have the data.

Final Thought

Most investors are trying to answer:

“What just happened?”

We’re focused on a different premise:

What’s about to happen and how do we position before it does.

That’s the edge.

What’s Next

Next week, Atlas will go public with the data we’ve been sharing privately with clients over the past three months:

Our Q1 2026 GDP estimate.

Curious?

Tune in next week for our YouTube release breaking down the data and what it means for markets.

In the meantime, if you’re interested in using Atlas data to inform your investment strategy, feel free to reach out.

Contact Us

Nearly a year ago, in A Tale of Two Prices, I drew a distinction between what an asset is worth and what it trades for.

That gap still exists, and, in recent weeks, it has widened.

Equity markets continue to react violently to geopolitical headlines and macro noise: conflict escalation, temporary ceasefires, oil price swings, policy speculation. Prices move quickly.

Fundamentals do not.

The result is a persistent dislocation between market sentiment and economic reality.

For example, this week, equities rallied approximately +3.5% in a single day on ceasefire headlines, while Atlas Core GDP declined by roughly -0.6% on a weekly basis.

In theory, this shouldn’t happen.

The intrinsic value of an equity is simply the discounted value of its future cash flows. In efficient markets, price should be a close approximation of that value.

In practice, it rarely is.

Not because investors misunderstand the theory, but because they cannot reliably forecast the inputs.

Projecting earnings even one year forward is difficult. Five years forward is guesswork. As a result, price targets diverge wildly across institutions, each built on different assumptions, models, and narratives.

Some will be directionally right.

None will be precisely correct.

Faced with this uncertainty, markets default to something easier: trading sentiment.

Oil spikes? Sell equities.
Ceasefire headlines? Buy risk.

These reactions are fast, intuitive, and almost entirely untethered from the magnitude of their impact on underlying cash flows.

That is the inefficiency.

The solution is to anchor valuation not to narratives, but to the underlying driver of aggregate earnings: GDP.

Calculating Fundamental Value Based on GDP

There is a reason many investors dismiss macro.

At the level of a single company, they are often right to do so.

The dominant risk to any individual equity is idiosyncratic:
balance sheet strength, management execution, competitive dynamics, capital allocation.

Macro matters—but it is often second order.

A weak company can fail in a strong economy.
A strong company can outperform in a weak one.

This is the foundation of bottom-up investing.

But something important happens when you move from a single stock to a basket of equities.

Idiosyncratic risk diversifies away.

The probability that one company’s earnings miss drives the entire S&P 500 lower is extremely small. Across hundreds of companies, firm-specific shocks cancel out.

What remains is the common factor.

Macroeconomic risk.

At the index level, earnings are no longer primarily a story of individual companies. They are a story of the economy itself.

And the most direct measure of that economy is GDP.

As the chart above shows, the correlation between GDP growth and the SPDR S&P 500 ETF Trust (SPY) is approximately 0.46—a statistically meaningful and economically intuitive relationship.

This is the key insight:

If aggregate earnings are a function of economic activity, and economic activity is captured by GDP, then equity indices should exhibit a systematic relationship to GDP over time.

That relationship can be measured.

Using historical data, we can estimate an econometric model linking GDP growth to index-level price movements. In its simplest form, this is a regression: mapping changes in GDP to changes in equity prices.

The output is not a narrative.

It is a mathematical estimate of fundamental value.

Once that relationship is established, we can input forward-looking GDP estimates—rather than backward-looking sentiment—to generate implied price targets for macro-exposed equities.

And this is where the opportunity emerges.

Markets trade on headlines, positioning, and emotion.
Fundamentals evolve more slowly, and more predictably.

When the market price diverges meaningfully from the GDP-implied fundamental value, that gap can be systematically arbitraged.

That is the foundation of the Atlas approach.

A Common Objection

I hear the same objection frequently from fundamental investors:

“We’re a bottoms-up shop. We don’t use GDP.”

My response is always the same:

You may not model it explicitly.
But you are trading it, whether you realize it or not.

Creating Price Targets By Inputting Credible GDP Forecasts

Estimating the historical relationship between GDP and equity prices gives us a framework.

But the real edge comes from what we input into it.

Most market participants rely on lagged and revised macroeconomic data.

GDP is released with a delay.
It is then revised, usually multiple times, over the following months.

