The current AI-driven stock market rally may not be an asset bubble yet.

In recent years, the current AI-driven stock market rally may or may not turn out to be another major asset bubble in global human history.

For the practical purposes of asset bubble assessments, we describe, discuss, and delve into the new, non-obvious, and useful technological advances in artificial intelligence (AI) in the broader context of another potential asset bubble around the world. Specifically, we focus on the 3 different layers of the current AI-driven stock market rally in many countries, regions, and jurisdictions worldwide. These 3 mainstream layers pertain to: (1) the bottom massive AI infrastructure with hefty capital investments in cloud data centers, electric power grids and several other alternative energy solutions, semiconductor microchips, graphics processing units (GPU), tensor processing units (TPU), and many other application-specific integrative circuits (ASIC); (2) the middle platform hyperscalers for cloud services, large language models (LLM), and many other multi-modal models; and (3) the top software applications with creative model context protocols (MCP), software development kits (SDK), and application programming interfaces (API) keys, modules, projects, and several other smart software solutions. In recent years, the current AI asset bubble concerns revolve around at least 3 key developments in some strategic sectors worldwide. These strategic sectors span law, finance, medicine, healthcare, trade, taxation, technology, science, and even education. In particular, these key developments span: (1) the significant, pervasive, and ubiquitous increase in stock market valuation in terms of P/E, P/B, and P/S metrics for the Magnificent 7 tech titans and many upstream suppliers, microchip manufacturers, graphics card designers, and cloud service providers as part of the global supply chain for new, non-obvious, and useful AI-driven technological advances; (2) the recent massive capital investments in AI infrastructure worldwide via Stargate, SoftBank, State Street, BlackRock, S&P, KKR, Blackstone, and the Magnificent 7 tech titans etc; and (3) the increasingly circular AI investments in the major market players such as OpenAI, Nvidia, AMD, Broadcom, Qualcomm, Oracle, and Cisco etc within the broader global AI market system. Amid these recent key developments, we assess the current concerns, worries, and several other negative expert views, opinions, and judgments in relation to the potential risks, threats, and headwinds for the recent AI stock market rally worldwide. In recent years, the current AI-driven stock market rally may or may not turn out to be another major asset bubble in global human history.

 

Our assessments shine new light on the fact that the vast majority of the current P/E, P/B, P/S ratios, and several other metrics for the AI tech bellwethers remain reasonably below the dotcom peaks although some major features of the current AI stock market rally rhyme with the past asset bubbles in global human history. These major features span the bizarre circularity of massive capital investments in AI infrastructure among the key market players such as OpenAI, Nvidia, AMD, Broadcom, Qualcomm, Oracle, Cisco, and so forth. In effect, this recent bizarre circularity blurs the boundaries between clients, suppliers, and cloud service providers within the broader global AI market system. Several recent mergers and acquisitions (M&A), R&D outlays, and other capital market deals further exhibit such similar circularity.

 

Unlike the past asset bubbles in global human history, the current AI stock market rally looks fundamentally robust because the Magnificent 7 tech titans, microchip manufacturers, hyperscalers, and other cloud service providers continue to produce substantial worldwide sales, profits, and free cash flows in recent years. In close collaboration with their global supply-chain partners such as TSMC, Broadcom, Qualcomm, and Samsung, these major market players further continue to declare cash dividends, share repurchases, and employee stock options in recent years. Specifically, these fundamentally robust, stable, and healthy capital market behaviors provide the vital proof of concept for many mainstream AI platforms, infrastructure networks, cloud services, and computationally intense software applications etc in stark contrast to the past asset bubbles, especially the U.S. dotcom asset bubble of 1999-2000 for many Internet companies and the subsequent U.S. residential real estate asset bubble of 2006-2008 for banks, insurers, and mortgage credit providers.

 

In addition, our assessments further highlight some similarities but key differences between the current AI-driven stock market rally and the vast majority of past asset bubbles in global human history. Today, the U.S. AI tech leaders retain rich fortress balance sheets, liquid and massive cash assets, and unique competitive advantages in the global markets for AI platforms, cloud services, software applications, graphics cards, and quantum computers. In combination, these fundamental strengths empower these AI tech titans to secure substantial sales, profits, and cash flows in support of future further M&A and R&D deals, capital investments, and cloud platform operations. To the extent that billions of global users now need AI-driven disruptive innovations from large language models (LLM) and smartphones to virtual reality (VR) headsets, smart glasses, and metaverses, we would expect the worldwide demand for new AI-driven technological advances to be substantially better, smarter, and greater in the next few decades. In this broader context of global market development, the current AI-driven stock market rally may not be an asset bubble yet. For the foreseeable future, we would expect the 3 major foundational models, Microsoft-OpenAI ChatGPT and Copilot, Google Gemini, and Anthropic Claude to dominate the global markets for AI-driven platforms, search engines, cloud services, chatbots, and software applications etc. In effect, these foundational models tend to outperform many other generative artificial intelligence (Gen AI) large language models (LLM) such as Meta Llama, Apple Siri, Amazon Alexa and Nova, Twitter-SpaceX-xAI Grok, Alibaba Qwen, DeepSeek, Perplexity, Jasper, Mistral, Midjourney, and Synthesia among many other alternative outlets.

 

Specifically, Google Gemini now integrates the built-in best-in-class Google Search tool for real-time Retrieval-Augmented Generation (RAG). Also, Google Gemini embeds many multi-modal features, functions, and benefits via better, smarter, more accurate, and more granular computer vision, voice, audio, video, animation, and even large-scale Monte Carlo simulation etc. With these state-of-the-art multi-modal capabilities, Google remakes, reshapes, and reinforces the extant mainstream foundational models such as Gemini (LLM), NotebookLM (multi-modal content curation, generation, and automation with real-time RAG), Nano Banana (creative image generation), and its broader suite of AI-driven online software applications such as Gemma (open-source software), Imagen (text-to-image generation), Lyria (music generation), Veo (video generation), and many more. In addition to Microsoft-OpenAI ChatGPT and Copilot and Anthropic Claude, Google Gemini seeks to integrate the vast majority of these mainstream multi-modal features, functions, and benefits across the full suite of AI-driven SDK and API user keys, modules, projects, platforms, cloud services, and software applications for better online personal search experiences. For Google, these mainstream multi-modal features, functions, and benefits combine to further bolster the tech titan’s economic moats, online search networks, platform lock-in effects, competitive advantages, and even financial resources in terms of stable sales, profits, and free cash flows for the foreseeable future.

