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

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

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

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

That is why we built JACK.

Meet JACK

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

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

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

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

But trade is fundamentally a physical process:

• Ships depart and arrive

• Containers move

• Ports expand and contract

These movements are observable phenomena.

How JACK Works

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

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

That relationship is the foundation of JACK.

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

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

This leaves two important pieces of information for JACK:

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

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

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

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

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

What Port-Level Signals Unlock

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

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

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

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

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

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

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

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

From Signal to Actionable Insights

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

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

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

Get in Touch

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

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

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

We believed this conventional thesis was wrong.

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

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

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

Atlas Analytics TLT Positioning (January – May 2026)

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

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

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

The Market Expected a Slowdown

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

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

This narrative seemed intuitive.

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

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

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

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

The market was pricing a recessionary slowdown.

But the physical economy was signaling something very different.

What Atlas Forecasted

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

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

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

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

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

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

This distinction proved critical.

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

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

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

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

Why Long-Duration Bonds Became Vulnerable

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

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

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

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

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

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

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

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

The End of the Post-2008 Regime?

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

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

That environment may now be changing.

Today’s economy increasingly appears characterized by:

• Larger fiscal deficits

• Persistent government spending

• Deglobalization and supply-chain restructuring

• Industrial policy and reshoring

• Higher structural inflation volatility

• Tight labor markets

• Elevated geopolitical fragmentation

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

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

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

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

Why This Matters Beyond Bonds

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

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

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

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

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

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

Critical Questions Remain:

• Will inflation continue proving stickier than expected?

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

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

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

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

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

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

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

Get in Touch

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

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

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

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

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

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

That raised a bigger question:

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

Welcome Atlas Analytics to the Stage

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

Those signals are then translated into a weekly GDP estimate.

Not quarterly. Weekly.

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

The Gap

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

That distinction is important.

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

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

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

As Jake put it on the podcast:

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

The Core of the Atlas Thesis

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

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

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

Learn More

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

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

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

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

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

A Different Lens on FX: Policy, Not Just Price

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

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

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

This provides a simple but powerful lens:

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

• When it falls → currencies tend to weaken

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

Why This Matters Now

Atlas’ real-time GDP signals suggest that:

• U.S. growth is stabilizing rather than deteriorating

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

Put together, this implies:

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

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

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

What the Model Is Saying

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

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

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

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

The model suggests that:

• The dollar remains modestly undervalued relative to macro fundamentals

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

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

Timing the Move

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

Based on current trajectories:

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

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

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

Revisiting the USD–EUR Trade

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

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

• Policy expectations may be too dovish relative to underlying data

• Europe faces a more constrained growth backdrop

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

Market Implications

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

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

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

• Commodities → potential headwind

• Emerging markets → tighter financial conditions

• Multinationals → earnings pressure via FX translation

• Risk assets → modest drag at the margin

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

Final Thought

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

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

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

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

It’s higher.

Stay Ahead of the Macro Curve

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

Each week with our clients, we share:

• Real-time GDP updates through ROY

• Trade and net export signals through JACK

• Actionable macro insights across equities, rates, and FX

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

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

Contact Us

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

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

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

The results:

Headline GDP

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

Core GDP

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

Private Inventories Contribution

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

Net Exports Contribution

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

The Forecast — Published Before the Official Release

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

Here is the original video:

At the time, Atlas forecasted:

• +2.0% Headline GDP

• +2.8% Core GDP

On Friday, the BEA release came in at:

• +2.0% Headline GDP

• +2.9% Core GDP

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

The Big Picture

This was a strong validation of the Atlas approach.

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

• Where growth was coming from

• Where it was being offset

• And how those components ultimately shaped headline GDP

That is the broader objective:

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

Core GDP: The Signal Was Clean

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

This continues a pattern we’ve seen repeatedly:

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

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

• Tracking underlying economic activity

• Filtering out volatility from inventories and trade

• Providing a cleaner signal for markets

The Drag: Net Exports

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

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

This is consistent with prior quarters:

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

They are influenced by:

• Global demand

• Supply chain dynamics

• Currency movements

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

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

Inventories: Small but Meaningful

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

Inventories are:

• Volatile

• Frequently revised

• And capable of shifting headline GDP in meaningful ways

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

What This Means for Markets

Stepping back, this release reinforces a few key themes:

1️⃣The U.S. economy remains stable

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

2️⃣Headline GDP can mislead

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

3️⃣Real-time data is closing the gap

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

Why This Matters

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

It is:

• Delayed

• Subject to several revisions

• Often misinterpreted in real time

At Atlas, our objective is straightforward:

bring GDP forward,

make it more interpretable,

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

This release represents another step in that direction.

Looking Ahead

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

• Stable core growth

• Continued noise from trade

• Evolving sector-level dynamics

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

Final Thought

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

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

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

Connect Now

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

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

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

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

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

What Happened

Our core system, ROY, held up directionally.

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

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

But the real story was Net Exports.

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

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

What We’re Fixing

Two changes are now underway.

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

Second, we’re introducing something new: JACK.

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

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

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

The Q1 Forecast

Recent quarters have been volatile.

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

For Q1, Atlas sees a more balanced picture:

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

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

Domestic activity is holding up.

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

What Comes Next

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

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

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

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

From day one, the goal has been simple:

We’re not forecasting the economy.

We’re measuring it.

Directly, From space.

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

Let's Connect

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

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

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

They wait for:

• Official GDP releases

• Lagging economic indicators

• Consensus forecasts

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

This is why macro investing often feels frustrating:

You’re right… just too late.

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

Where Atlas Fits In

This is where Atlas Analytics changes the equation.

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

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

That allows us to answer a much more valuable question:

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

That distinction is everything.

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

From Signal to Trade

What does this actually look like in practice?

