J Curve and Solow Productivity Paradox are at Work with AI

Investors are going to keep challenging firms to show evidence their heavy artificial intelligence investments really are boosting productivity.

That is going to continue being a tough challenge, as history suggests the real output gains will take some time to develop.

So AI "productivity," or the "lack of quantifiable gains," are currently the most significant contemporary case of the Solow productivity paradox. 

In 1987, Nobel laureate Robert Solow famously remarked, "You can see the computer age everywhere but in the productivity statistics."

Recent research suggests productivity might actually decline for a time as firms deploy AI. 

The reason is the J curve. 

“We find causal evidence of J-curve-shaped returns, where short-term performance losses precede longer-term gains,” say economists Kristina McElheran; Mu-Jeung Yang; Zachary Kroff and Erik Brynjolfsson. “Consistent with costly adjustment taking place within core production processes, industrial AI use increases work-in-progress inventory, investment in industrial robots, and labor shedding,

while harming productivity and profitability in the short run.”

In other words, it takes time for enterprises to retool their business processes for the new technologies. And the more profound the innovations, perhaps the longer it takes to integrate those tools. 

Also, much of the reported AI adoption is horizontal rather than vertical; personal rather than systematic. In other words, individuals might be using chatbots, but workflows have yet to be transformed. 

So “personal productivity” has not yet been matched by an applied transformation of key work processes. And personal productivity gains are hard to measure, in terms of impact on firm performance. 

Agentic AI should help, as they can affect complex business processes. 

source: Forbe

Many have noted that  U.S. labor productivity significantly slowed in the 1970s and 1980s, despite rapid information technology investment. 

Then starting in the mid 1990s a decade of faster growth returned arguably because business process re-engineering had taken place.

A similar productivity paradox surrounds AI. As explained by economists Erik Brynjolfsson, Daniel Rock, and Chad Syverson in a 2017 working paper, AI and the Modern Productivity Paradox,” the paradox is primarily due to the time lag between technology advances and their impact on the economy. 

While technologies may advance rapidly, humans and our institutions change slowly. 

Moreover, the more transformative the technologies, the longer it takes for them to be embraced by companies and industries across the economy.

Translating technological advances into productivity gains requires major transformations, and therefore time.

Today, we see a "Modern AI Paradox": while Large Language Models (LLMs) and Generative AI are ubiquitous in headlines and corporate pilots, global aggregate productivity growth  remains sluggish.

Economists like Erik Brynjolfsson argue that the paradox isn't a failure of the technology, but a timing and structural issue. He identifies four main reasons for this lag:

1. Mismeasurement: AI often improves quality, variety, or speed in ways that traditional GDP (which tracks "units produced") fails to capture.

2. Redistribution: AI may be used for "rent-seeking" (competing for market share) rather than increasing total industry output.

3. Implementation Lags: Significant "General Purpose Technologies" (like electricity or the steam engine) require decades of organizational restructuring before they move the needle.

4. Mismanagement: Companies often use AI to automate old processes rather than inventing new, more efficient business models.

Study	Target Group	Productivity Impact Found	Notes on Enterprise Deployment Gaps
MIT/Stanford (NBER)	Customer Support Agents	14% increase in issues resolved per hour.	High-skilled workers saw less gain; impact was greatest on novices. Enterprises often fail to use AI as a "leveler" for training.
Harvard/BCG (SSRN)	Management Consultants	40% higher quality; 25% faster task completion.	"Jagged Frontier": AI failed spectacularly on certain logic tasks where humans over-relied on it, leading to "falling off the cliff" errors.
Microsoft/GitHub	Software Developers	55% faster at completing coding tasks.	Gains are often eaten by "code bloat" and increased technical debt if not managed by senior architects.
Goldman Sachs Research	Aggregate US Economy	Projected 1.5% annual increase over 10 years.	Real-world adoption is currently hindered by power grid constraints and data center infrastructure delays.
NBER / Brynjolfsson et al.	Generative AI & the "J-Curve"	Initial 0% or negative impact.	The "Productivity J-Curve": Measured productivity dips initially as firms invest in "intangible capital" (retraining, restructuring) before the payoff.