 

Artificial General Intelligence (AGI) remains the only way for most macro-financial economists to justify the recent massive global data center buildout in the next few years. For the practical purposes of asset bubble assessments, we would expect the recent AI infrastructure buildout to cost several trillion dollars by 2030. When the 3 major AI foundational models reach AGI with all kinds of dynamic capabilities for modern knowledge workers and subject matter experts in law, finance, medicine, healthcare, trade, taxation, technology, science, and even education, the Magnificent 7 tech titans, cloud hyperscalers, and graphics card providers are likely to benefit substantially from the current AI-driven business cycle in the next few years. Specifically, these AI tech leaders can benefit from hefty gross margins, net profit margins, and cash flow yields as AGI almost always drives down large fractions of the average overhead costs of data center maintenance worldwide. In time, AGI can probably propel the next wave of scale economies for the AI tech leaders within the global market system. In essence, these scale economies tend to further bolster the platform lock-in effects, competitive moats, and network cascades in favor of the major AI tech leaders.

 

In practice, many macro-financial economists regard the recent AI asset bubble concerns, worries, and other negative expert views, opinions, and judgments as overblown in recent years. As the Magnificent 7 tech titans, cloud hyperscalers, graphics card manufacturers, and software service providers continue to produce hefty, robust, and stable sales, profits, and cash flows worldwide in the current path of least resistance toward AGI, we would expect to see greater strategic interdependence across the global AI value chain. In turn, this greater strategic interdependence manifests in at least some of the circularity in the recent flagship AI capital investments between OpenAI, Nvidia, AMD, Broadcom, Qualcomm, Oracle, and Cisco etc. In the best likelihood of success, we would expect the American AI tech titans to extract at least $8 trillion to even $12 trillion of the $20 trillion total economic value of AGI over the next couple of decades. From this new normal perspective, AI ecosystem circularity is less as artificial inflation and substantially more as a key reflection of strategic interdependence between these core AI partners across both best-in-class hardware and software requirements. For these reasons, the current AI-driven stock market rally is not fundamentally a hope-and-hype asset bubble like the past Internet dotcom era.

 

We believe there are still some valid, fair, and reasonable concerns, worries, and other negative expert views, opinions, and judgments on the current path of least resistance toward AGI. Today, the vast majority of the mainstream Gen AI LLM foundational models, machines, robots, agents, and avatars remain far from AGI despite some incremental improvements over the past few years since OpenAI’s launch of ChatGPT in November 2022. In essence, most mainstream AI models, machines, robots, agents, and avatars remain autocomplete on steroids in the sense that these advances rely on next-token probabilistic predictions rather than true human-like perceptions. Meanwhile, we cannot be completely sure whether the current AI tech titans can become the ultimate beneficiaries in the current global race toward AGI. At the same time, however, we believe the bulk of AI economic value tends to concentrate in the Magnificent 7 tech titans, platforms, cloud hyperscalers, microchip manufacturers, and the vast majority of upstream suppliers, graphic card designers, and several other hardware service providers in the global markets for new, non-obvious, and useful AI-driven cloud platforms, technological advances, and disruptive innovations. In this broader context of global market development, we believe it would be wise for investors to further diversify their stock market investments across a wide spectrum of strategic AI market niche opportunities.

 

Today, the vast majority of the current P/E, P/B, P/S ratios, and several other metrics for the AI bellwethers remain reasonably below the Internet dotcom peaks although some major features of the current AI stock market rally rhyme with the past asset bubbles in global human history.

As part of our broader fundamental analysis of each of the Magnificent 7 tech titans, we can assess the current AI stock market rally across 3 different layers. At the bottom layer, we continue to witness the ongoing AI infrastructure buildout. This AI infrastructure buildout spans massive nuclear and hydrogen power plants, efficient electric power grids, and alternative energy solutions such as solar panels and wind turbines; massive cloud data centers; and graphics processing units (GPU), tensor processing units (TPU), and several other application-specific integrative circuits (ASIC) in many different countries, regions, and jurisdictions worldwide. In the meantime, we believe the global demand for smart computational power continues to outstrip the collective dynamic capabilities of Nvidia, AMD, Broadcom, and Qualcomm in association with their high-performance graphics cards and other semiconductor microchips. At the middle platform layer, several Gen AI-driven tech titans have transitioned from training, running, and applying the new foundational models to large-scale datasets with general-purpose cloud services, model context protocols (MCP), application programming interfaces (API), software development kits (SDK), and smart software products, services, and solutions. In recent years, at least some of the cloud hyperscalers have been able to significantly scale up their major foundational models for smarter, faster, better, more accurate, and more granular inferences across all the major mainstream fields such as law, finance, medicine, healthcare, trade, taxation, technology, science, and even education. At the top application layer, we continue to see many mainstream state-of-the-art Gen AI large language models (LLM). These Gen AI foundational models include Microsoft-OpenAI ChatGPT and Copilot, Google Gemini, Anthropic Claude, Meta Llama, Apple Siri, Amazon Alexa and Nova, Twitter-SpaceX-xAI Grok, Alibaba Qwen, DeepSeek, Perplexity, Jasper, Mistral, Midjourney, and Synthesia among many other alternative software outlets. Today, the vast majority of global users still prefer to leverage the free-tier versions of these Gen AI-driven disruptive innovations; and only some superusers pay to use the premium versions of these new AI models, machines, robots, agents, and avatars for greater human productivity gains. For the foreseeable future, we would expect the 3 major massive foundational models, specifically Microsoft-OpenAI ChatGPT, Google Gemini, and Anthropic Claude, to dominate the global markets for AI-driven online platforms, search engines, cloud services, chatbots, and several smart software applications. For this reason, we believe significant business model monetization remains quite elusive for the vast majority of the other smaller, less powerful, and more computationally complex market options.