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

Using our forward-looking GDP forecasts, we saw:

• Economic activity was accelerating relative to consensus

• Inflation was proving structurally stickier than expected

This combination pointed to a clear conclusion:

This was not an environment for rate cuts.

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

And we positioned accordingly.

Those signals translated into positioning:

• Short long-duration bonds

• Long the U.S. dollar

The result:

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

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

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

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

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

The Bigger Game: The Macro Regime

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

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

Think:

• Strong growth vs. weak growth

• Inflationary vs. disinflationary

• Expansion vs. slowdown

Each of these environments rewards a different set of assets.

And crucially:

These regimes persist long enough to be traded.

A Quick Reminder

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

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

When there’s a gap, we step in.

But that’s only half the story.

Bringing It Together

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

• Arbitraging the deviation between fundamental value and market sentiment

• Trading the macro regime

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

One is about valuation.
The other is about direction.

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

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

Final Thought

Most investors are trying to answer:

“What just happened?”

We’re focused on a different premise:

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

That’s the edge.

What’s Next

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

Our Q1 2026 GDP estimate.

Curious?

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

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

Contact Us

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

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

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

Fundamentals do not.

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

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

In theory, this shouldn’t happen.

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

In practice, it rarely is.

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

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

Some will be directionally right.

None will be precisely correct.

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

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

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

That is the inefficiency.

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

Calculating Fundamental Value Based on GDP

There is a reason many investors dismiss macro.

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

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

Macro matters—but it is often second order.

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

This is the foundation of bottom-up investing.

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

Idiosyncratic risk diversifies away.

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

What remains is the common factor.

Macroeconomic risk.

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

And the most direct measure of that economy is GDP.

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

This is the key insight:

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

That relationship can be measured.

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

The output is not a narrative.

It is a mathematical estimate of fundamental value.

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

And this is where the opportunity emerges.

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

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

That is the foundation of the Atlas approach.

A Common Objection

I hear the same objection frequently from fundamental investors:

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

My response is always the same:

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

Creating Price Targets By Inputting Credible GDP Forecasts

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

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

Most market participants rely on lagged and revised macroeconomic data.

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

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

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

At Atlas, we take a different approach.

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

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

This distinction is critical.

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

Instead of asking:

What did GDP look like?

We ask:

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

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

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

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

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

The Result: The Atlas Model Portfolio

This framework allows us to move from theory to execution.

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

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

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

It is constructed around measurable economic reality.

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

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

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

Markets may trade on sentiment in the short run.

But over time, fundamentals assert themselves.

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

Let's Connect

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

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

The 1996 Parallel

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

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

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

The Productivity Data Is Turning

The chart below tells the story.

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

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

Why AI’s Impact Appears First in Physical Data

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

• Business surveys

• Hiring intentions

• Capex guidance

• Earnings commentary

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

This is where observational data becomes powerful.

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

Deployment > Invention

This brings us back to the 1996 analogy.

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

It happened when:

• Firms reorganized workflows

• Supply chains digitized

• Communication costs collapsed

• Information became real time

AI is entering that same deployment phase today.

We’re moving from experimentation to integration.

Why This Matters for GDP

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

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

• Higher potential GDP

• Stronger corporate margins

• Lower unit labor costs

• Delayed disinflation in services

• Higher neutral rates (r*)

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

The Observational Economy vs The Survey Economy

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

By observing:

• Construction activity

• Energy utilization

• Industrial throughput

• Logistics flows

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

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

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

Are We in 1996?

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

But the ingredients are familiar:

• Breakthrough general-purpose technology

• Massive infrastructure buildout

• Corporate workflow reorganization

• Capital deepening cycles

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

It looked like a slow turn higher in the data.

Exactly like today.

Closing Thought

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

It will compound quietly through:

• Compute infrastructure

• Energy demand

• Industrial production

• Logistics optimization

Long before it shows up cleanly in GDP releases.

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

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

Interested?

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

Let's Connect

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

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

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

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

Is Warsh still a “Hawk”?

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

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

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

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

From “Flexible” to “Reform”

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

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

In practical terms, that could mean:

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

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

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

Balance Sheet Policy Back in Focus

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

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

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

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

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

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

Political Economy and Institutional Tension

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

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

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

• Critics wary of tighter policy slowing growth

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

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

Unprecedented Volatility

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

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

Three signals will matter most:

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

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

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

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

Is AI in a Bubble? Our thoughts here.

A Regime Shift, Not Just a Personnel Change

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

But the chair sets the tone.

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

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

Warsh’s Biggest Challenge

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

What This Means for You

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

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

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

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

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

Get in Touch

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

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

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

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

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

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

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

The History of the Chair of the Federal Reserve

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

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

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

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

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

President Richard Nixon and Former Chair Arthur Burns

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

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

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

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

President Jimmy Carter and Former Chair G. William Miller

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

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

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

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

President Ronald Reagan and Former Chair Paul Volcker

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

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

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

The Atlas View

At Atlas Analytics, our position is clear.

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

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

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

Let's Connect

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

The average hedge fund has returned ~5.3% per year over the last decade1.

A model portfolio informed by Atlas signals has returned nearly four times that amount in the first ten weeks of the year.

While the sample period is short, the early results illustrate the potential of the Atlas approach.

Atlas signals generated approximately 21% year-to-date in a model portfolio through a combination of systematic macro trades (such as shorting long-duration bonds and going long the USD) and arbitraging the deviations between fundamental value and market price for a bevy of ETFs.

In short: better economic measurement creates an informational edge in financial markets.

The performance reflects a repeatable framework that links forward-looking economic data with macro-exposed financial instruments.

Atlas converts satellite imagery into forward-looking economic indicators that inform both client research and, in this case, a demonstration portfolio used to illustrate how those signals can be applied in financial markets.

Atlas’ Portfolio Performance

Our recent performance is not a statistical gimmick. It is the recognition of a new way of viewing economic value through satellites.

