Every quarter, economists, investors, policymakers, and journalists wait for the same number: U.S. GDP growth.

Within minutes of its release, the number is dissected. Did the economy beat expectations? Is growth accelerating or slowing? What does it mean for the Federal Reserve? Markets can move, forecasts are updated, and narratives about the economy begin to take shape.

But there is an important caveat: the first GDP estimate is not the final word on how fast the economy grew.

It can’t be.

Measuring an economy as large and complex as the United States in something close to real time is an extraordinary statistical undertaking. When the Bureau of Economic Analysis (BEA) publishes its initial estimate, some of the information needed to calculate GDP is incomplete or not yet available. As more comprehensive data arrive, the BEA updates its estimates.

Those revisions can be meaningful.

As the chart above shows, BEA estimates are revised by an average of approximately 0.3 percentage points in the first revision, 0.7 percentage points in the second, and 1.2 percentage points over a five-year period.

For an economy whose long-run growth rate is measured in just a few percentage points per year, those aren’t trivial differences.

Imagine an initial estimate showing the economy growing at a 2.0% annualized rate. A revision of even several tenths of a percentage point can meaningfully change our assessment of the quarter. Around economic turning points, revisions can be even more consequential for understanding whether the economy was accelerating, stagnating, or contracting.

Why GDP gets revised

Revisions aren’t a flaw in the system. They reflect a fundamental tradeoff in economic measurement: speed versus completeness.

If we wanted to know exactly what happened in the economy, we could wait until nearly every business survey, tax record, trade report, construction estimate, and other underlying dataset was complete.

But a GDP estimate arriving years after the fact wouldn’t be particularly useful to a policymaker deciding what to do today or an investor deciding where to allocate capital.

So statistical agencies produce the best estimates they can using the information available at the time. As better and more complete information arrives, those estimates improve.

That raises an interesting question: Can we expand the information available to us earlier?

A New Economic Information Set

For most of modern economic history, measuring the economy has depended heavily on businesses, households, and governments reporting what happened.

Today, we have another possibility: observing portions of economic activity directly.

Satellite imagery can reveal changes in construction, industrial facilities, transportation networks, ports, and other physical infrastructure. Geospatial datasets can help identify where economic activity is occurring. Shipping and logistics data can provide signals about the movement of goods. Other high-frequency datasets can capture activity long before traditional statistics are finalized.

None of these sources replaces official economic statistics.

A satellite cannot tell us how much households spent on healthcare last quarter. It cannot directly measure wages, software investment, or the enormous service economy. And alternative datasets introduce their own measurement challenges, biases, and noise.

But they can provide independent signals about parts of the economy while the traditional data picture is still incomplete.

That distinction matters.

Learn More

Forecasting the Present

We normally think of economic forecasting as trying to answer a question about the future: What will the economy look like next quarter or next year?

But there is another problem that is arguably just as important:

What is happening in the economy right now?

Economists call this nowcasting.

The difficulty of nowcasting is easy to underestimate. By the time we learn the official GDP estimate for a quarter, that quarter has already ended. And, as the history of revisions demonstrates, our understanding of what happened can continue changing long afterward.

This means that nowcasting is not simply a forecasting problem. It is also a measurement problem.

The opportunity presented by new forms of data is therefore not to replace the national accounts. It is to complement them: to add information to the economic picture sooner.

At Atlas Analytics, that’s the problem we’re working on. We combine satellite imagery, geospatial data, and traditional economic information to measure economic activity before the official picture is complete.

Better real-time measurement won’t eliminate uncertainty. No dataset can.

But if we can observe more of the economy as it happens, we can make better estimates of the present, and potentially make better decisions about the future.

Work With Us

Satellite-based macroeconomic forecasting isn’t just about GDP.

Atlas uses proprietary machine-learning and satellite-imagery models to measure economic activity from above. We are currently taking on a select number of projects to explore how these capabilities can be applied to specific industries, assets, and investment questions.

Have a use case where better real-time economic visibility could matter? Reach out to connect.