 

The current AI infrastructure buildout has gone on for a much longer lifespan than many macro-financial economists expected at first glance as the mainstream Gen AI-driven tech titans launched their foundational models in recent years. To the extent that it takes significant computational power to train these foundational models with hundreds of billions of parameters for better inferences, we would witness another longer lifecycle of global demand for computational power in support of this Gen AI-driven global market development over the next couple of decades. In practice, this high level of Gen AI model development has recently begun to percolate to the platform layer. In time, this platform layer stands on the shoulders of giants who continue to engage in the current AI infrastructure buildout with massive data centers worldwide; efficient electric power grids, nuclear and hydrogen power plants, alternative energy solutions such as solar panels and wind turbines; and graphics cards, and several other application-specific integrative circuits (ASIC). From Snowflake and ClickHouse to Databricks and MongoDB, the platform companies provide cloud software solutions for relational data management, retrieval, and safe storage for enterprise use cases. In recent years, these platform companies continue to experience a major widespread acceleration in sales growth, profitability, and cash capacity etc due to the current global race toward AGI. Another platform company, Salesforce’s Data Cloud, now produces almost $1 billion annual sales with triple-digit topline growth in recent years. In this broader context of global market development, both the Gen AI-driven tech titans and platform companies continue to enjoy significant economic tailwinds across the top application layer and the middle platform layer on the solid basis of massive AI infrastructure buildout worldwide. Across the 3 major layers of the current global AI-driven technological advancements, many of these AI tech leaders are now in a much better strategic market position with new and non-obvious fundamental strengths, economic moats, network effects, and competitive advantages than they were back in the Covid pandemic crisis. Globally, billions of users continue to cast their vote of confidence in the mainstream state-of-the-art Gen AI models such as Microsoft-OpenAI ChatGPT and Copilot, Google Gemini, and Anthropic Claude.

 

For computational power, most of the mainstream product lifecycles rely heavily on freemium users, corporate clients, and retail consumers worldwide. In this broader context, we can draw a useful and relevant analogy from Apple’s iPhone revolution worldwide: retail consumers owned, enjoyed, and adopted iPhones well before iPhones became ubiquitous in workplaces. In recent years, the current Gen AI-driven models, systems, chatbots, and smart software solutions tend to play out the same way. Today, many enterprises work within their extant IT budgets, gain employee buy-in, and build new AI-driven products, services, and other software applications for widespread user adoption at the global scale. In some use cases, however, these new AI technological advances may end up disrupting the current workflows for some enterprises. As a result, we would witness slower, less widespread, and more restrictive enterprise adoption than retail consumer adoption in most of the next-gen mainstream AI-driven product cycles worldwide.

 

In the new normal view, we believe many mainstream next-gen AI-driven disruptive innovations combine to advance human productivity in law, finance, medicine, healthcare, trade, taxation, technology, science, and even education. For this reason, the broader U.S. capital system seems to regard the current AI infrastructure buildout with massive capital investments as worthy risks. In time, these worthy risks come with relatively high, stable, and robust rewards in the common form of global sales, profits, and free cash flows for the Magnificent 7 tech titans, cloud hyperscalers, microchip manufacturers, and online platform companies etc. In the late-1990s, the massive capital investments in fiber optic networks worldwide led to the classic Internet dotcom asset bubble of 1999-2001. However, the unique worldwide capacity further allowed the Internet to flourish in due course. Across the wider global supply chain for online platforms, cloud services, and high-performance quantum computers, everyone eventually became better off as a result of the massive capital investments in fiber optic networks worldwide. This time may not be different. Today, many of the Magnificent 7 tech titans, platform companies, cloud hyperscalers, and microchip manufacturers continue to invest heavily in high-performance computation in the common form of AI graphics cards, data centers, and efficient electric power networks worldwide etc. Despite some recent bizarre circularity within the global AI-driven capital system, most of the mainstream AI tech leaders use, apply, and leverage their cash flows to finance these recent massive capital investments in AI high-performance computation worldwide. In time, we would witness some of the circular capital investments to pay off in terms of stable sales, profits, and further free cash flows for these AI tech leaders within the global markets for new AI-driven online platforms, search engines, cloud services, chatbots, and many other smart software applications. At the same time, however, we would expect only the 3 to 5 major AI tech leaders to dominate each of the oligopolistic global markets in due course. For the foreseeable future, the 3 major massive Gen AI foundational models, specifically Microsoft-OpenAI ChatGPT, Google Gemini, and Anthropic Claude, are most likely to win the current global race toward AGI. In effect, we believe only these 3 major Gen AI bellwethers can earn excess returns on their respective costs of capital with smart tokenization in the next couple of decades. For this reason, significant business model monetization remains a bit elusive for the vast majority of the other smaller, less powerful, and more computationally intense market options. These alternative market options span both proprietary and open-source Gen AI foundational models, machines, systems, robots, agents, and avatars such as Meta Llama, Apple Siri, Amazon Alexa and Nova, Twitter-SpaceX-xAI Grok, Alibaba Qwen, DeepSeek, Perplexity, Jasper, Mistral, Midjourney, and Synthesia among many other alternative outlets worldwide.

 

However, some of the common characteristics of the current AI stock market rally rhyme with some past asset bubbles. Today, several AI-driven private companies attract substantially higher market valuations than their public counterparts. Many macro-financial economists often tend to value these private companies on the standalone basis of both annual sales and sales growth rates over recent years. In stark contrast, most macro-financial economists often tend to value the public companies in terms of many more metrics, financial statements, statistics, and stock market investment criteria such as gross margins, net profit margins, free cash flows, fortress balance sheets, debt-to-equity capital ratios, stock returns, and their respective recent growth rates. Also, most macro-financial economists often compare and contrast the recent financial profiles of public companies to some broader baseline market benchmarks for some strategic sectors. For these reasons, the private and public markets often tend to use 2 diametrically different market valuation frameworks. We believe any wide divergence between these 2 major market valuation frameworks can often indicate undue risks, threats, and vulnerabilities in the global capital system. To the extent that some of the recent AI-driven private companies seek astronomical stock market valuations when these tech leaders go public in the next few years, we now respectfully regard these recent market valuations as relatively outsize. Today, these AI-driven private companies with outsize market valuations include OpenAI, xAI, SpaceX, Anthropic, ByteDance, Canva, Waymo, Cursor, and Perplexity etc.

