NNuggets
BookmarksCollections
  • About Us
  • Terms of use
  • Privacy policy
  • Disclaimer
  • Copyright & Takedown Policy
  • Community Guidelines
  • Cookie Policy
  • Contact

© 2026 Nuggets

NuggetsMarket PulseCollections

On this page

Speakers & Credentials

  • Speakers & Credentials
  • 1. Executive Summary
  • 2. Chronological Table of Contents
  • 3. Detailed Thematic Summary
  • The Reference Vault
  • 4. Data & Figures
  • 5. Core Frameworks & Mental Models
  • 6. Anecdotes
  • 7. References & Recommendations

On this page

  • Speakers & Credentials
  • 1. Executive Summary
  • 2. Chronological Table of Contents
  • 3. Detailed Thematic Summary
  • The Reference Vault
  • 4. Data & Figures
  • 5. Core Frameworks & Mental Models
  • 6. Anecdotes
  • 7. References & Recommendations
Technology/March 27, 2026/12 min read/youtu.be

Investing in AI opportunities across markets: Part 2 | J.P. Morgan Asset Management

Source
Source
Watch on YouTube ↗

"If I could simplify it from innings I would say there's really like two big acts here that we've seen around AI and act one was just around you know is AI real is AI a big deal..." - Stephanie Aliaga [00:01:08]

"AI is increasingly zero sum one company's profit margin is another company's cost of goods sold and markets are trying to parse through all of that." - Stephanie Aliaga [00:02:54]

References

  1. Original source (youtu.be)

Disclaimer: Orignal content owned by or sourced from third parties. It does not represent the views of 'Nuggets' platform or it's team. AI is used extensively across this platform including for summaries. Accuracy is not guaranteed, there can be mistakes. Any info or content on this platform is not a financial, legal, or investment advice. Do your own research. Refer for complete disclosures:- Terms of Use · Full Disclaimer

Related nuggets

Jun 2, 2026

AI Is Escaping the Screen | 01 Jun 2026 | Coatue

Coatue : AI is entering a new phase: moving beyond digital tools and into fully autonomous systems operating in the physical world. From advanced manufacturing and surgical robotics to robots in the home, the next wave of innovation will b…

Jun 2, 2026

Kalshi Monthly Volume - Politics ($M) | Chart of the Day | Coatue

Coatue: Kalshi's political volume has scaled dramatically, and the American Power Index KPOW is what that scale enables: a single number gauge of the current balance of political power and where markets expect it to move, which Kalshi bill…

Jun 2, 2026

The BlackBerry Problem |18 May 2026 | The Mistakes Series | Malcolm Gladwell's Revisionist History

"My mistake and naivity was to think that people are were with me so you're flying around the world you're trying to get people on side and you think they're on side but they're not mhm mhm and you get blindsight" Jim Balsillie 00:01:34 ht…

Jun 2, 2026

Partnership Perspectives: Network International | 2 Jun 2026 | Brookfield Perspectives

Actions

Reading

Published
March 27, 2026
Read time
12 min read
Progress0%

"In some ways I think we're kind of like tiger parents these days and we've had straight A report cards year after year like anything below that feels like a crisis." - Stephanie Aliaga [00:03:12]

"A year ago coding was maybe helping them write 20 to 30% of their code today the best software engineers in the world are not writing a lick of code." - Stephanie Aliaga [00:08:36]

"If you had relied on pretty sticky revenue for years and years into the future... that does come into question with AI so some of this rethink I think is absolutely warranted." - Stephanie Aliaga [00:13:13]

"We spend so much time as investors worried about the downside... but the reality is one of the bigger risks is discounting the upside risk." - Meera Pandit [00:29:38]


Speakers & Credentials

  • Meera Pandit - Global Market Strategist at J.P. Morgan Asset Management.
  • Stephanie Aliaga - Global Market Strategist at J.P. Morgan Asset Management, specializing in AI's macroeconomic and public market impacts.