In May, Atlas wrote that the dollar was primed for a breakout, arguing that persistent inflation would keep U.S. interest rates higher than markets expected and, in turn, support a stronger dollar.

The Federal Reserve hasn’t raised rates yet. The dollar has moved anyway.

The Invesco DB U.S. Dollar Index Bullish Fund (UUP), our preferred proxy for the dollar, is now up approximately 4% year-to-date.

And following Federal Reserve Chairman Kevin Warsh’s hawkish Jackson Hole speech last week, the case for higher U.S. interest rates, and a stronger dollar. has only strengthened.

The question now isn’t whether the May thesis has begun to play out. It’s whether the move has further to run.

We think it does.

Why Do Higher Interest Rates Likely Mean a Stronger Dollar?

At its core, the relationship between interest rates and currencies is about where investors can earn the highest return on their money.

When U.S. interest rates rise relative to those in other major economies, dollar-denominated assets become more attractive. An investor choosing between otherwise comparable U.S. and European assets, for example, has a greater incentive to hold the U.S. asset when it offers a higher yield.

But to buy U.S. Treasuries, corporate bonds, or other dollar-denominated assets, investors generally need dollars.

That creates additional demand for the currency.

The key, therefore, isn’t simply whether the Federal Reserve raises interest rates. It’s how U.S. rates evolve relative to rates elsewhere, and how investors expect that gap to change.

This is why expectations matter so much. Currency markets don’t wait for the Federal Reserve to actually change the federal funds rate. If investors become convinced that U.S. rates will remain higher for longer, or rise while other central banks hold steady or cut, the expected return from holding dollar-denominated assets increases today.

That’s precisely the dynamic we’re watching now.

The Fed has yet to raise its benchmark rate, but expectations have shifted toward tighter U.S. monetary policy. If Chairman Warsh follows through on the hawkish message delivered at Jackson Hole, widening interest-rate differentials could provide another source of support for the dollar.

Why Should Warsh Hike?

Two weeks ago, we argued that the bond market is already sending the Federal Reserve a message: monetary policy may not be tight enough.

Despite the Fed holding its benchmark rate at 3.50%–3.75% at its July meeting, long-term borrowing costs have continued to rise. The 10-year Treasury yield recently climbed to roughly 4.7%, its highest level since January 2025. Mortgage rates have risen, corporate borrowing has become more expensive, and financial conditions have tightened even without another move from the Fed.

Why?

Part of the answer is inflation. Core PCE inflation remains above the Federal Reserve’s 2% target, while renewed energy-price pressures threaten to keep inflation elevated. Investors increasingly appear to be pricing the risk that inflation will remain persistent, and that today’s policy rate may ultimately prove insufficient to bring it back to target.

But inflation isn’t the only concern. Large federal deficits have increased the supply of Treasury securities just as foreign investors account for a smaller share of the market. At the same time, investors are demanding greater compensation for the uncertainty associated with holding long-term government debt.

The result is an important divergence: the Fed has paused, but the bond market hasn’t.

As we wrote in The Bond Market Is Telling You the Fed Should Raise Rates, Treasury markets continuously incorporate expectations about inflation, growth, fiscal policy, and monetary policy. Rising long-term yields therefore suggest that investors are demanding tighter financial conditions than they were only a few months ago.

Chairman Warsh now faces a choice. He can wait for inflation to return convincingly toward 2%, or he can respond to a market that is already signaling that rates may need to remain higher, and perhaps move higher still.

We think the balance of risks favors another hike.

What Could Make Us Wrong

No macroeconomic forecast is certain. While we believe the balance of risks points toward higher interest rates and a stronger dollar, there are several developments that could undermine our thesis.

• First, economic growth could deteriorate faster than expected. Atlas’s satellite-based macroeconomic forecasts are already pointing toward a slowdown in U.S. growth. So far, however, we don’t believe that deterioration is severe enough to mandate a rate cut. If incoming data were to show a much sharper contraction, particularly alongside a substantial deterioration in the labor market, the Fed could be forced to prioritize growth over inflation. Rate cuts would substantially weaken our dollar thesis.