 

For many of the AI-driven Magnificent 7 tech titans, cloud hyperscalers, and microchip manufacturers, the current stock market valuations remain well above historical norms. At the same time, however, these AI-driven stock market valuations remain reasonably below their Internet dotcom peaks in 1999-2000. From mergers and acquisitions (M&A) and R&D outlays to capital expenditures, these recent capital market activities further remain well below the prior peaks in the Internet dotcom era of 1999-2000 and Global Financial Crisis of 2007-2008. In recent years, the U.S. IPO market seems to be substantially more selective because the average deal has become much larger after the Covid pandemic crisis worldwide. For all these reasons, the current AI-driven stock market rally may not be an asset bubble yet. In the new normal view, we would not expect to see pervasive AI-driven stock price run-ups and subsequent dramatic declines in stock market valuations for the Magnificent 7 tech titans, cloud hyperscalers, and microchip manufacturers in the next few years. Today, most of these AI tech leaders tend to trade at the baseline P/E ratios of 27 times to 33 times versus the P/E benchmarks of 21 times to 25 times for S&P 500, Dow Jones, Nasdaq, NYSE, and MSCI USA. After the Covid pandemic crisis of 2020-2022, these AI tech leaders continue to produce stable sales, profits, and free cash flows from their foundational models. As a result, these AI tech leaders continue to declare cash dividends, share repurchases, and employee stock options in recent years. This cash capacity draws a distinction between the current AI tech leaders and their Internet counterparts back in the dotcom era from the mid-1990s to 2000.

 

In recent years, financial leverage has begun to emerge in the current AI market system. Oracle completed an $18 billion bond sale to finance the extant AI-driven cloud operations worldwide. Also, CoreWeave secured significant corporate debt capital instruments to further support the AI-driven GPU infrastructure networks, high-performance cloud services, and several other online software solutions. Specifically, there was an almost one-to-one correspondence between debt and equity for many of the residential real estate trusts (REIT) in support of massive cloud data centers worldwide in the past few years. At this stage, the American capital system tends to combine 55% debt with 45% equity to bolster the current AI-driven high-performance cloud operations, smart software services, and online platforms for better customer relationship management, worldwide man-machine collaboration, and human productivity. In most use cases, the AI-driven corporate sponsor provides collateral to back the equity portion of the debt-driven capital structure as part of the current U.S. capital system.

 

Another valid asset bubble concern pertains to the bizarre circularity of massive capital investments in AI infrastructure among the key market players such as OpenAI, Nvidia, AMD, Broadcom, Qualcomm, Oracle, and Cisco etc. In recent years, OpenAI and Nvidia entered a strategic partnership to deploy 10 gigawatts of AI data centers with Nvidia’s Blackwell GPU high-performance infrastructure. Also, OpenAI and AMD entered another strategic partnership to deploy at least 6 gigawatts of AMD’s GPU high-performance infrastructure. Further, OpenAI signed a new deal to purchase $300 billion high-performance computation from Oracle and Cisco in the next 5 years. Over the next couple of decades, Nvidia agreed to invest up to $100 billion in OpenAI, Anthropic, and some other AI tech leaders. Broadcom and Qualcomm entered new strategic partnerships with OpenAI, Microsoft, Meta, Google, Amazon, and Anthropic to deploy more than 10 gigawatts of AI-driven custom graphics cards, microchips, and several other application-specific integrative circuits (ASIC) over the current decade. In effect, this bizarre circularity blurs the boundaries between clients, suppliers, and cloud service providers within the broader AI ecosystem. Several recent mergers and acquisitions (M&A), R&D outlays, and other capital market deals further exhibit such similar circularity. Despite this recent bizarre circularity within the AI-driven capital system, most mainstream AI tech leaders use, apply, and leverage their free cash flows to finance these recent capital investments in high-performance computation worldwide. In time, we would witness some of the circular capital investments to pay off in terms of stable sales, profits, and further free cash flows for these AI tech leaders within the global markets for AI-driven online platforms, search engines, cloud services, chatbots, and smart software applications. At the end of the day, there will be both winners and losers in several strategic sectors. At any pace, such similar circularity should not slow down these mainstream AI tech leaders in the global race toward AGI.

 

The future cumulative economic value of Gen AI can ultimately justify the current massive AI capital investment boom worldwide.

In the post-pandemic years, the current AI stock market rally arises from massive capital investments in AI-driven hardware infrastructure networks worldwide. These AI-driven hardware infrastructure networks span new cloud data centers, electric power grids, nuclear and hydrogen power plants, high-performance graphics cards, and several other custom semiconductor microchips. With these AI hardware infrastructure networks, the major AI-driven tech titans train their built-in best-in-class foundational models with hundreds of billion parameters; process prompts, queries, and inferences across their Gen AI large language models (LLM); and further decipher online personal search experiences across all of the mainstream Internet search engines, social media platforms, and smart software outlets. Despite some recent asset bubble concerns, worries, and several other negative expert views, opinions, and judgments in relation to new AI-driven circular capital investments, we continue to regard the current AI-driven capital investments as sustainable over the next couple of decades. In the current global race toward AGI, whether these major AI tech leaders emerge as the ultimate winners remains an open question. In recent years, we continue to see substantial stock market concentration in the Magnificent 7 tech titans, online platforms, cloud hyperscalers, and microchip manufacturers.