As I’ve written about at length on our Substack, Atlas utilizes geospatial imagery and our proprietary, patent-pending algorithms (ROY and a forthcoming invention we’ll disclose shortly) to ingest pictures of Earth, transform this into economic data, and then use this to predict future GDP performance ahead of government releases.

With this forward-looking view on economic activity, we create price targets for a subset of macro-exposed ETFs and a coherent view of future likely macroeconomic conditions.

These signals can then be applied to financial markets through options or positions in the underlying securities.

The early results have been encouraging.

Atlas Portfolio Performance vs S&P 500

Since May 2024, the Atlas portfolio has produced cumulative returns of approximately 93%.

When compared to the S&P 500, the Atlas Portfolio has 22 percentage points of alpha in the first 10 weeks of 2026, 50 points of alpha over the last year, and 61 points over the last nearly two years.

On a transaction basis, nearly two-thirds have been winners.

Atlas Macro Strategy (One Year: March 2025 - 2026)

• Return: +72.9% annualized

• Volatility: 41.4%

• Sharpe Ratio: 1.69

• Alpha vs S&P: ~21%

A one-year Sharpe Ratio of 1.69, where 1.5 is generally considered strong and 2.0 is considered elite, compares favorably with many hedge fund strategies.

The early results suggest the framework is working.

The Atlas Trading Strategy

As I tell my financial clients, if you can predict GDP with both accuracy and timeliness, you can often anticipate the direction and magnitude of movements in financial markets.

I do this in two ways:

1. Arbitraging the Deviation Between Fundamental Value and Market Price for ETFs:

The market oscillates day-to-day for largely short-term sentiment and macro headlines that traders assign value to, often arbitrarily. I’ve long discussed the difference between fundamental value and market price for equities (for instance, see this article The Tale of Two Prices from the spring 2025).

Traders believe that a war in the Middle East, the assignment of tariffs during Liberation Day and other associated short-term events are selling opportunities. Although they might be, often the moves are much more exaggerated than they should be. Our Atlas signal cuts through this noise based upon 50 years of economic data to answer the basic question that financial analysts should be asking: Has the fundamental basis of economic activity as visible from the satellites changed due to the event?

If the answer is yes, then I update my priors and rebase our financial targets based on the data.

If the answer is no, then in fact this is a buying opportunity, as the move to the downside is overdone.

This is how you make money in the financial markets using statistics, rather than discretionary reaction.

2. Systematic Macro Trading Based on a Forward-Look at the Plausible Economic Environment

If you can predict GDP ahead of the market consensus, you have a vantage point to analyze the likely macro regime of the unfolding epoch. For instance, earlier this year I wrote that, despite calls from the consensus to the contrary, that interest rates from the Federal Reserve would be likely higher (not lower).

How did I come to this conclusion?

As I elucidated here in our article “Powell Paused. The Data May Force the Next Fed Move Higher”, two observations informed this view.

First, GDP was likely stronger than the market was pricing in. (As a side note, I was wrong for Q4 largely due to the Government Shutdown and Net Exports, as I’ve openly admitted. The BEA also revised Q4 growth down to 0.7% from 1.4% on March 13. That revision reinforces how volatile and backward-looking official GDP releases can be and why forward-looking indicators are useful for interpreting the evolving macro environment.)

Second, inflation would likely be more sticky than previously thought. Both CPI and PCE inflation have remained above the Federal Reserve’s 2% target.

As I wrote in Powell Paused: the combination of strong economic growth and sticky inflation is not an environment in which the Fed cuts. In fact, it is the opposite.

Recent minutes from the Federal Reserve’s FOMC show that several members are considering similar risks. And the bond market is waking up to the same reality, with this Bloomberg article suggesting bond yields are rising in anticipation of a higher benchmark interest rate.

Atlas data allowed me to identify and trade this opportunity early.

Two Illustrative Trades

How did I trade this in practice?

1. Arbitraging ETF Fundamental Value vs Sentiment Deviations: Short the XLI

As I stated above, if you can predict GDP, you can predict ETF prices. One example I’ve traded recently is shorting the XLI.

The XLI is the State Street sector spider for industrials. Despite pallid returns across the market this year (the NASDAQ is down 2% and the S&P is down 0.8%), the XLI has jumped nearly 12% at its peak on March 2.

This jump was spurred on bullish sentiment and some economic reality, but it was overbought and our Atlas data cut through that.

On February 22, I shorted it by buying puts (4 contracts, 171 strike price, March 27 expiration). The stock then summarily declined. I closed the contract on March 3 grossing approximately $1500.

As bullish sentiment has over-run its economic basis, I believed the XLI was over-valued and the position benefited when the broader market declined.

2. Systematic Macro: Short the TLT

I wrote three months ago that the consensus was wrong, that economic growth was too strong and inflation too high for persistent rate cuts, and therefore long-duration bonds were at risk of a violent repricing.

I traded this, buying long-dated puts on the TLT (25 contracts, 88 strike price, July 17 expiration).

Bonds rallied in the first two months of the year and I admit, I was nervous. But I stuck to my thesis. This last week, the TLT plummeted, dropping from 90.82 to 86.97 as of close on Thursday.

I closed 10 contracts on Thursday morning (recovering my initial investment); I will let the rest run using the house’s money to earn an asymmetric potential payoff.

The position benefited as Treasury yields rose following stronger economic data and hawkish Fed commentary.

What This Means for You

The Atlas framework is built on a simple premise: if you can measure economic activity earlier and more accurately than the market, you can trade ahead of consensus.

Satellite imagery provides a new dataset through which to observe the economy in near real-time. By converting those observations into macro forecasts and tradable signals, Atlas aims to create a systematic edge in financial markets.

The portfolio performance shown here simply illustrate how those signals can translate into financial market opportunities.