• Second, inflation could surprise meaningfully to the downside. August Core PCE will be an important test. A particularly soft reading could give the Fed greater confidence that inflation is returning toward its 2% target and reduce the case for another hike. We think that’s possible, but unlikely enough that it doesn’t change our base case today.

• Third, even a Fed hike may not be enough. Currency values depend on relative interest rates, not simply the direction of U.S. rates. If other major central banks tighten more aggressively than the Federal Reserve, the yield advantage of dollar-denominated assets could narrow, or even move against the United States. In that scenario, we could be right about the Fed and still be wrong about the dollar.

• Finally, politics and Treasury policy could complicate the signal coming from bond markets. The administration has sought to influence longer-term borrowing costs through changes in Treasury issuance and other policy levers. So far, those efforts have not been sufficient to prevent long-term yields from remaining elevated. But a more successful effort to push down the long end of the Treasury curve could weaken one of the pillars supporting our outlook.

For now, none of these risks is enough to overturn our base case. We continue to expect tighter U.S. monetary policy, and thus believe the dollar has further room to run.

Work With Us

Satellite-based macroeconomic forecasting isn’t just about GDP.

Atlas uses proprietary machine-learning and satellite-imagery models to measure economic activity from above. We are currently taking on a select number of projects to explore how these capabilities can be applied to specific industries, assets, and investment questions.

Have a use case where better real-time economic visibility could matter? Reach out to connect.

Over the past several years, the AI investment story has largely centered on chips, models, and technology stocks. Today, the scale of the AI buildout is shifting attention toward the physical infrastructure required to support and power it.

Hundreds of billions of dollars are flowing into data center buildouts, land acquisition, servers, power generation, transmission, cooling systems, legal, and construction. As that capital moves into the physical economy, AI is beginning to reshape not only technology markets, but energy demand, infrastructure development, and regional economies across the United States.

The gap between what is announced and what is actually built may therefore become an increasingly important signal for understanding the next phase of AI investment.

ICYMI: Atlas AI Bubble Recap

Follow the Capital

The spending numbers alone illustrate how quickly the AI economy is changing.

Amazon increased its expected 2026 capital spending to approximately $220 billion following strong second-quarter cloud growth. AWS revenue increased 37% year over year, its fastest growth in more than four years, even as Amazon said demand continued to exceed available computing capacity.

Microsoft expects to invest roughly $190 billion in capital expenditures during calendar year 2026. The company has said that much of its recent spending is going toward GPUs, CPUs, and long-lived assets such as large data center sites. Microsoft also expects to remain capacity constrained as it continues expanding AI infrastructure.

Alphabet has guided toward $175 billion to $185 billion of 2026 capital expenditures, primarily to expand AI computing capacity and meet growing Google Cloud demand.

Meta expects another $130 billion to $145 billion of capital expenditures in 2026. In the second quarter alone, Meta spent approximately $31 billion, nearly double the level of the same quarter a year earlier.

Taken together, these four companies alone are planning more than $700 billion of capital spending in 2026, with AI and cloud infrastructure among the major drivers.

That is no longer simply a technology investment cycle.

It is becoming one of the largest physical capital deployment cycles in the U.S. economy.

The Investment Is Showing Up on the Ground

Recent announcements make the scale even more tangible.

In Kentucky, NextEra Energy announced plans for a more than $100 billion privately funded development at the U.S. Department of Energy’s Paducah Site in Kentucky.

Kentucky is not an isolated case.

In Ohio, Nvidia has agreed to provide guarantees of up to $105 billion supporting OpenAI’s long-term lease of a data center development that could ultimately reach 8 gigawatts of capacity. The project’s first 800 megawatts are expected online in 2028.

In West Virginia, Anthropic is expected to commit $45 billion over six years to rent computing capacity from an Nscale data center, representing roughly 460 megawatts of capacity.

In Texas, Meta is also bringing outside infrastructure capital into the buildout. In July, the company announced a partnership with BlackRock to finance a roughly $14 billion data center campus. Funds managed by BlackRock will own 80% of the venture, with Meta retaining 20%.