 

As part of our fundamental analysis of each of the major AI-driven tech titans, our asset bubble assessments focus on 2 major value drivers in support of the recent massive AI capital investments worldwide. First, the vast majority of the current AI-driven online platforms, cloud services, and smart software applications continue to deliver a widespread rapid acceleration across both task automation and human productivity. To the extent that these new AI technological advances displace many knowledge workers, we would expect to see substantial declines in labor costs across some strategic sectors such as law, finance, medicine, healthcare, trade, taxation, technology, science, and education. Some recent survey estimates show a 15% gross increase in U.S. labor productivity after the next-gen complete user adoption of AI-driven technological advancement over the next couple of decades. Also, some recent academic studies and corporate anecdotes support 25% to 35% human productivity gains in the next 5 years after AI-driven cloud software deployment for customer relationship management, worldwide man-machine collaboration, and other subject matter expertise. In this broader context, we believe substantial human productivity gains serve as one of the 2 major value drivers in support of the massive AI-driven capital investments worldwide in recent years.

 

Second, it takes significant high-performance computational power for AI-driven knowledge workers and subject matter experts to unlock substantial human productivity gains in due course. High-performance computation requires both efficient energy solutions and foundational model inferences. In recent years, the high-performance computational power for training large language models (LLM) continues to grow more rapidly in increments of 400% floating-point operations per year than the 40% finite incremental increases in the computational costs. Also, we continue to see significant increases in both LLM queries (350% per year) and mainstream multi-modal AI advances (135% per year) in recent years. At the same time, however, global energy efficiency improves at a relatively slower pace of 35% per year. In essence, we would expect to see such wide divergence between the massive average computational cost declines and the respective exponential global growth rates for high-performance computation, widespread user adoption, and human labor productivity. Hence, this baseline cost-benefit analysis shines new positive light on the current race toward AGI.

 

Many macro-financial economists flag the extraordinary AI infrastructure buildout as a strategic bet of several trillion dollars on the distant future payoffs over the next couple of decades. On a standalone basis, Nvidia CEO Jensen Huang recently highlighted that the current AI infrastructure capital investments could total $3 trillion to $4 trillion by 2030. Also, some stock market analysts recently further projected sizable capital investments in cloud data centers alone to reach $2 trillion in the next few years. Although the recent AI-driven capital investments seem significantly larger than the past peaks in the Internet dotcom era of 1999-2000 and Global Financial Crisis (GFC) of 2007-2008, we believe the recent massive AI-driven capital investments look more benign when we scale them relative to U.S. total GDP per annum. Specifically, these recent AI-driven capital investments remain well below 1% of U.S. total GDP, whereas, the Internet dotcom and GFC benchmarks rise toward the more extreme range of 2.5% to even 5% of U.S. total GDP per annum. When we put these relative figures into perspective, the recent AI-driven capital investments are large in absolute terms but not overblown in relative terms by historical standards.

 

More importantly, we believe the next-gen AI-driven economic gains can ultimately justify the current multi-trillion-dollar global capital investment cycle. With some reasonable ranges of economic projections, we can approximate the present value of all future cash flows as a result of the current AI infrastructure buildout worldwide. First, we assume a baseline 15% gross increase in U.S. labor productivity after the next-gen complete user adoption of AI-driven technological advancement over the next couple of decades. This gross increase in U.S. labor productivity leads to at least $4.5 trillion economic value in present-value terms. Second, we expect global user adoption to take place over the next couple of decades with a 3-year time lag between the widespread user adoption and the subsequent realization of U.S. labor productivity gains. Third, we expect to see a 45% capital share of incremental economic value due to AI-driven U.S. labor productivity gains. This capital share projection accords with the average baseline benchmark for the American real economy. Fourth, we apply a 15% discount rate for present value calculations. This discount rate corresponds to the top end of the interquartile range of WACC figures for the AI-driven Magnificent 7 tech titans, cloud hyperscalers, and microchip manufacturers. Under all of these assumptions, we gauge whether the current global race toward AGI proves to be sustainable in the long run. In the best likelihood of success, we would expect the American AI tech titans to extract at least $8 trillion to even $12 trillion of the $20 trillion total economic value of AGI over the next couple of decades. From this new normal perspective, AI ecosystem circularity is less as artificial inflation and substantially more as a key reflection of strategic interdependence between these core AI partners across both best-in-class hardware and software requirements. For these reasons, the current AI-driven stock market rally is not fundamentally a hope-and-hype asset bubble like the past Internet dotcom era.

 

We need to raise several cautionary caveats in relation to our current cost-benefit analysis of whether the current AI infrastructure buildout makes sense in terms of both fundamental factors and financial metrics. From new antitrust regulation and stock market concentration to intense competition and vertical integration, many historical precedents help highlight several fundamental factors for us to assess whether the new AI tech leaders can win the global race toward AGI as a result of the recent AI infrastructure buildout worldwide. First, many macro-financial economists take notice of the fact that significant stock market value tends to concentrate in several AI-driven tech titans. For instance, specifically, the Magnificent 7 tech titans, cloud hyperscalers, and microchip manufacturers account for almost 45% of S&P 500 stock market capitalization in recent years. Also, the top 10 American tech titans account for almost 25% to 30% of S&P 500 stock market capitalization in recent years. Today, this high stock market concentration may call for some new antitrust scrutiny with proper penalties, rules, and regulations in response to any anti-competitive business practices in modern AI-driven technological advances.