By the time the “truth” is known, the market has already moved.

In effect, investors are attempting to price the present using an imperfect view of the past.

At Atlas, we take a different approach.

We generate real-time estimates of economic activity using satellite imagery and machine learning.

Our Core GDP signal is designed to capture the underlying momentum of the economy as it is happening, not as it is later reported.

This distinction is critical.

Because once the relationship between GDP and equity prices is established, even modest improvements in GDP estimation translate directly into more accurate estimates of fundamental value.

Instead of asking:

What did GDP look like?

We ask:

What does GDP look like right now—and where is it going?

By inputting these forward-looking GDP estimates into our model, we generate implied price targets for macro-exposed equity indices.

These estimates are not driven by headlines.
They are not driven by positioning.

They are driven by the underlying trajectory of the economy itself.

And when those estimates diverge meaningfully from market prices, the opportunity becomes clear.

The Result: The Atlas Model Portfolio

This framework allows us to move from theory to execution.

When market prices deviate meaningfully from GDP-implied fundamental values, we take positions to capture the convergence.

When equities trade below fundamental value, we go long.
When they trade above it, we reduce exposure or position short.

The portfolio is not constructed around narratives or forecasts of sentiment.

It is constructed around measurable economic reality.

In an environment increasingly driven by short-term reactions and macro headlines, that discipline matters.

The results have been compelling and increasingly difficult for even the most committed “bottoms-up” investors to ignore.

As the graphic above shows, as of the close of market on Wednesday, the Atlas Model Portfolio is up approximately ~40% year-to-date, compared to a broader market that is down roughly 2% over the same period.

Markets may trade on sentiment in the short run.

But over time, fundamentals assert themselves.

If you’d like access to our forward-looking GDP signals and model-implied price targets, we’re beginning to share a limited set of these insights with partners. Feel free to reach out.

Let's Connect

Most discussion around AI is still showing up in surveys, sentiment, and earnings calls. That’s not where technological shifts show up first.

The more relevant question is whether this is already observable in the data, rather than waiting for it to appear in lagging indicators.

The 1996 Parallel

The late-1990s productivity boom didn’t look obvious at the start. Computers had been around for decades. The internet wasn’t new. But around 1995–1996, productivity growth began to accelerate meaningfully as firms learned how to reorganize around new technologies. It wasn’t the invention that mattered most. It was the deployment.

In a recent discussion with Cliff Waldman and Dan Bachman, we revisited this period and how long it took markets and policymakers to recognize the scale of the shift.

That distinction matters today, because AI appears to be following a similar adoption curve.

The Productivity Data Is Turning

The chart below tells the story.

After more than a decade of post-Global Financial Crisis stagnation, U.S. productivity growth is beginning to re-accelerate. The 10-year moving average has turned higher for the first time since the early 2000s recovery.

We’re still well below the peaks of the late 1990s, but the trend has clearly shifted and the data is beginning to reflect it.

Why AI’s Impact Appears First in Physical Data

When economists try to measure technological change, they often look in the wrong places:

• Business surveys

• Hiring intentions

• Capex guidance

• Earnings commentary

But technological revolutions show up first in deployment infrastructure, not opinions.

This is where observational data becomes powerful.

From space, through geospatial measurement, we can already see AI’s footprint expanding in real time.

Deployment > Invention

This brings us back to the 1996 analogy.

The late-90s productivity surge didn’t happen when computers were invented.

It happened when:

• Firms reorganized workflows

• Supply chains digitized

• Communication costs collapsed

• Information became real time

AI is entering that same deployment phase today.

We’re moving from experimentation to integration.

Why This Matters for GDP

As I stated in our interview with Dan and Cliff, productivity (particularly Total Factor Productivity or “TFP”) is the single most important long-run driver of economic growth.

If AI is lifting output per worker, several macro implications follow:

• Higher potential GDP

• Stronger corporate margins

• Lower unit labor costs

• Delayed disinflation in services

• Higher neutral rates (r*)

The risk is not missing the level, but misreading the timing, as productivity gains tend to appear with a lag in official data.

The Observational Economy vs The Survey Economy

Traditional macro measurement is lagged by design. Productivity data itself is revised for years. By contrast, the physical economy updates daily.

By observing:

• Construction activity

• Energy utilization

• Industrial throughput

• Logistics flows

We can identify early signs of technological deployment in real time.