These projects represent different types of financial commitments, but together they point to the same structural shift. AI investment is becoming geographically specific, asset intensive, and increasingly dependent on physical infrastructure.

Corporate Balance Sheets to Capital Markets

The financing is changing too.

For much of the AI boom, the largest technology companies could finance infrastructure largely from their enormous operating cash flows.As investment requirements have grown, outside capital and debt markets have become increasingly important.

According to Reuters, U.S. corporate debt issuance associated with AI infrastructure reached approximately $220 billion in 2026, compared with just $12.5 billion the previous year.

Private capital is moving in as well.

This matters because the AI buildout is beginning to resemble other major infrastructure cycles. As the sector becomes more capital intensive, the relationship between capital committed and productive infrastructure delivered will matter more.

The Tale of Two Buildouts

This creates a useful distinction for investors.

There is the announced AI buildout. It consists of capital expenditure guidance, financing commitments, data center announcements, power agreements, equipment orders, and projected capacity.

Then there is the physical AI buildout. Land gets cleared. Foundations appear. Buildings go vertical. Cooling systems and electrical infrastructure are installed. New generation comes online. Facilities expand, become operational, or encounter delays.

Over time, the two should converge.

If capital in planned investment translate into completed assets with strong utilization and attractive returns, the physical economy will provide evidence that the infrastructure cycle is delivering on its promise.

If deployment begins slowing, construction timelines slip, facilities remain underutilized, or announced capacity repeatedly fails to materialize, the physical economy may provide an early indication that expectations have moved ahead of reality.

That makes physical deployment an increasingly important piece of the AI investment puzzle.

Measuring What Actually Gets Built

At Atlas Analytics, our technology is designed to measure economic activity through changes that can be observed in the physical world.

Using satellite imagery, computer vision, AI/ML, and time-series analysis, Atlas can monitor activity across individual locations and assets over time.

For the AI infrastructure cycle, that creates an opportunity to track activity around specific data centers, construction projects, energy infrastructure, industrial facilities, and other economically significant assets.

Financial markets provide one view of the AI boom. Corporate announcements provide another.

Physical activity provides an independent layer of evidence.

As AI investment becomes increasingly tied to land, energy, construction, and individual facilities, understanding what is actually happening at those locations can help distinguish between capital announced and capital deployed.

The AI boom is increasingly leaving a physical footprint across the United States.

And that footprint can be measured.

Learn More on Atlas Asset Signals.

Recent movements in U.S. Treasury markets present a puzzle. At its July meeting, the Federal Open Market Committee left the federal funds rate unchanged at 3.50%–3.75% for a second consecutive meeting, despite three officials dissenting in favor of an immediate increase1.

Since then, long-term borrowing costs have continued to rise. The yield on the 10-year U.S. Treasury has climbed to roughly 4.7%, its highest level since January 20252. Thirty-year mortgage rates have increased to 6.65%, corporate borrowing has become more expensive, infrastructure projects face higher financing costs, and higher discount rates have weighed on equity valuations3. Financial conditions have therefore continued to tighten despite no change in the Fed’s policy rate.

If the Fed has stopped raising interest rates, why have borrowing costs continued to increase?

One Rate, Many Yields

The Federal Reserve directly controls the federal funds rate, the overnight interest rate at which banks lend reserves to one another. Most borrowing decisions in the economy, however, are made over much longer horizons. Households borrow through thirty-year mortgages. Firms issue debt with maturities of five, ten, or even twenty years. Governments finance infrastructure whose lives extend over decades.

The interest rates attached to these decisions are not set directly by the Federal Reserve. They are determined in financial markets, where investors decide the return they require to lend over different horizons. U.S. Treasury securities play a central role in this process because they provide the benchmark against which many other borrowing costs are priced4.

This distinction is important. The federal funds rate reflects the Federal Reserve’s current policy stance. Treasury yields reflect investors’ expectations about the future. Every day, investors reassess inflation, economic growth, fiscal policy, and risk, and those expectations are incorporated into Treasury prices and yields.