 

Second, we continue to see intense competition among the major AI-driven tech titans at almost all of the 3 layers of the current AI infrastructure buildout. Although we expect Microsoft-OpenAI ChatGPT and Copilot, Google Gemini, and Anthropic Claude to dominate the top application layer with their built-in state-of-the-art foundational models, we believe it is a bit unclear whether there would ultimately be dominant winners at the middle platform layer and the bottom infrastructure layer. In the meantime, Nvidia seems to dominate some part of the bottom infrastructure layer with the next-gen Blackwell GPU advances; however, we would witness more intense competition from Nvidia’s rivals such as AMD, Broadcom, Qualcomm, Micron, and Intel etc. Even some of the Magnificent 7 tech titans, specifically, Apple, Meta, Google, Amazon, and Microsoft seek to design their own custom application-specific integrative circuits (ASIC) over the next couple of decades. This recent market development arises from the cost concerns in relation to Nvidia’s current dominance in the global market for the next-gen Blackwell GPU advances, cloud data centers, and smart software solutions for the respective high-performance hardware devices. Meanwhile, OpenAI, Microsoft, Meta, Apple, Google, Amazon, Oracle, and Cisco further seek to enter new long-term strategic partnerships for multi-gigawatt high-performance computation and efficient energy consumption in the next 5 to 10 years. In time, these rare unique resources span massive cloud data centers worldwide, nuclear and hydrogen power plants, and even alternative energy sources such as solar panels and wind turbines. After all, more intense competition drives the AI tech leaders to use more cost-effective, efficient, and powerful solutions for high-performance computation and energy consumption. Today, these AI tech leaders continue to diversify across a broader suite of foundational models to maintain their first-mover advantages, economic moats, and other competitive advantages in the global capital system.

 

Third, the major AI tech leaders continue to seek some sort of vertical integration to further secure their first-mover advantages, economic moats, network effects, and other competitive advantages in the current race toward AGI. Also, some of these major AI tech leaders attempt to establish new product market standards such as model context protocols (MCP), application programming interfaces (API), and software development kits (SDK). In the meantime, we would witness the bizarre circularity of massive capital investments in AI infrastructure among the key market players such as OpenAI, Nvidia, AMD, Broadcom, Qualcomm, Oracle, Cisco, and so forth. In effect, this bizarre circularity blurs the boundaries between clients, suppliers, and cloud service providers within the broader AI ecosystem. Several recent mergers and acquisitions (M&A), R&D outlays, and other capital market deals further exhibit such similar circularity. All of these Herculean efforts can probably help accelerate next-gen widespread, pervasive, and ubiquitous user adoption at the global scale. At this stage, however, it is not so clear whether all of these major AI tech titans can pass muster to be the ultimate winners in the current race toward AGI. In recent years, we continue to see substantial stock market concentration in the Magnificent 7 tech titans, online platforms, cloud hyperscalers, and microchip manufacturers.

 

Despite some recent incremental improvements with fewer hallucinations, most large language models (LLM) remain autocomplete on steroids, still rely on the next-token probabilistic predictions rather than true human-like perceptions, and therefore continue to be far from artificial general intelligence (AGI).

In the current race toward artificial general intelligence (AGI), we expect the major AI-driven technological advances to be capable of both task automation and human productivity with good knowledge and subject matter expertise in several strategic sectors. These strategic sectors span law, finance, medicine, healthcare, trade, taxation, technology, science, and even education. The major AI-driven foundational models can help with coding new computer programs; addressing user prompts, queries, and questions with encyclopedic long-form answers; and even brainstorming new research ideas and hypothesis tests for some subject matter experts. Despite some recent incremental improvements with substantially fewer hallucinations, most large language models (LLM) remain autocomplete on steroids, still rely on the next-token probabilistic predictions rather than true human-like perceptions, and therefore continue to be far from artificial general intelligence (AGI). Today, the current mainstream AI-driven foundational models can hardly grasp abstract concepts, human perceptions, linguistic nuances, and interpersonal emotions. Also, many mainstream AI-driven foundational models still struggle with reliably carrying out basic human tasks, although Google Gemini now integrates the built-in best-in-class Google Search grounding tool with real-time Retrieval-Augmented Generation (RAG) to fact-check online search queries, reviews, and long-form answers with substantially fewer hallucinations. In recent years, we continue to view life as a box of chocolates from Forrest Gump; specifically, life is like an LLM: you never know what you are gonna get.

 

On the technical level, most mainstream large language models (LLM) continue to heavily rely on the black box of next-token probabilistic predictions with rather opaque inside statistical mechanisms. Within the black box, these next-token probabilistic predictions can help autocomplete each of the grammatically correct sentences as part of the long-form real-time answers to user search queries. When we fundamentally put these answers into perspective, the next-token probabilistic predictions often rhyme with user search queries without true human-like perceptions. Specifically, most mainstream foundational models lack human capabilities to truly understand abstract concepts, linguistic nuances, and even interpersonal emotions. Unlike humans, these models fail to feel specific verbal cues, sensations, and other human sentiments. In recent years, several AI-driven tech titans use, apply, and leverage reinforcement learning algorithms and deep neural networks to reduce model mistakes, errors, and hallucinations. However, these quick fixes are often temporary and can create new problems in relation to the initial user search queries, questions, and prompts. For all these reasons, most mainstream foundational models often run into many limitations in some specific fields such as law, finance, medicine, healthcare, trade, taxation, technology, science, and even education.

 