While individual tasks show gains, enterprise-wide productivity often remains flat for several reasons:

• The "Pilot Trap": According to recent Adobe/Business research, 86 percent of IT leaders see potential, but only a fraction have moved beyond "isolated experiments" to organization-wide workflows

• Inertial Workflows: Companies often use AI to "do the old thing faster" (e.g., writing more emails) rather than "doing the right thing" (e.g., eliminating the need for those emails entirely). This results in "Digital Overload"

• The Human Bottleneck: AI can generate a report in seconds, but a human still takes hours to verify, edit, and approve it. Without changing the governance and approval structures, the AI speed gain is neutralized

• Data Fragmentation: Most AI models are effective only if they can access clean, centralized data. Most enterprises still have "siloed" data, leading to AI hallucinations or irrelevant outputs

• Skills Gap: Enterprises frequently treat AI as a "plug-and-play" tool like a calculator, failing to realize it requires a new type of "AI Literacy" to prompt and integrate effectively into complex projects.

None of that will be too comforting for suppliers who must justify their heavy AI capital investment. 

But history suggests the payoff is coming. It just will take some time. It always does.

Debating Amazon Leo Objectives

Amazon’s objectives with Leo are debated. 

Is this a standalone telecom business or a strategic infrastructure layer feeding higher-margin businesses (likely AWS)?

The possible motives are complicated as Amazon often talks like a “margin hunter,”  but often acts like a scale builder that tolerates thin margins for a time. 

The trick is that Amazon usually tries to separate where value is created from where it is captured. 

Amazon repeatedly enters markets characterized by low margin and high margin, so “margin” is not the primary consideration.

The effort to find “moats” or bottlenecks where value is extracted, and sometimes a low-margin business can lead to a high-margin moat. 

Layer	Characteristic	Amazon Behavior
Customer-facing layer	Huge TAM, fragmented, price-sensitive	Compete aggressively, often low margin
Infrastructure / platform layer	High fixed cost, scalable, defensible	Invest heavily, aim for high margin long term
Data / control layer	Feedback loops, optimization	Build moats that others can’t replicate

The point is that Amazon doesn’t mind entering a low-margin market if it helps it own a high-margin layer underneath or adjacent to it.

Also, “high capital investment” can be a feature, not a bug:

• High CapEx deters competitors

• Once built, marginal costs drop sharply

• Scale converts fixed costs into a profit flywheel

• Infrastructure can support multiple businesses

• Pricing power eventually comes, once dominance is achieved.

So huge capex commitments are consistent with Amazon’s playbook, if Amazon believes it can control a bottleneck layer.

“Is this a high-margin or low-margin business?” might not be the right question for Amazon leaders, who likely are asking:

• Can we own a critical layer?

• Does this scale globally?

• Does it reinforce our existing flywheels?

• Can we improve cost structure vs incumbents?

• Is there a hidden high-margin component?

So the larger picture is often not the immediate or obvious business, but the ability to create leverage elsewhere. Consumer initiatives such as e-commerce; devices or streaming then can be viewed as demand aggregators and ecosystem lock-in creators that drive revenue indirectly (advertising, cross selling, subscriber lock in).

Enterprise infrastructure plays such as AWS or logistics might be better examples of direct, high margin initiatives.

The thing about Leo is where it fits. From one point of view, consumer telecom is a low-margin, highly-competitive business with high regulatory conditions, low innovation and low growth rates. 

So why even consider it?

Amazon probably envisions non-obvious leverage points:

• Edge computing

• Private 5G networks

• Carrier cloud

• Where Amazon captures high-margin compute, not connectivity

• With different value drivers in consumer and business markets.

Owning a connectivity service could:

• Reduce internal costs

• Improve performance (latency, reliability)

• Be bundled with Prime and devices to

• Drive usage of AWS, the advertising platform and e-commerce

• Support IoT connectivity (devices, logistics, smart home). 

Framed that way, Leo might be viewed as a platform layer supporting:

• Edge cloud

• AWS (compute plus connectivity)

• Telcos as customers

• Prime average revenue per user or account

• Customer retention and acquisition

To be sure, execution will matter. But, in theory, Leo is not directly about high margin. It is about control of what is likely to be a low-margin feature of a higher-margin ecosystem. 

Amazon’s explicit framing is straightforward:

• Create a global broadband access business

• Serving “tens of millions of customers” globally

• in “unserved and underserved” markets

• Offers private connectivity directly into AWS

• for enterprise, government, and telecom customers.

So AWS integration, enterprise and government use cases and private networks might be key, not “consumer telecom.”

Leo then is a connectivity extension of AWS. 

But there are clear risks and some skeptics. 

Optimistically, Leo extends AWS to the edge of the network. 

On the other hand, it is a near-term drag on earnings, in a business with tough economics and financial returns that could take some time to develop.