Including increases in power demand and data center construction are already visible in key regions, which historically precede shifts in reported output and productivity.

More on measuring construction in near real-time from outer space here.

Are We in 1996?

It’s too early to declare a full productivity renaissance.

But the ingredients are familiar:

• Breakthrough general-purpose technology

• Massive infrastructure buildout

• Corporate workflow reorganization

• Capital deepening cycles

The late-90s boom didn’t look obvious at the start either.

It looked like a slow turn higher in the data.

Exactly like today.

Closing Thought

AI’s economic impact won’t arrive all at once.

It will compound quietly through:

• Compute infrastructure

• Energy demand

• Industrial production

• Logistics optimization

Long before it shows up cleanly in GDP releases.

The productivity story of the next decade may already be underway.

By the time this shows up clearly in GDP data, the adjustment will already be underway.

Interested?

If you’re interested in how satellite and geospatial data can track AI infrastructure, industrial activity, and productivity shifts in real time, feel free to reach out or subscribe below.

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TLDR: A shift from Powell to Warsh signals a move toward tighter, more preemptive Fed policy, with direct implications for rates, asset prices, and portfolio positioning.

As Jerome Powell’s time as Chair of the Federal Reserve comes to an end on May 15, the anticipated confirmation of nominee, former Fed Governor Kevin Warsh, marks more than a standard leadership change at the Federal Reserve.

It signals a potential shift in how rates are determined and executed. Both Powell and Warsh are nominees of President Donald J. Trump but it is unclear what type of Chair Warsh will be should the Senate confirm his nomination.

Although a Senate Banking Committee confirmation is expected in the coming weeks, a paved path does not lie ahead of Warsh. It is anticipated that he will face rigorous questions on central bank digital currencies (CBDCs), federal debt, and how he will maintain Fed independence coming off of Powell’s term that reflected an administration that hurled insults and launched a federal investigation when Powell did not lower rates.

Is Warsh still a “Hawk”?

Kevin Warsh previously served on the Federal Reserve Board of Governors from Feb. 24, 2006, to March 31, 2011 during the financial crisis of 2008 and remains a long-standing critic of the Fed’s post-crisis policy record.

Warsh acted as the bank’s primary liaison to Wall Street and as the Fed’s representative to the Group of Twenty (G20). His career began as Special Assistant to former President George W. Bush at the White House. Bush’s nomination of Warsh drew criticism as he was the youngest appointment in the history of the Fed.

The difference between Powell’s framework and what a Warsh-led Fed could look like is not theoretical. It shows up clearly in how inflation, rates, and policy responses have evolved in recent years.

This dynamic helps explain why a shift toward a more preemptive Fed would represent a meaningful change in how markets interpret policy signals.

From “Flexible” to “Reform”

Powell’s Fed embraced flexibility. Under Powell’s leadership, the central bank formally shifted to an average inflation targeting framework, allowing inflation to run above 2% for some time following periods of undershooting. This framework justified patience in tightening, even as inflation began to accelerate in 2021.

Warsh, by contrast, has consistently warned about the long-term costs of easy money, particularly on its impact on financial stability and asset prices. If he brings that philosophy to the chair, the Fed could move toward a more preemptive posture: quicker to tighten, less tolerant of inflation overshoots, and more focused on maintaining credibility with markets.

In practical terms, that could mean:

• Potentially Higher Rates: Warsh’s Fed may have lower tolerance for upward inflationary pressure

• Likely More Positive Real Rates: Greater emphasis on real rates (the policy rate after inflation) staying positive

• More Opaque: Less reliance on forward guidance as a policy tool

Balance Sheet Policy Back in Focus

Another likely shift is the role of the Fed’s balance sheet. Powell oversaw an era of massive expansion, first through quantitative easing and later through emergency liquidity facilities.

Warsh has been openly critical of the Fed’s footprint in financial markets. His leadership could accelerate balance sheet normalization, both in pace and in principle. The Fed might become more reluctant to intervene in market functioning unless absolutely necessary, raising the bar for future liquidity programs.

That has implications not just for rates, but for risk assets broadly. A smaller, less active Fed balance sheet typically translates into tighter financial conditions, even absent explicit rate hikes.

Perhaps the most immediate change would come in how the Fed communicates.