In her book, The Worlds I See, Stanford computer vision professor Dr Fei-Fei Li suggests that humans are often able to infer the gist of the scene at first glance in a photo, an image, or a fragment of a video clip. For instance, this key gist can often help highlight some particular human activity in the broader social context: a Christmas party at home, a lecture on calculus at school, or a corporate conference call at work. In his book, AI 2041, the former AI veteran at Google and Microsoft, Dr Kai-Fu Li suggests that the mainstream AI-driven foundational models should be able to exhibit such similar human-like perceptions in many different forms such as computer vision, voice, audio, video, animation, and even large-scale Monte Carlo simulation etc. These multi-modal features, functions, and benefits further empower new AI-driven advances to harness better inferences and genuine human-like perceptions in all aspects of the human life experiences. In his book, The Algebraic Mind, NYU Professor Gary Marcus proposes new neuro-symbolic AI as an alternative solution to the current status quo of the major mainstream Gen AI-driven foundational models. Specifically, neuro-symbolic AI combines the 2 major traditions as part of the current race toward AGI. First, deep neural networks can often serve as fast, almost automatic, and statistical systems of genuine human-like perceptions. Second, symbolic advances apply slowly some specific human reasons, first principles, logical rules, complex flows, and rigorous mathematical formulae, equations, and derivations to solve real-world problems. In combination, these first and second traditions correspond to Nobel Laureate Daniel Kahneman’s respective System 1 and System 2 for human judgment under uncertainty. As a result, neuro-symbolic AI integrates both systems to arrive at substantially more robust, interpretable, and flexible answers, choices, and decisions in accordance with true human perceptions. In practice, the recent global AI-driven technological advances, disruptive innovations, and many other technical developments show a quiet structural shift toward neuro-symbolic AI. Almost all of the major AI tech leaders have begun to embed some specific symbolic tools within the foundational online search systems, large language models (LLM), and automatic content generators for real-time Retrieval-Augmented Generation (RAG). From OpenAI and Google to Meta, Apple, and Amazon, the mainstream foundational models now seek to embed encyclopedic answers, code interpreters, and real-time online news snippets to ensure better, smarter, more accurate, and more granular human-like perceptions, predictions, trade-offs, choices, and decisions. At Google DeepMind, specifically, AlphaFold runs hundreds of billions of simulations to accurately predict protein structures, amino-acid chains, and many other biological shapes, molecules, and organisms. In effect, AlphaFold helps highlight neuro-symbolic AI-driven advances in effective disease detection, treatment, and prevention worldwide in due course. From ancient times to the modern day, humans often make mental models to better represent their external macro environments, social interactions, virtual reality headsets, metaverses, and even fictional worlds like Harry Potter and The Lord of the Rings. With the recent neuro-symbolic AI advances, subject matter experts can seek to establish some sort of mutual causation between external variables and some specific neurons in the deepest layer of the dense neural network. This mutual causation helps us better decipher any potential cause-effect nexus between our deepest neurons and external variables. In turn, this causal nexus can empower us to better explain why some specific dense neural networks outperform most other mainstream machine-learning algorithms. In this rare unique fashion, all of these new explainable AI advances continue to push the boundaries for deep neural networks to more fundamentally exhibit genuine human-like perceptions rather than only next-token probabilistic predictions.

 

In macro-finance and asset return prediction, our AYA proprietary alpha stock market investment model applies the same baseline logic to help establish mutual causation between fundamental factors and macroeconomic innovations. In combination, our AYA stock synopses, podcasts, ebooks, reports, reviews, and fintech research articles combine to shine new light on the comprehensive fundamental analysis of each of the AI-driven Magnificent 7 and other tech titans. In this new research direction, we serve as active storytellers but not only passive observers. In essence, we open the black box with better economic stories, refresh our fundamentally broad suite of deep machine-learning algorithms, and so provide novel, non-obvious, and useful alternative AI-driven glass-box and white-box smart software solutions to macro-finance and asset return prediction in due course. Over many years, we create, curate, apply, adapt, and leverage many compelling economic stories for smart stock market investment decisions. In time, all these smart stock market investment decisions often tend to arise from the econometrically robust, recurrent, and pervasive causal nexus between fundamental factors and macroeconomic innovations in global human history.

 

With U.S. fintech patent approval, accreditation, and protection for 20 years, our AYA fintech network platform provides proprietary alpha stock signals and personal finance tools for stock market investors worldwide.

 

We build, design, and delve into our new and non-obvious proprietary algorithmic system for smart asset return prediction and fintech network platform automation. Unlike our fintech rivals and competitors who chose to keep their proprietary algorithms in a black box, we open the black box by providing the free and complete disclosure of our U.S. fintech patent publication. In this rare unique fashion, we help stock market investors ferret out informative alpha stock signals in order to enrich their own stock market investment portfolios. With no need to crunch data over an extensive period of time, our freemium members pick and choose their own alpha stock signals for profitable investment opportunities in the U.S. stock market.

 

Smart investors can consult our proprietary alpha stock signals to ferret out rare opportunities for transient stock market undervaluation. Our analytic reports help many stock market investors better understand global macro trends in trade, finance, technology, and so forth. Most investors can combine our proprietary alpha stock signals with broader and deeper macro financial knowledge to win in the stock market.

 

Through our proprietary alpha stock signals and personal finance tools, we can help stock market investors achieve their near-term and longer-term financial goals. High-quality stock market investment decisions can help investors attain the near-term goals of buying a smartphone, a car, a house, good health care, and many more. Also, these high-quality stock market investment decisions can further help investors attain the longer-term goals of saving for travel, passive income, retirement, self-employment, and college education for children. Our AYA fintech network platform empowers stock market investors through better social integration, education, and technology.

 

Andy Yeh

Online brief biography of Andy Yeh

https://ayafintech.network/blog/a-brief-biography-of-andy-yeh/

Co-Chair

AYA fintech network platform

Brass Ring International Density Enterprise ©

 

 

President Trump refreshes American fiscal fears and sovereign debt concerns through the One Big Beautiful Bill Act.

Podcast: https://bit.ly/4eSEU1w

Article: https://ayafintech.network/blog/president-trump-refreshes-american-fiscal-fears-and-sovereign-debt-concerns-through-the-one-big-beautiful-bill-act/

 

President Trump poses new threats to Fed Chair monetary policy independence again.

Podcast: https://bit.ly/4ebeoQH

Article: https://ayafintech.network/blog/president-trump-poses-new-threats-to-fed-chair-monetary-policy-independence-again/

 

What are the mainstream legal origins of President Trump’s tariff policies?

Podcast: https://bit.ly/3ZnNMG7

Article: https://ayafintech.network/blog/mainstream-legal-origins-of-recent-trump-tariffs/

 

American exceptionalism often turns out to be the heuristic rule of thumb for better economic growth, low and stable inflation, full employment, and macro-financial stability.

Podcast: https://bit.ly/4iuWuJ9

Article: https://ayafintech.network/blog/american-exceptionalism-turns-out-to-be-the-heuristic-rule-of-thumb-for-better-economic-growth-low-stable-inflation-full-employment-macro-financial-stability/

 

In the broader modern monetary policy context, central banks learn to weigh the trade-offs between output and inflation expectations and macro-financial stress conditions.