The results so far are encouraging. And as the dataset expands and the algorithms continue to evolve, the opportunity set may grow even larger.

Let's Connect

For the past several years, Atlas Analytics has been developing a simple idea:

If you can observe the real economy directly from space you can measure economic activity before the official statistics are released.

Our satellite-based models track patterns in construction activity, infrastructure usage, logistics networks, and other physical indicators of economic production. From these signals we estimate Core GDP, a measure of the domestic economy consisting of Consumption, Government Expenditures, and Fixed Investment.

The key insight is not only the numerical prediction of those indicators but the turning points. Our algorithms signal shifts in the trajectory of the real economy roughly one quarter before they appear in the official data.

This week we’re releasing an updated comparison between Atlas’ satellite-based estimate of Core GDP and the realized data from the Bureau of Economic Analysis.

The results show that signals captured from space track the underlying trajectory of the economy remarkably well—and may provide an early indication that growth is gradually slowing beneath the surface of the headline numbers.

Satellite Signals vs. Official Data

Since 2023, our satellite-based estimate of Core GDP has shown a strong relationship with the official data released by the BEA.

Across this period, the Atlas model demonstrates:

• Pearson correlation: 0.70

• Root Mean Squared Error (RMSE): ~0.77 percentage points (relative to the BEA’s third revision)

In other words, signals extracted from satellite imagery track the direction and magnitude of economic activity with surprising accuracy.

Even more importantly, the satellite signal typically leads the official data by roughly three months.

That lead time matters.

Government statistics arrive with delays and are often revised multiple times. By the time markets fully digest them, the underlying economy may already be moving in a different direction.

The goal of Atlas Analytics is to observe those movements as they happen.

The latest BEA print was temporarily reduced by the government shutdown at the end of 2025. Adjusting for that effect brings the underlying growth rate closer to ~2%, broadly consistent with the Atlas signal. Over the full sample since 2023, the satellite-based estimates maintain a correlation of 0.70 with official data and an RMSE of ~0.77 percentage points, indicating that the model continues to track the underlying direction of economic activity well.

The investment implication?

Markets react to changes in economic momentum rather than the level of GDP itself. Our satellite signals suggest that shifts in that momentum can be detected roughly one quarter before they appear in the official statistics.

The Trend Is Gradually Slowing

One feature of the data stands out clearly:

Core GDP appears to be trending gradually lower over the past several years.

The economy has not collapsed. Far from it.

But the underlying rate of growth has steadily moderated from the stronger pace seen earlier in the cycle.

Several forces may be contributing to this shift:

• The fading effects of post-pandemic fiscal stimulus
• Higher real rates weighing on investment
• Moderating consumer demand after several years of unusually strong spending

Individually, none of these forces necessarily signals an imminent downturn.

But together they suggest that the underlying pace of economic expansion may be cooling.

If the trend continues, it could eventually lead to a broader slowdown or recessionary environment. For now, the data suggests moderation rather than contraction.

Why Core GDP Matters

Headline GDP can often be misleading.

Two components—Net Exports and Private Inventories—can swing dramatically from quarter to quarter due to factors unrelated to the underlying health of the domestic economy.

To isolate the true momentum of economic activity, Atlas focuses on Core GDP, which captures the more stable components of economic growth: consumption, government spending, and long-term investment.

This measure tends to provide a clearer signal of underlying economic momentum.

However, accurately measuring Net Exports and Private Inventories remains one of the most difficult challenges in macroeconomic forecasting. Both components can create large surprises in GDP releases and often drive the gap between expectations and realized outcomes.

To address this challenge, Atlas is currently developing a new generation of algorithms designed to measure these components directly using satellite and geospatial data.

Early results are promising, and we believe these tools will meaningfully expand our ability to measure the full economy in near real time.

The Trading Advantage of Early Signals

The most powerful aspect of satellite-based macro measurement is timing.

Because Atlas’ Core GDP estimate tends to lead official data by roughly three months, it provides a window into economic momentum before it becomes visible in traditional statistics.

During volatile market periods, such as the turbulent weeks we have seen recently, having a clearer picture of underlying economic activity can be extremely valuable.

It allows investors to position ahead of the consensus narrative.

Using Atlas signals to guide our positioning, our trading strategy is currently up roughly ~6% year-to-date, while several major equity indices have struggled over the same period.

(We plan to share a more detailed performance breakdown in a future post.)

The goal is not simply forecasting.

It is turning economic insight into actionable investment decisions.

What Comes Next

Satellite-based measurement of economic activity is still in its early stages.

Over the past few months, Atlas has begun collaborating with several longstanding institutions in the macroeconomic and policy community to explore how satellite-derived economic indicators can complement traditional statistics.

At the same time, we are expanding our modeling framework to incorporate additional components of economic activity that have historically been difficult to measure in real time.

Together, these developments represent the next phase of Atlas’ work.

We look forward to sharing more about these efforts, and what they reveal about the economy, in the months ahead.

Stay tuned.

The Bottom Line

The satellites are telling a clear story.

The U.S. economy remains resilient but underlying growth appears to be gradually slowing.

And thanks to new sources of data, we can see these shifts months before they appear in official statistics.

That is the power of observing the economy from space.

Let's Connect

I had the pleasure of attending this year’s NABE Economic Policy Conference, “The Great Realignment: Navigating AI, Demographic, and Geoeconomic Change,” and left the meetings energized by both the intellectual honesty and forward-looking tone of the discussions.

The central thread running through the conference was clear: artificial intelligence is no longer a speculative tailwind but an active force reshaping economic performance in real time. Much of the dialogue focused, appropriately, on productivity, where gains may emerge, how quickly they may diffuse, and what it means for labor markets and capital allocation.

But an equally important theme, and one that resonated deeply with me, was measurement.

