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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, 2026/7 min read/youtu.be

Investing in AI opportunities across markets: Part 1 | J.P. Morgan Asset Management

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"This key word disruption it's like the perhaps the word for this next chapter of AI." - Stephanie Aliaga [00:03:11]

"The length of tasks that these AI models can autonomously complete has been doubling every seven months we're in this like exponential progress here." - Stephanie Aliaga [00:06:55]

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  1. Original source (youtu.be)

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

"Last year the markets forward PE only grew 3% despite an 18% gain in the markets... because they're generating real earnings." - Stephanie Aliaga [00:11:24]

"Every bubble that we've had historically has been heavily reliant on external capital... leverage has been ultimately the culprit." - Stephanie Aliaga [00:11:48]

"I don't think AI is going to automate creativity I think it really empowers it." - Stephanie Aliaga [00:20:12]

"You don't actually need to know a lick about code to vibe code." - Stephanie Aliaga [00:21:34]


Speakers & Credentials

  • Aaron Mulvahill: Host of the Alternative Realities podcast.
  • Stephanie Aliaga: Global Market Strategist on the Market Insights team at J.P. Morgan Asset Management. She transitioned from being a core macro research analyst for Dr. David Kelly to focusing heavily on artificial intelligence and its implications for public and private markets, productivity, and the global economy.

1. Executive Summary

  • The episode kicks off a crossover series on AI investing, focusing explicitly on the deep shifts occurring within private markets and the software sector.
  • Stephanie Aliaga argues that the current AI wave is grounded in fundamental earnings rather than debt-fueled leverage, contrasting today's market conditions with historical bubbles like the dot-com era.
  • A massive structural shift is underway as the barrier to entry for software development collapses via "vibe coding" and agentic AI, forcing a repricing of traditional incumbent software expectations.
  • Crucially, the biggest wealth creation and valuation growth—exemplified by massive private valuations of AI models—is remaining locked in private markets, requiring investors to diversify beyond standard public equities to capture the full AI upside.
  • To weather AI concentration risks and potential disruption, portfolios must balance offensive tech exposure with defensive infrastructure and private market alternatives.

2. Chronological Table of Contents

  • Introduction & Stephanie Aliaga's AI Journey: [00:00:01]
  • The Disruption of Incumbents & Agentic AI: [00:03:11]
  • Model Benchmarks & The Exponential Rise of Task Autonomy: [00:06:24]
  • Evaluating the AI Bubble & Public Market Fundamentals: [00:10:44]
  • The Draw of Private Markets for AI Native Giants: [00:13:08]
  • Portfolio Construction, Diversification & Defensive Assets: [00:16:18]
  • Practical Advice on AI Adoption & "Vibe Coding": [00:19:32]

3. Detailed Thematic Summary

The Dawn of Agentic AI and Software Disruption [00:03:11]

  • Disruption is the defining keyword for the next chapter of AI, moving beyond the hype cycle into tangible applications [00:03:11].
  • The catalyst for this phase is the rapid advancement in coding agents and agentic AI, which can execute complex daily workflows autonomously [00:03:48].
  • Traditional software companies are serving as the "canary in the coal mine" because the barriers to entry for building applications have drastically collapsed [00:04:16].
  • Because anyone can now "vibe code," markets are actively repricing the Annual Recurring Revenue (ARR) expectations of software incumbents, demanding proof of revenue acceleration in the face of AI disruption [00:04:48].

Exponential Progress and "Vibe Coding" [00:06:24]

  • Research group Meter benchmarks AI intelligence not by static exams, but by mapping the length of complex workflows AI can autonomously complete, revealing that this capability is doubling every 7 months [00:06:55].
  • The frontier model race is highly volatile, with leaders like Gemini, Claude, and ChatGPT swapping the "best in class" title on a weekly basis [00:09:40].
  • Stickiness among consumers is surprisingly low; users are willing to swap operating systems to utilize the best current model [00:09:49].
  • Future market dynamics may lead to models specializing in distinct verticals: enterprise programming, consumer-facing tasks, music/video generation, or robotics training [00:10:06].

Dismantling the AI Bubble Thesis [00:10:44]

  • While public valuations are elevated, they are not strictly "bubbly." Last year, forward PE only grew by 3%, despite an 18% market gain, indicating that valuations are backed by actual, realized earnings [00:11:24].
  • The PEG ratio (price for long-run earnings growth) is currently roughly half of what it was during the dot-com era [00:11:32].
  • Unlike historical financial bubbles driven by leveraged debt, the current AI buildout is largely cash-financed and supported by robust underlying demand [00:11:48].
  • However, portfolios are heavily levered to expectations; any disruption to data center timelines, power availability, or component pricing (e.g., memory capacity sold out for 2026) could trigger a severe market repricing [00:12:35].