Podcast: https://bit.ly/42SwrXG

Article: https://ayafintech.network/blog/central-banks-weigh-the-monetary-policy-trade-offs-between-output-inflation-and-macro-financial-stress-conditions/

 

Is higher stock market concentration good or bad for Corporate America?

Podcast: https://bit.ly/3F1fpgN

Article: https://ayafintech.network/blog/is-higher-stock-market-concentration-good-or-bad-for-corporate-america/

 

Geopolitical alignment often reshapes and reinforces asset market fragmentation in the broader context of financial deglobalization.

Podcast: https://bit.ly/3ZpGMcD

Article: https://ayafintech.network/blog/geopolitical-alignment-often-reshapes-and-reinforces-asset-market-fragmentation-in-the-broader-context-of-financial-deglobalization/

 

The global cloud infrastructure helps accelerate the next high-tech revolutions in electric vehicles (EV), virtual reality (VR) headsets, artificial intelligence (AI) online services, and the metaverse.

Podcast: https://bit.ly/47pDk3z

Article: https://ayafintech.network/blog/the-global-cloud-infrastructure-helps-expand-what-can-be-made-digitally-viable-from-electric-vehicles-and-virtual-reality-headsets-to-artificial-intelligence-metaverse/

 

How can generative AI tools and LLMs help enhance human productivity?

Podcast: https://bit.ly/4elAFKv

Article: https://ayafintech.network/blog/generative-artificial-intelligence-uses-large-language-models-and-content-generation-tools-to-enhance-human-productivity/

 

Both BYD and Tesla have become serious global manufacturers of electric vehicles (EV) worldwide.

Podcast: https://bit.ly/3BgL0sL

Article: https://ayafintech.network/blog/mainstream-technological-advances-in-the-global-auto-industry/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Meta Platforms (U.S. stock symbol: $META).

Podcast: https://bit.ly/3Vt1Sng

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-meta-platforms-meta/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Alphabet Google (U.S. stock symbol: $GOOG).

Podcast: https://bit.ly/46yuX5T

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-alphabet-google-goog/  

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Nvidia (U.S. stock symbol: $NVDA).

Podcast: https://bit.ly/3Kh8Qta

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-nvidia-nvda/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Tesla (U.S. stock symbol: $TSLA).

Podcast: https://bit.ly/4nRGLqy

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-tesla-tsla/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Apple (U.S. stock symbol: $AAPL).

Podcast: https://bit.ly/4ndXt3K

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-apple-aapl/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Amazon (U.S. stock symbol: $AMZN).

Podcast: https://bit.ly/46fUWQE

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-amazon-amzn/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Microsoft (U.S. stock symbol: $MSFT).

Podcast: https://bit.ly/46biKoG

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-microsoft-msft/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of IonQ (U.S. stock symbol: $IONQ).

Podcast: https://bit.ly/3IXfnss

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-ionq-ionq/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Oracle (U.S. stock symbol: $ORCL).

Podcast: https://bit.ly/47fF94u

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-oracle-orcl/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Netflix (U.S. stock symbol: $NFLX).

Podcast: https://bit.ly/4q7cTss

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-netflix-nflx/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Palantir (U.S. stock symbol: $PLTR).

Podcast: https://bit.ly/4gZTiWO

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-palantir-pltr/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of AT&T (U.S. stock symbol: $T).

Podcast: https://bit.ly/4q2VfG4

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-att-t/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of T-Mobile (U.S. stock symbol: $TMUS).

Podcast: https://bit.ly/4mV2ays

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-t-mobile-tmus/ 

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Cisco Systems (U.S. stock symbol: $CSCO).

Podcast: https://bit.ly/48gGjxM

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-cisco-systems-csco/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of AMD (U.S. stock symbol: $AMD).

Podcast: https://bit.ly/470BoPm

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-amd-amd/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Salesforce (U.S. stock symbol: $CRM).

Podcast: https://bit.ly/46LpXvZ

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-salesforce-crm/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Uber Technologies (U.S. stock symbol: $UBER).

Podcast: https://bit.ly/4nOTVFm

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-uber-technologies-uber/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of IBM (International Business Machines) (U.S. stock symbol: $IBM).

Podcast: https://bit.ly/4ohozqT

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-ibm-ibm/

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Intuit (U.S. stock symbol: $INTU).

Podcast: https://bit.ly/4ohAKUE

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-intuit-intu/ 

 

Stock Synopsis: With a new Python program, we use, adapt, apply, and leverage each of the mainstream Gemini Gen AI models to conduct this comprehensive fundamental analysis of Texas Instruments (U.S. stock symbol: $TXN).

Podcast: https://bit.ly/4nVq0Ly

Article: https://ayafintech.network/blog/gen-ai-fundamental-analysis-of-texas-instruments-txn/

 

Industry Analysis

AYA ebook hyperlink: https://bit.ly/4hxvrwy

AYA ebook length: 283 pages (21 chapters and 122,241 words).

 

Stock Synopses for the Top 20 Tech Titans

AYA ebook hyperlink: https://bit.ly/3VR7Ka5

AYA ebook length: 449 pages (20 chapters and 168,639 words).

 

Top-Tier Self-Improvement Book Reviews

AYA ebook hyperlink: https://bit.ly/46Iqkrc

AYA ebook length: 133 pages (10 chapters and 54,529 words).

 

Bidenomics

AYA ebook hyperlink: https://bit.ly/44CdDu7

AYA ebook length: 206 pages (18 chapters and 90,405 words)

 

Trump Economic Reforms

AYA ebook hyperlink: https://bit.ly/2ZwYfiE

AYA ebook length: 507 pages (21 chapters and 97,854 words)

 

Modern management macro themes, insights, and worldviews

AYA ebook hyperlink: https://bit.ly/2IezdQh

AYA ebook length: 225 pages (top 40 recent management book reviews)

 

Economic science macro themes, insights, and worldviews

AYA ebook hyperlink: https://bit.ly/3FaegyI

AYA ebook length: 220 pages (top 40 recent economic science book reviews).