As AI accelerates the pace of economic activity, our traditional statistical frameworks, survey-based, lagged, and often revised months later, are increasingly strained in their ability to capture what is happening on the ground. Multiple panels touched on the need for new data architectures: higher-frequency, more granular, and alternative datasets capable of observing the economy as it evolves rather than after the fact.

As AI accelerates economic activity, the institutions and frameworks we rely on to measure it will have to evolve just as quickly.

One highlight of the conference was the discussion between Jeff Frieden (a valued contributor here on the Atlas Substack) and MIT’s Daron Acemoglu on the emerging political and economic order. Their conversation explored how technological transformation, industrial policy, and geopolitical fragmentation are interacting to reshape globalization’s next chapter. It was a thoughtful and nuanced dialogue that underscored how economics and political economy are becoming increasingly inseparable in the AI era. Still, the question remains: how will governments and individuals reconcile with the vast and frenetic change brought on by this most recent wave of innovation, and what will that mean for institutions going forward?

Another memorable moment was seeing Christine Lagarde recognized with the Paul Volcker Lifetime Achievement Award. It was a fitting tribute to her stewardship across international finance and central banking, and the standing ovation in the room reflected the depth of respect she commands across the global policy community. But it also felt emblematic of a deeper institutional transition underway. Central banking itself is being pulled into a new measurement regime, one where policymakers are increasingly expected to interpret higher-frequency signals, alternative datasets, and AI-derived indicators alongside traditional releases. The frameworks built for slower, lower-resolution economic data are now being stress-tested by a world that moves, and can be observed, in real time.

Stepping back, what struck me most was the degree of openness, across both public and private sector economists, to new tools and new methodologies. There is growing recognition that understanding the modern economy will require integrating machine learning, alternative data, and interdisciplinary approaches alongside traditional macro frameworks. If economic measurement becomes AI-enabled and more granular, the practical implications are significant: policy reaction times compress, informational advantage shifts toward those measuring activity first, and aggregate GDP becomes less sufficient as a standalone signal.

The future of economic measurement is being written now, and it will be faster, more granular, and increasingly AI-enabled. At Atlas, we are building for that world, not just to interpret the data, but to measure the economy as it unfolds.

This week, the BEA released its first estimate of Q4 2025 GDP at 1.4%, below our Atlas Analytics forecast of 5.2%.

We believe in addressing misses directly.

Forecasting quarterly GDP, particularly at the subcomponent level, is one of the most complex measurement challenges in macroeconomics. This release provides an opportunity to examine where divergence occurred and what it reveals about the structure of GDP itself.

Below is a decomposition of the variance.

Breaking Down the Gap

GDP is the sum of three major contribution buckets:

• Core GDP (Consumption + Government + Fixed Investment)

• Private Inventories

• Net Exports

Our variance relative to the BEA print was concentrated in two areas: Net Exports and Inventories, with an additional meaningful drag from Government spending that downwardly affected Core GDP.

1. Core GDP

Atlas forecasted 2.35pp versus the BEA’s 1.13pp.

The primary divergence within Core GDP came from Government spending, which subtracted approximately 0.9pp from growth following last fall’s government shutdown. That contraction meaningfully reduced headline growth and amplified the overall gap.

Importantly, private-sector activity signals (consumption + fixed investment) remained materially stronger than the headline figure suggests, and if Government spending had remained consistent with previous quarters instead of the protracted shutdown, the gap between Atlas’ Core GDP and the actual would have been negligible.

2. Private Inventories

Atlas projected 0.77pp versus the BEA’s 0.21pp.

While directionally consistent, inventories contributed less to growth than anticipated. Inventory accounting remains one of the most volatile components of quarterly GDP and is often revised in subsequent releases.

3. Net Exports

Atlas forecasted 2.05pp versus the BEA’s 0.08pp.

This was the largest source of variance.

Trade flows, particularly imports, can swing quarterly GDP materially due to timing effects, booking conventions, and seasonal adjustments. Measured trade activity and its contribution to GDP are not always perfectly synchronized in real time.

This quarter highlighted that volatility clearly.

Measurement Is Hard — Especially at the Margin

Quarterly GDP is constructed from thousands of data inputs, surveys, and accounting adjustments. Certain components, particularly Net Exports and Private Inventories, are inherently more volatile and prone to revision.

This release reinforces three realities:

1. Subcomponent forecasting is significantly more difficult than headline alone.

2. Trade and inventory dynamics can overwhelm otherwise stable private-sector growth.

3. Real-time measurement requires continuous refinement.

What We Are Doing

In response, we are:

• Operationalizing additional algorithms and model modules focused specifically on volatile trade and inventory dynamics.

• Continuing to refine our Core GDP tracking, where underlying private-sector signals remain consistent, while expanding our capacity to incorporate atypical government spending shocks and other non-recurring economic events.

• Conducting a full post-release decomposition and lag analysis.

Our objective is not short-term perfection.

It is a durable measurement improvement.

The Broader Signal

Despite the headline print, our Core GDP signal continues to indicate steady underlying economic activity.

Quarterly prints can be noisy.

Economic structure is not.

Understanding the difference between the two is precisely why high-frequency, observational data matters.

Transparency as Our Policy

We will continue to publish both our successes and our misses.

Forecasting is probabilistic. Measurement is iterative. Credibility comes from transparency.

More detailed analysis, including component-level diagnostics and model adjustments, will follow and we will be releasing updates to our key algorithms (including new ones) in the weeks to come.

As always, we appreciate your engagement and thoughtful dialogue.

Leave a comment

As 2025 came to a close, the dominant narrative was clear: the U.S. economy was running out of steam.

Slower consumption, tighter financial conditions, and trade imbalances led many economists to expect a soft finish to the year. Consensus forecasts drifted lower through the fall.

But our models were telling a different story.

While traditional forecasters leaned cautious, Atlas Analytics’ satellite-derived indicators across consumption, trade, and fixed investment remained firm. And now, the official data are beginning to reflect that strength.