So it might matter hugely whether Leo can generate AWS pull-through; enterprise demand and other ecosystem upsides. 

Also, how long this takes could matter. 

Layer	Role	Margin Potential
Consumer broadband (Leo ISP)	Distribution / scale	Low
Enterprise connectivity	Premium services	Medium
AWS integration layer	Data + compute + control	High
Ecosystem effects (Prime, commerce, ads)	Indirect monetization	Very high

Sure, it’s risky. But some will point to past Amazon initiatives based on entry into low-margin businesses that provided moats:

• Retail → low margin → enabled AWS scale

• Devices → low margin → enabled ecosystem lock-in

• Logistics → low margin → enabled marketplace dominance.

Leo arguably fits the pattern, optimists will argue. It’s about high-margin AWS, not low-margin telecom. Skeptics will worry about the execution risk. 

Cardboard Boat Race, Golden, Colorado

What do geeks at Colorado School of Mines do for fun on an April weekend? Build boats out of cardboard and duct tape. 

Will they float? Will they capsize at the first rapid? Many do.

But some boats and teams do make it to the finish line without swamping, having their boats disintegrate or getting grounded and disqualified. 

The water level in Clear Creek is very low, so grounding was common. 

Great fun. 

Musical Creativity is Hard to Sustain, Apparently

As I was sitting in church recently, I wondered why the worship music I was hearing did not strike me as powerfully as some of the contemporary Christian worship music from the 1995 to 2015 period or thereabouts. 

Some might point to such examples such as::

• Hillsong (Darlene Zschech's "Shout to the Lord," 1993, then the broader Hillsong output through the 2000s)

• Chris Tomlin, Matt Redman, David Crowder — the "worship pastor as recording artist" era

• Michael W. Smith, Paul Baloche, Tim Hughes

These songs, from that era, crossed denominational lines, being sung in Catholic parishes, black Baptist churches, and Anglican cathedrals alongside evangelical megachurches.

More recently, I admit I hear few newer compositions that seem to have that same resonance, and just wondered whether bursts of musical creation have happened before. 

Maybe the creative cycles are inevitable, though. You might note that some of your favorite musical groups or singers in general also are not equally commercially successful throughout their careers.

Some might argue the history of music is actually full of concentrated creative bursts followed by relative fallow periods:

The Viennese Classical Period (~1770–1800) Haydn, Mozart, and the early Beethoven produced a staggering concentration of the foundational classical repertoire in roughly 30 years. The generation immediately after struggled to define itself in that shadow. 

The Florentine Camerata and Early Opera (1590s–1640s) The invention of opera produced a creative explosion — Monteverdi, Peri, Caccini. Then a relative consolidation period before the Baroque opera seria fully matured.

Tin Pan Alley's Golden Era (~1920s–1950s) Gershwin, Porter, Rodgers & Hart, Berlin, Arlen — a concentrated burst of the "Great American Songbook." The late 1950s saw it run dry just before rock replaced it entirely.

The British Invasion / Classic Rock Peak (~1965–1975) An almost absurdly dense decade of canonical output. By the late 1970s, critics were already writing "rock is dead" pieces.

Bebop and Hard Bop Jazz (~1945–1965) Miles, Coltrane, Monk, Parker — followed by fusion, which many felt diluted rather than advanced the tradition.

Scholars of creativity point to a common structure:

• A new platform or technology opens up (the printing press for Luther, the concert hall for Vienna, recording for Tin Pan Alley, the megachurch and Christian radio for the worship era)

• A small, networked community of talented people cross-pollinate intensely

• There's a genuine felt need the music meets — social, spiritual, political

• Then institutionalization sets in, the pioneers age or scatter, and the field fills with imitators.

The worship music burst fits this template almost perfectly, some might argue. 

“Paradoxically, it was the success of Christian music that led to its failure,” says Derek Walker, writing for Christianity Today. “The industrialisation that managed people’s demands for Christian music ended up being the cause of its own demise.”

In the 90s, sales of Christian music albums exceeded those of classical, jazz and new age music. Supply could not keep up with demand. Record companies had to keep signing bands, just to keep the machinery of business going, feeding the playlists of Christian radio stations in the USA with new material. 

Lower quality was the result, he argues. The market was saturated with undistinguished material. Streaming might have fragmented the audience, while formats became clichéd, he says. 

That might be less directly true for liturgical music, rather than contemporary Christian music. But I still do not hear new compositions of great power.