1. Executive Summary

  • The AI narrative has rapidly matured over the 3.5 years since ChatGPT's launch, transitioning from a monolithic "Act 1" of undisputed enthusiasm into an "Act 2" characterized by fierce competition, high capital expenditures, and complex zero-sum dynamics.
  • Markets are currently wrestling with dual anxieties: the existential fear that AI works too well and could cause job displacement threatening the 70% consumption-driven economy, and the ROI fear of who actually captures the value of massive infrastructure spend.
  • Despite sentiment-driven turbulence, underlying corporate fundamentals remain incredibly robust, with mega-cap tech achieving double-digit YoY earnings growth and 37% YoY cloud revenue growth, while earnings growth broadens out to the rest of the market.
  • Investors are advised to look beyond concentrated mega-cap names—which currently make up 40% of the top 10 in the index—and diversify into global infrastructure, power utilities, and emerging market beneficiaries to capture the broadening AI value chain while shielding portfolios from disruption risks.

2. Chronological Table of Contents

  • [00:00:05] Introduction and Framing the AI Cycle
  • [00:03:57] AI Anxiety and the Fear of "Working Too Well"
  • [00:06:33] The Rise of Agentic AI and "Vibe Coding"
  • [00:11:46] Disruption, Incumbents, and the Shifting Competitive Frontier
  • [00:16:36] Job Displacement vs. Workforce Augmentation
  • [00:21:13] Parsing the Productivity Boom
  • [00:23:46] Portfolio Positioning and Diversification Strategies
  • [00:27:54] Fundamentals vs. Sentiment: The 2026 Outlook

3. Detailed Thematic Summary

Introduction and Framing the AI Cycle [00:00:05]

  • We are roughly 3.5 years removed from the launch of ChatGPT, marking a rapid evolution in the AI narrative [00:00:50].
  • The market has transitioned from "Act 1," where investing merely meant buying the foundational "picks and shovels" companies, into "Act 2," where the competitive landscape is broadening out [00:01:08].
  • The first major stress test occurred early last year when DeepSeek, a Chinese newcomer, challenged closely held beliefs regarding the invincibility of established AI moats [00:02:05].
  • AI is increasingly viewed as a zero-sum game; one company's expanding profit margin fundamentally represents another company's rising cost of goods sold [00:02:54].
  • Market expectations have become so elevated that investors are behaving like "tiger parents"—expecting absolute perfection and interpreting anything short of straight A's as a fundamental crisis [00:03:12].

AI Anxiety and the Fear of "Working Too Well" [00:03:57]

  • Market anxiety has shifted from fears of an "AI bubble" (overestimating AI) to the existential dread that we might have underestimated AI, worrying about what happens if the technology works too well [00:03:48].
  • There are deep concerns regarding the ultimate ROI of massive AI capital expenditures, especially as advanced compute becomes increasingly expensive [00:04:17].
  • If AI-driven productivity enhancement leads to widespread layoffs, it poses a systemic macroeconomic risk because 70% of the economy is driven by consumer consumption [00:05:47].
  • Fewer workers directly equate to fewer consumers, creating a vicious cycle where society becomes incredibly productive but individuals lack the capital to purchase the produced goods [00:06:12].

The Rise of Agentic AI and "Vibe Coding" [00:06:33]

  • A major inflection point occurred approximately a year ago with the introduction of powerful reasoning models (like DeepSeek and comparable US models) that shifted capabilities from basic next-token prediction to multi-step problem solving [00:06:55].
  • These "agentic" models run parallel agents to research and execute complex workflows, heavily accelerating fields like software engineering [00:07:27].
  • Just a year ago, AI assisted developers by writing roughly 20% to 30% of their code; today, elite software engineers are relying entirely on AI orchestration ("vibe coding") without manually writing code [00:08:36].
  • However, exponential growth in coding automation does not perfectly translate to highly regulated sectors like financials, healthcare, and law, which face significant cyber risks, privacy concerns, and regulatory hurdles [00:09:21].

Disruption, Incumbents, and the Shifting Competitive Frontier [00:11:46]

  • Incumbents relying on historically "sticky" revenue streams are facing severe disruption risks, as upstarts from private markets introduce highly effective, niche AI plugins [00:13:13].
  • While disruption is real, extrapolating this to the imminent demise of all established players is premature; technology adoption follows a linear, jagged path prone to organizational potholes rather than a smooth exponential curve [00:14:04].
  • The core fundamentals of leading tech firms remain exceptionally strong; they are delivering double-digit YoY earnings growth and cloud revenues have surged by 37% YoY [00:15:03].
  • Markets are aggressively punishing companies whose CAPEX upside drastically exceeds their near-term revenue upside, reflecting a failure to appropriately price in the multi-year "J-curve" of technological transformation [00:15:33].