Our final forecast for Q4 2025 U.S. GDP comes in at +5.2%, a strong acceleration into year-end.

Here’s how we got there:

The Indicators:

Core GDP
Real-time satellite measurements of industrial activity, energy demand, and consumer intensity pointed to sustained domestic resilience. Core growth contributed +2.6 percentage points, reflecting firm production and steady household demand.

Net Exports
Port-level imagery and shipping volume data showed improving trade flows late in the quarter. As export activity strengthened and the deficit narrowed, Net Exports added +2.1 percentage points to headline growth.

Private Inventories
After weighing on GDP earlier in the year, inventories flipped into a tailwind. Firms rebuilt stock amid solid demand, contributing +0.8 percentage points.

Add it up, and Q4 was not narrow or technical. It was a broad-based expansion across production, trade, and domestic demand.

Where Growth Is Happening

The strength was not confined to the national aggregates.

Our Q4 state-level forecasts show expansion dispersed across much of the country. The Southeast led with above-trend momentum across the Carolinas, Georgia, Tennessee, and the broader Gulf corridor, supported by manufacturing activity and logistics flows.

The Mountain West, including Arizona, Utah, and Colorado, continued to benefit from in-migration, construction, and business formation trends.

National GDP is a headline number and will reflect the sum of the state’s growth— but the signal appears first at the state level. When policymakers and investors focus only on the top-line number, they miss the geographic drivers and often respond after the window to act has closed

Atlas’ state-level GDP forecasts give those decision-makers something they’ve never had before: An independent, real-time read on the underlying economic drivers at the state level, before the official data confirms them.

Let's Connect

On Q1 2026

Early Q1 2026 nowcasts suggest moderation off a strong Q4 base. Consumption remains positive but is cooling. Trade contributions are stabilizing. Inventory accumulation is slowing after companies rebuilt stock levels at year-end.

The Federal Reserve held rates steady in January, and for now we expect policy to remain in a holding pattern. Broader macro-financial risks, including interest rates, fiscal pressures, and global capital flows, remain important swing factors. Looking further into late 2026, credible paths exist in both directions. Growth could firm if demand re-accelerates, or soften further if cooling deepens. Macro-financial risks (rates, fiscal pressures, global capital flows) remain important swing factors, but state-level activity data will provide the earliest signal of which path is emerging.

At Atlas, we remain fully data dependent. As new satellite, trade, and consumption signals come online, they will shape our views in real time and provide early signals of which path is emerging.

Don’t miss our Q4 2025 GDP Forecast video for a deeper dive and stay tuned for our 2026 Q1 Forecast.

Subscribe for Q1 2026 Updates

AI has rapidly become the centerpiece of economic debate, with claims ranging from an imminent productivity boom to warnings of mass displacement, extreme resource extraction to power data centers to the potential of a bubble event.

In this roundtable conversation, Atlas Analytics Founder and CEO, Jake Schneider sat down with Cliff Waldman and Daniel Bachman to discuss where AI is really taking the economy.

Cliff and Daniel bring decades of experience in economic forecasting, productivity, and industrial analysis, and offered thoughtful, data-driven perspectives that complement how Atlas thinks about macro trends and forecasting.

The economists spoke about the gap between AI hype and measurable impact, what past technology cycles can teach us, and what the data suggests about long-term growth. This is a grounded, evidence-based discussion about where AI may matter most over time.

Growth Outlook

The discussion opened with a reminder that most economics forecasters—including the Congressional Budget Office—have not meaningfully revised long-term productivity assumptions in response to generative AI. Trend labor productivity growth remains around 1.4%, roughly where it stood before AI became a mainstream topic.

The reason is historical: major technologies rarely show up cleanly or quickly in aggregate statistics. Electricity, railroads, computers, and the internet all took years before their productivity effects were visible in macro data. Short-term bursts of growth may reflect many forces at once, making attribution difficult.

From this perspective, AI may matter enormously in the long run, while changing very little about near-term growth forecasts today.

AI and the Victorian Railroad Mania

To better understand the future, the discussion weighed the significance of previous market episodes like the dot com bubble, internet boom, and even Britain’s 19th century railroad boom.

“When the British started building railroads, there was a huge financial boom. But in the end, British railroads actually had a lot of trouble in the 19th century being profitable.”

Railroads undeniably transformed commerce, logistics, and economic integration, yet many projects struggled for years to generate profits, despite massive investment and public enthusiasm. The episode illustrated a recurring pattern in economic history: technologies can deliver long-term societal value while disappointing investors in the short and medium term.

Applied to AI, the analogy cautioned that even transformative infrastructure can experience periods of overinvestment, volatility, and financial stress before finding a sustainable role in the economy without invalidating its underlying economic importance.

An Uneven Future, Revealed Only in Hindsight

Still, uncertainty dominates the outlook. AI is not a single technology but a constellation of tools whose effects will vary widely across firms, sectors, and regions. Its success depends not only on algorithms, but on management execution, labor integration, consumer strength, and demographic trends.

If AI accelerates entrepreneurship and enables new industries, it could reinforce long-term growth. If gains accrue narrowly or consumer demand weakens, today’s optimism may fade. As with every major technological shift, the decisive verdict will arrive slowly and only in hindsight.

More on Atlas Analytics forecasting and AI market insights here:

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Cliff Waldman, CEO, New World Economics, LLC., has been an active and in-demand public speaker on topics ranging from the U.S. and global economic outlooks to new markets, productivity, and automation. From 2003 to 2018, Cliff served as Senior Economist and Chief Economist of the Manufacturers Alliance for Productivity and Innovation (MAPI). He has spent nearly two decades writing and speaking on the global economic picture as well as a range of issues of central importance to the manufacturing sector, including productivity, demographics and emerging markets. His career has also included tenures as an economic researcher with a state government forecasting and policy research unit as well as with a small business research team in Washington, D.C. Cliff directed a large contract for the Small Business Administration on the entrepreneurship potential of the veteran and service-disabled veteran population. He has won three national research awards. He received his M.A. in economics from Rutgers University. Cliff can be reached at cliff@newworldecon.com.