The Unprecedented Dominance of Private Markets [00:13:08]

  • AI native companies are reaching unprecedented valuations without needing to IPO. Anthropic recently hit a $380 billion valuation, and OpenAI reached $500 billion entirely in the private sphere [00:14:01].
  • Staying private allows these titans to keep their strategic, long-term 5-to-10-year data center plans shielded from public market scrutiny and immediate quarterly pressures [00:14:34].
  • If the three largest private AI companies went public today, they would instantly rank within the top 15 companies in the S&P 500 [00:15:05].
  • Because so much innovation and capitalization is bypassing public exchanges, private equity currently stands out in long-term capital market assumptions as the asset class most likely to achieve elevated long-run forward returns [00:15:51].

Portfolio Optimization and Defensive Posturing [00:16:18]

  • Investors are exhibiting anxiety over deep concentration in mega-cap tech and the inherent risk of disruption [00:16:39].
  • A robust defense mechanism requires integrating infrastructure into portfolios. Infrastructure provides stable income generation while acting offensively by being on the receiving end of massive AI capital expenditures [00:17:01].
  • The global hardware ecosystem and non-US equities must be utilized to lower standard S&P 500 correlation risks, diversifying upside potential across real estate, transport, and international alternatives [00:18:39].

The Reference Vault

4. Data & Figures

Data PointValueContextTimestamp
Market Forward PE Growth (Last Year)3%Indicates earnings caught up with prices; valuation expansion was minimal.[00:11:24]
Market Gain (Last Year)18%The broader market rally matched by solid fundamental earnings growth.[00:11:24]
PEG Ratio (Price/Earnings-to-Growth)Approx. 50% of dot-com peakDemonstrates the market is paying half as much for long-run earnings growth compared to the late 90s tech bubble.[00:11:32]
Memory Pricing/Capacity ForecastSold out for 2026Indicates massive supply bottlenecks and surging costs in hardware infrastructure.[00:12:35]

5. Core Frameworks & Mental Models

  • Vibe Coding: Originated by Andrej Karpathy, this model explains that users no longer need traditional syntax knowledge to build software. Instead, they interact conversationally with AI agents to orchestrate applications, effectively collapsing the barrier to entry in tech. [00:21:40]
  • The Incumbent Moat Framework (Data vs. Innovation): When evaluating legacy software companies, the core question is whether their defensive moat is sustained by the historical proprietary data they hold, or if the true moat moving forward is an innovative spirit and the ability to ask the right AI questions. [00:08:49]
  • Cash-Financed vs. Leveraged Bubbles: A mental model for distinguishing genuine market shifts from catastrophic bubbles. Historical bubbles (like the dot-com or 2008 housing crisis) burst due to external capital leverage and debt. The current AI boom is cash-financed and supported by real B2B demand, making it levered to "expectations" rather than systemic debt. [00:11:48]

6. Anecdotes

  • The Pre-ChatGPT Whitepaper: Stephanie recounts writing a massive 20-page paper on AI's potential impact on productivity before ChatGPT was widely used. She highlights the manual labor involved, likening it to "the old days," contrasting sharply with how quickly information is synthesized using AI tools today. [00:02:03]
  • Avoiding Coding Classes to "Vibe Coding": Stephanie admitted she actively avoided coding classes throughout her academic life. Yet, around the holidays, she decided to try building out her own workflows. She realized that by acting as a "chief orchestrator," she could easily build dynamic tools and dashboards without knowing a single line of traditional code. [00:07:12]

7. References & Recommendations

  • Companies/Models: OpenAI, Anthropic, Gemini (Google), Claude (Anthropic), ChatGPT (OpenAI), Nvidia, Netflix (cited as an analogy for premium AI tool subscription costs).
  • Concepts/People: Andrej Karpathy (coined "vibe coding"), Dr. David Kelly (Stephanie's previous macro research colleague).
  • Research Organizations: Meter (conducts extensive AI benchmark research regarding workflow autonomy and task length).
  • Publications: J.P. Morgan Asset Management's First Quarter 2026 Guide to Alternatives (jpmorgan.com/ta).

"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…

Anthropic Valuation$380 BillionHighlighting the massive scale of wealth generation in the private markets.[00:14:01]
OpenAI Valuation$500 BillionUnprecedented private market capitalization bypassing public IPOs.[00:14:01]
Hypothetical S&P 500 RankingTop 15Where the three largest private AI companies would rank if publicly traded today.[00:15:05]
AI Autonomous Task ComplexityDoubling every 7 monthsThe exponential rate at which AI models are lengthening and managing complex workflows (Meter research).[00:06:55]
Guide to Alternatives ReleaseQ1 2026Publication timing of JP Morgan's guide mentioned at the end of the episode.[00:22:36]