Powell evolved into a highly transparent chair, using press conferences, speeches, and clear forward guidance to shape market expectations. This approach reduced volatility at times, but also risked locking the Fed into specific policy paths.

Warsh is likely to favor more ambiguity. Historically, he has argued that excessive transparency can weaken policy effectiveness by encouraging markets to front-run the Fed. A shift in this direction could reintroduce uncertainty, and with it, volatility, into rate markets.

Political Economy and Institutional Tension

“That is up to the President,” Treasure Scott Bessent responded when asked if Warsh would be sued by the U.S. Justice Department for not cutting interest rates at the President’s direction.

Warsh’s nomination would also land in a different political environment than the one Powell navigated. Questions about central bank independence, fiscal dominance, and the Fed’s role in inequality and asset prices are now more prominent.

Unlike Powell, who often positioned himself as a pragmatic consensus-builder, Warsh may face sharper scrutiny from both sides:

• Critics wary of tighter policy slowing growth

• Others concerned about the Fed’s past tolerance for inflation

This could make the Fed’s policy path more contested, even if the underlying economic data is similar.

Unprecedented Volatility

The Trump administration has reflected an historically volatile stock market continuously threatened by government shutdowns, geopolitical events, and the looming potential threat of an AI bubble burst.

If Warsh is confirmed, the key question is not whether policy changes, but how quickly.

Three signals will matter most:

1. The reaction function: Does the Fed respond faster to inflation data than under Powell?

2. Balance sheet guidance: Is there a clear commitment to shrinking the Fed’s footprint?

3. Communication style: Does the Fed reduce forward guidance and tolerate more market uncertainty?

The answers will shape everything from the yield curve to equity valuations.

Is AI in a Bubble? Our thoughts here.

A Regime Shift, Not Just a Personnel Change

Leadership transitions at the Fed rarely produce immediate, dramatic shifts. Institutional inertia is real, and the broader FOMC will still shape decisions.

But the chair sets the tone.

Powell’s legacy will likely be defined by flexibility, crisis responsiveness, and a willingness to stretch the Fed’s toolkit. A Warsh era would point in a different direction: toward restraint, preemption, and a narrower interpretation of the Fed’s role.

If that shift materializes, markets may need to adjust not just to a new chair,but rather to a new regime and investors may need to rethink assumptions that have held for over a decade. Such as that the Fed will step in quickly, that liquidity will follow stress, and that policy will err on the side of support. A more preemptive Fed changes both the timing and direction of those expectations.

Warsh’s Biggest Challenge

History suggests that the market rarely gives a new Fed Chair a grace period. Instead, it tests them. Within roughly the first year of their tenure, each modern Chair has been forced to respond to a defining episode of financial stress: Black Monday under Alan Greenspan, the onset of the Global Financial Crisis under Ben Bernanke, the 2015–2016 stock market selloff often associated with the “flash crash” environment early in Janet Yellen’s term, and the pandemic shock of COVID-19 pandemic under Jerome Powell. If confirmed, Warsh is unlikely to be an exception. The combination of elevated inflation, geopolitical instability, and stretched asset valuations suggests that his credibility, and policy framework, may be stress-tested early, forcing him to balance market stabilization with his instinct toward tighter, more disciplined monetary policy.

What This Means for You

I have been consistently counseling our Atlas Analytics clients on this emerging regime for months and writing about this shift in the macroeconomic environment here for weeks.

With Warsh’s appointment, inflation remaining sticky, and geopolitical pressures building in the Middle East, our view has been that rates are unlikely to fall and may move higher.

Importantly, this is not a reactive view. Our GDP forecasting framework began signaling this shift earlier in the year, well before it became more widely discussed.

Recent positioning, including pressure on long-duration assets and strength in the dollar, has been consistent with that signal. The takeaway is not the trade itself, but the ability to identify inflection points early and translate them into actionable decisions.

As we move into what could be a fundamentally different rate environment, both opportunity and risk are expanding quickly. We will continue to share our views and positioning in real time. For those looking to stay ahead of these shifts rather than react to them, we’d welcome a conversation about how Atlas Analytics can support your investment process.

Get in Touch

This article was written with valuable research assistance from Morgan Reppert, Executive Operations Associate for Atlas Analytics.

The most important economic decision the Federal Reserve makes is whether to hold, raise, or lower interest rates.