Daniel Bachman, Ph.D., is an experienced U.S. and international macroeconomic forecaster and modeler. He is now retired, but from 2013-2024 he was the lead US Economic Forecaster for Deloitte. He also worked as a senior economic analyst and forecaster for the U.S. Commerce Department, Forecaster and model expert for Wharton Econometric Forecasting, and taught economics at Temple University. Mr. Bachman holds an M.A. and Ph.D. in Economics from Brown University and a B.A. in Political Economy from the Johns Hopkins University. Dr. Bachman maintains a blog that uses traditional methods to predict near term GDP, as well as providing opinions and books reviews on other economic topics. Daniel can be reached at danieldbachman@gmail.com and his blog, The Nowcast, can be found on Substack.

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

Two weeks ago, I introduced Atlas Analytics’ contrarian macro thesis.

As a reminder, I wrote:

Recall that up until that point, the consensus belief was: growth is slowing and the Fed will continue to cut.

On Wednesday, Jerome Powell, the Federal Reserve Chairman, paused – just as Atlas has been discussing with our clients.

Here’s where our view diverges even further from the consensus.

Instead of continuing to cut, as the market is pricing in throughout 2026, we see short-term rates going higher – not lower.

A Hawkish Pause?

Powell’s pause was broadly interpreted as benign. Markets barely reacted, and expectations for rate cuts remain firmly in place—if not at the March FOMC, then later this year.

But buried in the Q&A was a subtle shift.

In response to a question, Powell replies:

“The outlook for economic activity has improved… growth is on a solid footing,” and that while the risks to inflation and employment have diminished, they still exist — indicating that future rate moves will be driven by incoming data, not prior expectations.

Not prior expectation, but incoming data will drive future rate moves.

And Atlas Analytics knows that the incoming data will be strong.

The Interest Rate Corollary (Again)

As I wrote in my note Trump’s Challenge Leaves the Federal Reserve At A Crossroads two weeks ago: The corollary to strong incoming data is higher longer-term rates.

Not today, not next month, but – if the data comes in as strong as we expect – the conversation in the FOMC will shift by the summer, introducing the possibility of a rate hike in the fall of 2026 or the winter of 2027.

Atlas’ Preliminary Longer-Term Economic Forecast

So what is our satellite-powered GDP data showing – that the market and the FOMC doesn’t yet know.

Since Q1 2025’s slightly negative print, GDP growth has reaccelerated sharply.

Our satellite data points to approximately +5.0% growth in Q4 2025. While the official advance release was delayed by last fall’s government shutdown, the quarter is complete and the signal is already clear.

Looking ahead, we estimate Q1 2026 growth at roughly +4.5%, ahead of its expected late-April release.

If these estimates are even close, the implication is significant. U.S. growth is materially stronger than the market expects, and interest rates will need to move higher to contain inflationary pressure.

(We’ll be releasing our Q4 2025 video on our YouTube channel on Sunday, February 15th).

Atlas’ Preliminary Longer-Term Rate Forecast

What might the Federal Funds Rate look like if indeed growth is too strong and inflation too sticky?

The result: A policy rate surprise.

Using an applied Taylor Rule (a method that combines inflation and growth rates to arrive at the proper interest rate-level), and then augmenting it for a likely – but not guaranteed – hiking cycle, Atlas Analytics arrives at the chart below.

In plain terms: if growth and inflation evolve as our data suggests, today’s policy stance becomes accommodative—not restrictive—by late 2026.

If growth holds near +4–5% through mid-2026, the first serious discussion of hikes likely emerges in the second half of the year.

In our view, and without anchoring to precise timing, the implication of this chart is a Federal Funds Rate closer to 5.0% than 3.1%, the value proposed by the FOMC’s own Summary of Economic Projections.

What Does This Mean For You?

This is the type of insights made possible through Atlas Analytics’ satellite-based real-time GDP forecasts. We share these insights weekly with our clients so they can get ahead of the market.

While we are currently focused on the United States, Atlas’ mission is to bring satellite-based, real-time GDP insights to every country on Earth.

Whether you are an institutional investor, family office, government agency, or international organization, Atlas provides world-class macro data, financial targets, and economic insight. Reach out to learn more.

Contact Us

Bonus for Our Paid Subscribers: How to Trade This

If our thesis is correct—stronger-than-expected growth, stickier inflation, and a higher terminal policy rate—the implications for asset prices are asymmetric.

Below are three ways to position for a policy rate surprise.

Read more

Time-lapse satellite imagery of Las Vegas reveals the expansion of the built environment in near real time—new construction, site preparation, and infrastructure growth visible months before appearing in official economic statistics from 2015 to 2026.

Fabulous Las Vegas – the city of gambling, goofing off, and perhaps, gauging economic activity.

Previously in A Picture Is Worth A 1000 Words, Part I and Part II, we explored both our key algorithm ROY (named after our founder’s grandfather), and our discovery of a Geospectral Signature that uniquely identifies each space and place by its reflective resonant imaging measurable from outer space.

Today, we return to ROY to explore what it is that Atlas Analytics’ is looking at in our proprietary satellite-based algorithms: the expansion of the built environment.

To this end, the GIF above is illustrative – using time-elapsed satellite imagery, we can literally see new buildings (including Vegas’ newest and grandest structure, The Sphere – notice the top right of our image) being erected in near real-time.

Why Construction?

We’re interested in construction—so let’s make this concrete.

We don’t need to measure the number of cars in Walmart parking lots when we can see that Walmart is building stores–the demand and growth is there. That construction represents real dollars being paid to the local populace in wages, and real dollars that are (partly) spent in the local economy.