Job Displacement vs. Workforce Augmentation [00:16:36]

  • Rather than outright displacement, empirical evidence suggests AI acts primarily to augment workers, widening the scope of tasks that non-technical employees can accomplish [00:17:37].
  • The premium in the labor market is shifting rapidly away from detailed technical skills and toward adaptability, critical thinking, and problem-solving—skills that maximize the utility of "vibe coding" tools [00:18:51].
  • Fears of mass AI-driven joblessness are currently unfounded in the macro data; the unemployment rate sits at a very healthy 4.3%, signaling full employment with subdued layoffs [00:20:13].
  • Historically, technological transformations (like the internet or social media) tend to add net new workers and spawn entirely new industries rather than permanently shrinking the labor force [00:20:31].

Parsing the Productivity Boom [00:21:13]

  • The final figures for 2025 revealed a stellar productivity growth rate of 2.8%, nearly tripling the pre-pandemic decade average of 1% [00:21:39].
  • Analysts caution against attributing this boom entirely to AI; much of it likely stems from delayed efficiency gains related to pandemic-era work-from-home reorientations and better job matching [00:22:01].
  • Historically, transformative technologies have taken 20 to 25 years to meaningfully reflect in aggregate productivity statistics; even if AI accelerates this, a 5 to 10 year lag is expected [00:22:19].
  • Echoing Rob Solow's famous paradox, massive quality-of-life improvements from AI currently fail to capture cleanly in traditional economic output data [00:23:03].

Portfolio Positioning and Diversification Strategies [00:23:46]

  • Investors sitting passively in standard indices are currently carrying massive concentration risk, with 40% of the index weight consolidated in the top 10 mega-cap names [00:24:29].
  • To hedge bets on the ultimate winners of the AI application layer, capital should be allocated toward the infrastructure layer: power utilities, electrical equipment, and memory/chip manufacturing [00:24:59].
  • Value creation is expanding globally; Emerging Markets (EM) have delivered double-digit performance this year, heavily driven by structural earnings growth in AI hardware hubs like Taiwan and Korea [00:26:25].
  • Significant wealth generation is also shifting toward private markets, with top AI-native entities reaching unprecedented valuations before even considering public offerings [00:26:43].

Fundamentals vs. Sentiment: The 2026 Outlook [00:27:54]

  • The defining dynamic of 2026 is the stark divergence between deeply anxious market sentiment and historically strong underlying fundamentals [00:28:01].
  • The broader S&P 500 is potentially entering its third consecutive year of double-digit profits, with corporate margins resting at record heights [00:28:14].
  • Crucially, the profit story is broadening out beyond mega-cap tech, with the S&P 493 also starting to see double-digit earnings growth [00:29:09].
  • The quality of the market rally has improved drastically; in 2023, only 27% of the rally was driven by actual earnings growth, but by last year, that figure surged to an astonishing 84% [00:28:39].
  • With the Fed maintaining an easing bias, robust fiscal policy in a midterm year, and structural innovations fundamentally transforming the economy, investors face an immense risk by over-indexing on downside fears and structurally discounting upside reality [00:29:15].

The Reference Vault

4. Data & Figures

Data PointValueContextTimestamp
Time since ChatGPT launch3.5 yearsIllustrates the incredibly fast maturation of the AI cycle.[00:00:50]
Consumption Share of Economy70%Used to explain the systemic risk if AI displacement impacts consumer spending power.[00:05:47]
AI Code Generation (1 Year Ago)20% - 30%The historical baseline for how much code AI was writing for software engineers prior to agentic models.[00:08:36]
Mega-Cap Tech Earnings GrowthDouble Digits YoYShowcases the robust fundamental strength of leading tech incumbents.[00:15:03]