Over the past few months, we have seen direct tension between President Donald Trump and Fed Chair Jerome Powell over whether rates should be cut.

Last week, Powell announced a unanimous vote to keep rates steady between 3.5% and 3.75%, while signaling a possible cut later this year:

“The forecast is that we will be making progress on inflation, not as much as we had hoped, but some progress.”

Powell’s term as Fed Chair ends this May, though he has stated he will remain in his role until Fed Chair nominee, Kevin Warsh is confirmed. Powell’s term on the Board of Governors runs through 2028, though he has indicated he will not step down until the ongoing Justice Department probe is resolved.

This tension is not new. It is rooted in how the role of the Fed Chair and the Federal Reserve was designed.

The History of the Chair of the Federal Reserve

The role of the Fed Chair was established by the Banking Act of 1935. Since then, there have been 10 chairs. Most had prior government experience, and many served within the Federal Reserve system before being appointed. The position was designed to centralize authority and strengthen the Fed’s ability to respond to economic crises.

The Chair serves a four-year term, while members of the Board of Governors serve staggered 14-year terms. This structure is specifically designed to insulate monetary policy from short-term political pressure.

The Chair’s responsibility is to the public, not the president.

Disputes over rates are common and historically consistent. They reflect the tension between political pressure and economic judgment. While the President appoints the Chair, monetary policy decisions are made by the Federal Open Market Committee, reinforcing the system’s independence.

To understand today’s environment, it is useful to look at historical President and Fed Chair relationships that illustrate the Chair’s recurring challenge of balancing political pressure with economic reality.

President Richard Nixon and Former Chair Arthur Burns

“I respect his independence,” President Richard Nixon said. “However, I hope that independently he will conclude that my views are the ones that should be followed.”

As Nixon prepared for re-election in 1972, he pushed Federal Reserve Chair Arthur Burns to keep rates low to support economic growth. Burns ultimately eased policy, resulting in short-term economic strength but contributing to the high inflation of the 1970s.

Behind the scenes, pressure from Nixon and his cabinet was persistent.

Nixon and his advisors made clear they believed tighter policy would risk both the economy and the election. Burns faced a difficult choice between institutional independence and political reality. Policy remained loose longer than it otherwise might have. Inflation began to build even as unemployment improved, setting up the stagflation that defined the decade. The episode is often cited as a clear case of political influence shaping monetary policy outcomes.

President Jimmy Carter and Former Chair G. William Miller

President Jimmy Carter appointed G. William Miller, who was widely seen as too cautious on inflation, laid-back, and dovish.

Miller served only 17 months before being replaced, amid oil price shocks, weak policy credibility, and broader economic instability. The episode reinforced the cost of a Fed perceived as too aligned with the White House.

Markets questioned whether the Fed would act forcefully enough to contain inflation, and Miller was reluctant to tighten aggressively, allowing inflation expectations to continue rising. The lack of decisive action eroded confidence in the central bank.

Carter ultimately moved Miller to the Treasury, replacing Richard Blumenthal, and replaced him as Chair with Paul Volcker during Carter’s “cabinet crisis”. This marked a clear shift toward restoring credibility and independence in monetary policy.

President Ronald Reagan and Former Chair Paul Volcker

President Ronald Reagan inherited Paul Volcker from Carter, who had already begun aggressively raising rates to fight inflation.

The policy triggered a deep recession but ultimately reduced inflation from a record high in 1980. Reagan publicly supported Volcker despite the political risk, demonstrating that central bank independence can involve short-term economic pain in pursuit of long-term stability.

The pattern is clear. Presidents want growth. The Fed is tasked with stability. The tension between the two is not a flaw. It is by design.

The Atlas View

At Atlas Analytics, our position is clear.

In an environment of accelerating growth and persistent inflation risk, interest rates should be higher, and policy should remain firmly insulated from political pressure.

Supporting Federal Reserve independence today is not about personalities. It is about preserving the institutional framework that made long-run American prosperity possible.

At Atlas Analytics, we use satellite-derived GDP nowcasting and proprietary AI models to help investors anticipate policy inflection points before markets price them in. If you want to trade this thesis or stress-test it against your own views, we welcome the conversation.

Let's Connect

This article was written with valuable research assistance from Morgan Reppert, Executive Operations Associate for Atlas Analytics.