Extrapolate this to other sectors of the economy. Increases in the service sector can be seen by construction of hospitals, hotels, and restaurants. Upzoning developed areas for increased housing density allows population growth, increasing a county’s tax base and labor pool.

The blue line shows headline U.S. GDP growth as reported by the federal government, while the red line reflects reported new housing units from the St. Louis Fed (FRED).

The chart above illustrates the relationship between a key component of the construction economy—new privately owned housing starts—and overall U.S. GDP growth over the past 25 years.

How Do We Measure This Change?

There’s an old aphorism: “Cranes in the air, buyer beware.”

But what if you could measure the growth of an area – before the cranes appeared.

The construction of a building has substantial economic impact. Heavy equipment must be transported to the site, land prepared, and foundations poured. Framing follows, then roofs, windows, and doors, before interior build-out, landscaping, parking, and road improvements complete the process.

Each of these steps leaves a distinct multispectral footprint—changes in surface reflectance, material composition, shadow geometry, and activity density—that we observe continuously through satellite imagery.

Because these signals emerge before construction is completed—and often before it is officially reported—they allow us to detect inflection points in local and national economic growth well ahead of traditional statistics.

What It Means for You?

Atlas Analytics measures real economic activity before it appears in official statistics. By observing construction and physical expansion directly from space, we generate signals ahead of government data releases—often weeks or months earlier.

That timing matters. Markets move on expectations, not confirmations. Early visibility into economic inflection points provides meaningful informational advantage to investors, policymakers, and institutions.

We use these signals internally as well. Between November 1, 2024 and November 1, 2025, Atlas strategies informed by this data generated a 54% return.

We publish these insights weekly—translating satellite-derived signals into actionable macro views for investors and institutions. Contact us to learn more.

Last Sunday, Federal Reserve Chair Jerome Powell did something rarely seen from a sitting central banker: he publicly drew a line.

In a public statement responding to growing political pressure tied to “construction costs” and broader policy grievances, Powell made his position unmistakable:

This isn’t about renovations or testimony. It’s about whether monetary policy will be set by economic evidence — or by political intimidation.

That distinction matters more than most market participants currently realize.

Beneath the headlines lies a much larger story — one with direct consequences for interest rates, inflation, and U.S. economic growth in 2026 and beyond.

Powell’s stance matters because the economic backdrop he is responding to is very different from what markets currently assume.

The Consensus View — and Why It’s Wrong

The prevailing market thesis is simple:

• Growth slows

• Inflation fades

• Rates fall through 2026

At Atlas Analytics, we believe the opposite is increasingly likely.

Not because of ideology — but because the data no longer support the consensus narrative.

The Atlas Macro Thesis: Growth Is Accelerating

Here is what the economy is telling us:

• Q2 2025 GDP: 3.8% (BEA final)

• Q3 2025 GDP: 4.3% (BEA first estimate)

• Q4 2025 GDP: ~5.0% (Atlas preliminary estimate)

And our proprietary satellite-based GDP model, ROY, suggests that Q1 2026 growth will remain comparably strong.

This is not a decelerating economy.
It is an economy running well above trend.

The independent real-time nowcasts from the Atlanta Fed’s GDPNow for Q4 (5.3% as of January 14) corroborates Atlas’ economic picture. Yet markets continue to price a slowdown that has yet to materialize.

More on Atlas Insights Here

Inflation Isn’t Gone — It’s Sticky

December CPI tells the second half of the story.

At 2.7% year-over-year, inflation is not surging — but it is clearly above the Fed’s 2% target, and far from conclusively defeated.

With growth accelerating and demand remaining firm, inflation risks are asymmetric to the upside.

This places the Federal Reserve squarely back at the fulcrum of its dual mandate:

• Full employment remains resilient.

• Price stability is increasingly fragile.

In that configuration, the policy implication is not easing.
It is restraint.

The Interest Rate Corollary

Let’s follow the logic to its conclusion.

If:

• GDP sustains growth near 4–5%, and

• Inflation remains above target,

Then the Federal Open Market Committee will face a stark choice:
Defend price stability — or risk a renewed inflation cycle.

History is clear on this point.

In such an environment, the appropriate policy response is not rate cuts.

It is higher real rates.

That means the next major move in U.S. monetary policy is more likely to be tightening, not easing — potentially as early as the second half of 2026.

A Historical Parallel: The Mid-1990s

The closest modern analogue may be the mid-1990s under Chairman Alan Greenspan (my first boss), when the Fed engineered one of the few genuine soft landings in U.S. history.

During that period:

• Growth remained strong

• Inflation risks were contained only through disciplined policy

• The average effective federal funds rate was 5.3%

Today, the effective rate sits near 3.6% — materially below that historical benchmark, despite an economy that may be running just as hot.

Somewhere between those two numbers lies a more realistic equilibrium.

The lesson from the mid-1990s is that strong growth required higher policy rates than today’s to sustain a soft landing.

Why Politics Now Matters More Than Ever

This brings us back to the emerging tension between the White House and the Federal Reserve.

For more than a century, central bank independence has been a cornerstone of U.S. economic credibility, anchoring inflation expectations, stabilizing markets, and insulating monetary policy from short-term political incentives.

That independence is now being openly tested.

The outcome will not merely shape the next election cycle.
It will shape the trajectory of U.S. growth, inflation, and financial stability for decades.

The Atlas View

At Atlas Analytics, our position is clear.

Not politically — but economically.

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

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

Call to Action

This is not just a macro opinion — it’s a positioning framework.

At Atlas Analytics, we use satellite-derived GDP nowcasting and proprietary AI models to help investors anticipate policy inflection points before markets price them in.

If you want to trade this thesis — or stress-test it against your own — we’d welcome the conversation.

Get in Touch