This helps explain why long-term interest rates can continue to rise even when the Federal Reserve leaves its policy rate unchanged. The recent increase in Treasury yields is unlikely to have a single explanation. Instead, it reflects several forces that have influenced how investors value long-term government debt.

Four Explanations for Rising Yields

1️⃣ The first explanation is that investors may doubt the durability of the pause itself. Core PCE inflation rose to roughly 3.4% year over year in May before easing only modestly, to about 3.3%, in June, still well above the Federal Reserve’s 2% objective.

Fed Chair Kevin Warsh made this point directly at the July press conference, stating that “there’s only a target and it’s 2%,” and citing already-tight financial conditions, rather than confidence that inflation had been contained, as the rationale for holding rather than raising rates.

Markets responded by pricing in a higher probability of a hike later in the year. Under this interpretation, Treasury yields are not a reaction to today’s policy rate; they reflect expectations about the future policy path.

2️⃣ A second explanation is closely related but distinct: it concerns the persistence of inflation risk rather than the near-term policy path. Long-term bonds promise fixed payments many years into the future, and those payments lose real value if inflation proves more persistent than currently priced. Energy prices have re-emerged as a source of concern amid ongoing tensions in the Middle East, feeding directly into both core inflation readings and mortgage pricing5. Under this reading, investors are not necessarily forecasting another rate increase; they are demanding higher compensation for the risk that inflation does not return smoothly to target.

3️⃣ A third explanation is fiscal rather than monetary. The Committee for a Responsible Federal Budget noted in July that the 10-year yield had moved roughly 45 basis points above the level implied by Congressional Budget Office projections, and warned that yields sustained at this level could add an estimated $1.7 trillion to the national debt over the next decade.

The Treasury continues to issue substantial volumes of debt to finance persistent deficits, and, as in any market, a growing supply of securities requires sufficient demand to absorb it at unchanged prices.

That demand has become harder to secure. Foreign investors, who held nearly half of publicly traded federal debt in the early 2010s, now hold closer to 30%, even as debt outstanding has grown more than fourfold since 20086. A $16 billion Treasury auction in May 2025 drew unusually weak bidding, and, taken together with the S&P, Fitch, and Moody’s sovereign credit downgrades of the past fifteen years, has reinforced a narrative in which federal borrowing needs are beginning to outrun the market’s willingness to absorb them at prevailing yields7.

4️⃣ The fourth explanation is more structural: investors may simply require greater compensation for bearing long-horizon uncertainty. Economists refer to this as the term premium, defined as the portion of a bond’s yield not explained by the expected path of short-term rates, but instead by the additional risk of committing capital for ten or thirty years. The Federal Reserve Bank of New York’s ACM model, developed by economists Tobias Adrian, Richard Crump, and Emanuel Moench, decomposes Treasury yields into precisely these two components, and has repeatedly shown the term premium rising during periods of heightened fiscal and inflation uncertainty8.

A rising term premium requires neither an expectation of a further Federal Reserve rate increase nor a deterioration in current economic data. It reflects, more simply, a higher price placed on the unknown. These four explanations are not competing hypotheses so much as complementary ones.

Treasury yields represent the aggregated judgment of a large number of investors simultaneously reassessing inflation, growth, fiscal sustainability, and monetary policy. The resulting price reflects a distribution of probabilities, not a single certainty.

A Precedent: 2013 and the Return of the Bond Vigilantes

This pattern, in which yields move well ahead of, or independently of, the Federal Reserve’s own decisions, is not new.

During the 2013 episode known as the Taper Tantrum, the 10-year Treasury yield rose by 137 basis points between early May and early September, after then-Chair Ben Bernanke signaled only that the Federal Reserve might eventually slow its asset purchases, not that it intended to raise rates.

Emerging-market currencies absorbed much of the resulting adjustment: a basket of eighteen floating currencies depreciated by roughly 8.8% against the dollar over four months, even though tapering did not begin until December of that year and rates were not raised until December 20159. The market repriced on an expectation, not an action.