5. Core Frameworks & Mental Models

  • The Two Acts of AI Maturation [00:01:08]: A framework dividing the AI cycle into two phases. "Act 1" was characterized by universal enthusiasm and clear-cut investments in foundational hardware ("picks and shovels"). "Act 2" represents a maturing environment marked by intense, broadening competition, zero-sum margin dynamics, and heavily scrutinized ROI.
  • Vibe Coding / Agentic Workflows [00:07:27]: The paradigm shift from models operating as mere "next-token predictors" to multi-step reasoning engines that autonomously deploy parallel sub-agents. This model fundamentally alters how human labor interfaces with technology, shifting the premium from technical syntax execution to high-level creative orchestration.
  • The Technological Productivity J-Curve [00:15:33]: A mental model explaining the disconnect between immediate capital expenditure and delayed monetization. Markets operating on quarterly reporting schedules struggle to properly value the initial heavy investment phase (the dip in the J) before the exponential long-term technological payoff materializes.
  • Productivity: Business Cycle vs. Technological Cycle [00:23:36]: A framework for parsing economic data. When observing productivity spikes, investors must separate short-term business cycle efficiencies (e.g., pandemic work-from-home reorientations) from the structural, long-term secular growth driven by a technological cycle like AI.
  • Fundamentals vs. Sentiment Divergence (The 2026 Dichotomy) [00:27:54]: A framework for understanding current market behavior where psychological sentiment is plagued by existential dread and disruption fears, while the absolute quantitative reality (record margins, 84% earnings-driven rallies) dictates a roaring, fundamentally sound economic environment.

6. Anecdotes

  • The DeepSeek Stress Test [00:02:05]: Stephanie points to the early emergence of DeepSeek—a relatively unknown Chinese upstart—as the first major market stress test. It shattered the closely held belief that established mega-cap tech companies possessed unbreachable ironclad moats, proving that the competitive AI frontier remains incredibly fluid.
  • Tiger Parent Market Expectations [00:03:12]: To illustrate how distorted market reactions have become, Stephanie compares investors to "tiger parents." Because mega-cap tech has delivered perfect "straight A" earnings reports for years, any metric that falls slightly below perfection is instantly treated as a catastrophic failure, rather than normalized maturation.
  • The Internet and the Local Bakery [00:11:10]: Used to explain the difficulty of measuring downstream technological adoption. It is incredibly hard to quantify exactly how much a local bakery benefits from the internet, yet the aggregate reality is a massive structural benefit. AI adoption will likely look the same.
  • Advising University Students on Adaptability [00:18:39]: Recounting a recent trip to her alma mater, Stephanie notes that students are terrified about what specific technical skills they need to avoid obsolescence. She advised them to abandon rigid technical credentialing and instead focus entirely on adaptability, critical thinking, and finding creative ways to orchestrate AI to solve legacy inefficiencies.

7. References & Recommendations

  • DeepSeek: Specifically cited as the AI model/company that served as a major competitive stress test to US tech incumbents.
  • Magnificent 7 (Mag 7) / Mega-Cap Tech: The dominant tech leaders that have driven a vast majority of the initial AI market returns.
  • The S&P 493: The remainder of the S&P 500 outside the top mega-cap tech names, currently cited as starting to experience robust double-digit earnings growth.
  • Rob Solow / The Solow Computer Paradox: Referenced to explain why massive technological utility (like the internet or AI) fails to show up immediately in aggregate macroeconomic productivity data.
  • J.P. Morgan Asset Management AI Hub: The recommended resource for further research on AI's market impact (jpmorgan.com/aihub).

"Brookfield's the largest infrastructure owner in the world... We drew a pipeline and we showed all the different components of the payments ecosystem on a pipeline and said it's like a pipe that moves any commodity except what it's moving…

Cloud Revenue Growth
Up 37% YoY
Highlights the sustained demand for AI-related compute and infrastructure.
[00:15:03]
US Unemployment Rate4.3%Indicates full employment, refuting immediate fears of mass AI-driven joblessness.[00:20:13]
2025 Final Productivity Growth2.8%Represents a massive boom in productivity, nearly tripling pre-pandemic averages.[00:21:39]
Pre-Pandemic Decade Productivity1% AverageThe historically weak baseline that the recent 2.8% productivity surge is measured against.[00:21:48]
Lag for Tech to Hit Productivity Data20 - 25 yearsHistorical norm for how long transformative tech takes to reflect in aggregate macro statistics.[00:22:19]
Top 10 Index Concentration40%Demonstrates the extreme concentration risk for passive investors in the current market.[00:24:29]
Emerging Markets (EM) PerformanceDouble DigitsUnderscores that AI value creation is global, driven heavily by Asian hardware supply chains.[00:26:25]
2023 Rally Driven by Earnings Growth27%Shows the relatively low fundamental backing of the initial leg of the AI market rally.[00:28:39]
Last Year's Rally Driven by Earnings84%Proves the market rally has drastically improved in quality, driven by actual profit generation.[00:28:39]
S&P 493 Earnings GrowthDouble DigitsDemonstrates the broadening out of corporate profits beyond just the Magnificent 7.[00:29:09]