A comparable pattern has been visible more recently in what analysts have described as the return of the bond vigilantes, a term coined in the 1980s for investors who discipline government borrowing by demanding higher yields rather than waiting for policymakers to act.

In May 2025, the 30-year Treasury yield briefly exceeded 5% for the first time since 2007, coinciding with Moody’s downgrade of U.S. sovereign debt and growing concern that a pending tax bill could add several trillion dollars to projected deficits over the following decade. None of that repricing required action by the Federal Reserve. It required only a market reassessing how much risk it was prepared to absorb at prevailing prices.

The broader lesson is that markets rarely wait for official decisions before repricing economic risk. They act on the expected distribution of future outcomes, not merely on realized policy.

More on Predictive Yield Rates

Implications for Monetary Policy

This has direct implications for how monetary policy should be understood.

Central banks do not influence the economy solely through the federal funds rate. Monetary policy operates primarily by affecting financial conditions more broadly, and Treasury yields sit close to the center of that transmission mechanism. They influence mortgage rates, corporate financing costs, infrastructure investment, commercial real estate valuations, and equity pricing.

When long-term yields rise independently of the policy rate, financial conditions tighten even in the absence of further action by the Federal Reserve. The bond market can therefore perform part of the central bank’s tightening function on its own; symmetrically, a decline in yields can perform part of its easing function.

The relevant question is not only whether the Federal Reserve raises rates at its next meeting, but whether financial markets have already begun tightening conditions on the Federal Reserve’s behalf.

The bond market does not set monetary policy. It does, however, continuously assess whether current policy is consistent with the economy that investors expect to face in the years ahead.

The recent rise in Treasury yields is unlikely to reflect a single cause. It more plausibly reflects the combined influence of higher-for-longer policy expectations, persistent inflation risk, expanding Treasury supply against softening demand, and a higher premium for long-term uncertainty. Taken together, these forces indicate that investors are demanding tighter financial conditions than most expected only a few months ago.

The broader implication extends beyond the current episode. The Federal Reserve announces policy eight times a year. The bond market updates its assessment continuously. Central banks communicate their intentions through periodic policy statements, but Treasury markets aggregate dispersed information from a large number of investors into a single price in real time.

Recognizing this distinction helps explain not only why yields move, but why financial markets frequently begin adjusting well before official policy catches up.

Tell Us What You Think

This summer, those indicators moved in different directions.

According to the Bureau of Economic Analysis (BEA)’s advance estimate, the U.S. economy expanded at a 1.5% annualized rate during the second quarter. That headline was relatively soft, but underlying private domestic demand was stronger. Real final sales to private domestic purchasers, the sum of consumer spending and gross private fixed investment, increased 3.9%.

Then came the July jobs report.

The Bureau of Labor Statistics (BLS) estimated that nonfarm payroll employment declined by 23,000 in July. BLS described that change as “little changed,” not as a collapse in employment. The report nevertheless showed that hiring had become subdued. Payrolls increased by an average of only 34,000 per month over the prior 12 months, and the May and June estimates were revised down by a combined 103,000 jobs.

How can the economy continue producing more while payroll growth stalls?

Timing is part of the answer. GDP measures activity from April through June, while the latest employment report covers July. The soft July payroll estimate could reflect conditions that developed after the second quarter ended.

Productivity offers another possible explanation.

More Output Does Not Necessarily Require More Workers

The latest productivity report provides the clearest bridge between the GDP and employment data.

During the second quarter, nonfarm business output increased at a 1.7% annualized rate. Hours worked increased only 0.3%. The difference produced a 1.4% increase in labor productivity, which BLS defines as output per hour worked.

The year-over-year comparison is even more striking. Nonfarm business output increased 2.5%, while hours worked rose just 0.2%. Recent output growth has therefore required very little growth in total labor hours.

That does not mean artificial intelligence eliminated 23,000 jobs in July. The data cannot support that conclusion. Productivity can improve for many reasons, including new equipment, software, automation, changes in business processes, or more efficient use of existing workers.

The composition of second-quarter growth illustrates one possible mechanism. BEA reported that the increase in investment primarily reflected gains in equipment and intellectual property products, including software and research and development. These data do not prove that investment caused the weak hiring figures. They do show how capital investment can allow output to grow faster than payrolls.

The July payroll weakness was also concentrated rather than universal. Local government education lost an estimated 50,000 jobs, retail trade lost 19,000, and financial activities continued to trend down by 14,000. Health care added 22,000 positions. Employment in construction, manufacturing, transportation and warehousing, information, and most other major industries changed little.

That is not evidence of a broad-based employment contraction. It is evidence of a labor market that has lost momentum, with the latest weakness concentrated in several sectors.

The distribution of the gains also deserves attention. Labor’s share of nonfarm business output was 52.9% in the second quarter, the lowest level in a BLS series beginning in 1947. Price-adjusted hourly compensation declined at a 3.1% annualized rate during the quarter and was down 0.1% from a year earlier.

Together, these figures support a narrower conclusion. Output per hour increased, total hours barely grew, and the share of output accruing to workers as compensation reached a series low. They do not tell us whether the divergence will be temporary or structural.

Which Signal Should We Trust?

The answer is not to choose between GDP and employment. They measure three parts of the economy.

1. GDP measures output.

2. The payroll report estimates the number of jobs on employer payrolls.

3. Productivity measures output per hour worked.

Read together, the three reports describe an economy that continued to produce more in the second quarter without a comparable increase in labor input.

This is where measuring the physical economy can add another perspective.

Atlas Analytics’ algorithms, ROY and JACK, combine satellite-derived measures of the built environment with traditional economic data to forecast GDP. It provides an independent way to assess whether observable economic activity is expanding or slowing while official GDP and employment estimates are revised.

If physical activity remains firm while hiring weakens, the current divide may reflect a more productive or increasingly capital-intensive expansion. If physical activity and employment both begin declining, the July report may instead prove to have been an early warning of a broader slowdown.

Geography also plays a role.

A national payroll number can conceal meaningful differences between states and industries, just as national GDP can. Atlas produces economic forecasts for all 50 states and the District of Columbia, allowing us to examine whether growth remains broadly distributed or is becoming concentrated in fewer markets.

For now, the data do not establish either a recession or an AI-driven labor shock. They show that output grew in the second quarter, labor hours barely increased, and July payroll employment was essentially flat to slightly negative.

The next few months will show whether this is a temporary hiring pause or a more durable change in the relationship between output and work.

Get in Touch

For Atlas Analytics, our final forecast, published on July 13, more than two weeks before the release, projected:

• Headline GDP: 2.0%

• Core GDP: 3.2%

• Private Inventories: 0.3 percentage points

• Net Exports: -1.5 percentage points

The BEA’s Advance Estimate came in at:

• Headline GDP: 1.5%

• Core GDP: 3.2%

• Private Inventories: -0.7 percentage points

• Net Exports: -1.0 percentage points

While these comparisons are interesting, they raise a broader question that often gets overlooked:

What should we actually benchmark a GDP forecast against?

GDP Isn’t Measured Once

Unlike many economic indicators, GDP is not published once and left unchanged.

Instead, every quarter goes through a sequence of revisions:

• Advance Estimate

• Second Estimate

• Third Estimate

• Annual revisions

• Comprehensive benchmark revisions

The number reported on release day is simply the BEA’s best estimate using incomplete information. As additional surveys, trade statistics, inventory reports, tax records, and other source data become available, the estimate evolves.

In other words, even the government’s “official” GDP release is itself revised over time.

Why GDP Revisions Matter

This creates an important challenge when evaluating any GDP forecasting model.

Should a forecaster be judged against the BEA’s Advance Estimate? The Second Estimate? The Third Estimate? Or the benchmarked values published years later?

Each answers a slightly different question.

The Advance Estimate matters because it is the first number markets react to. But it is also produced using incomplete information. As additional source data become available, including more complete trade statistics, inventories, business surveys, and administrative records, the BEA updates its estimate to better reflect underlying economic activity.

Historically, these revisions have been far from trivial.

On average, the BEA revises quarterly GDP growth by approximately:

• 0.3 percentage points between the Advance and Second Estimates,

• 0.7 percentage points by the Third Estimate, and

• 1.2 percentage points following its five-year comprehensive benchmark revisions.

In other words, the “official” estimate released on GDP day is often only the beginning of the story.

Q2 2025 provides a good illustration. The BEA initially reported annualized GDP growth of 3.0%. One month later, the estimate was revised to 3.3%, and by the Third Estimate it had increased again to 3.8%, a cumulative upward revision of 0.8 percentage points.

This example highlights why evaluating a real-time forecasting model solely against the Advance Estimate can be misleading. The target itself continues to evolve as better information becomes available.

For that reason, we view every GDP release as the start of the evaluation process rather than the end. We’ll compare Atlas against the Advance Estimate because that’s what markets observe in real time, but we’ll also continue tracking the forecast as the BEA revises its estimate over the coming months and years.

What Atlas Is Actually Forecasting

Atlas was never designed simply to predict what the BEA will print.

Our objective is different.

Using satellite imagery and proprietary geospatial intelligence, we’re attempting to measure underlying economic activity directly, independently of the government statistical process.

That distinction matters.

The BEA builds GDP from thousands of surveys, administrative records, and statistical assumptions. Atlas begins with a completely different information set.

Sometimes those approaches converge almost perfectly.

Sometimes they don’t.

Understanding why they differ is often just as informative as the forecast itself.

Q2 2026: A Closer Look

One aspect of this quarter that stood out was the composition of growth.

Atlas matched the BEA’s estimate for Core GDP almost exactly. The primary differences came from the more volatile components of GDP: Private Inventories and Net Exports.

That result is perhaps not surprising. These are two of the most difficult components of GDP to measure in real time because they depend on complex physical supply chains that are only partially observed through traditional economic statistics.

Net Exports

Trade is fundamentally a logistics problem.

Every quarter, millions of containers move through U.S. ports carrying imported and exported goods. Traditional trade statistics are compiled from customs declarations and administrative reporting, meaning they become available only after considerable processing.

Atlas approaches this differently.

Using our Joint Algorithm for Containerized Knowledge (JACK), we identify and count individual shipping containers directly from commercial satellite imagery at major ports across the United States. Rather than waiting for reported trade statistics, we’re able to observe physical trade activity as it occurs.

By monitoring container flows over time, JACK provides an independent measure of import and export activity that feeds directly into our estimates of Net Exports.

Private Inventories

Inventories present a different challenge.

Unlike trade, there is no single observable location where inventories accumulate. Goods are dispersed across manufacturing facilities, warehouses, distribution centers, retail locations, and transportation networks throughout the country.

As a result, inventories remain one of the most difficult components of GDP to estimate in real time.

Atlas is actively developing new computer vision capabilities designed specifically to improve our measurement of inventory accumulation and depletion. While this work is still underway, we also closely monitor each month’s Census Bureau inventory releases to refine our estimates as additional information becomes available.

Over time, we believe combining satellite-derived observations with traditional inventory statistics will provide a more complete picture of inventory dynamics than either source can offer independently.

We’ll Continue Following the Story

One of the advantages of publishing forecasts in real time is that the evaluation doesn’t stop after release day.

Over the coming months, we’ll continue comparing Atlas against each successive BEA revision while also examining what drove any remaining differences.

Forecasting isn’t just about producing a single number: it’s about understanding the economy as new information becomes available.

As the Q2 data evolve, we’ll continue sharing what we learn.

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.

Learn More

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.

Let's Connect

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.

Get in Touch

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.

Get in Touch

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.

Get in Touch

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.”

Get in Touch

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.

Let's Chat

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.

Learn More

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.

Get in Touch

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.

Learn More

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.

Learn More

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.

Learn More

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.