AI Is Transforming Global Business Models
Artificial intelligence has become a deeply integrated part of modern life, advancing at an unprecedented pace. In a world traditionally dominated by global corporations, AI has emerged as not just a revolutionary technology but as a primary disruptor.
AI is challenging not only established business models and market structures but entire industries. The question isn't whether AI is disruptive—it’s how profound these changes will be for the world.
A New Technological Era
In October 2024, the report "Is AI Truly Disruptive?" was released, based on research conducted by ARK Invest’s Chief Futurist, Brett Winton.
Through these innovations, resource-limited small companies can now not only challenge established industry giants but even overtake them.
These technologies share three key characteristics:
Together, these characteristics set AI apart as a transformative force in the global economy.
AI’s Cost Reduction: Making Revolutionary Accessibility Possible
One of AI’s most striking features today is its rapid reduction in operational costs.
According to an exponential trend mapped out by Winton, training an AI model like GPT-4 in 2020 would have cost an estimated $6 billion, with a dedicated machine for generating outputs priced around $40 million. Technologically feasible but economically impractical, such costs made this step unattainable at the time.
Generating high-quality GPT-4 outputs became feasible only in 2023, when training costs dropped to a still substantial but much lower $100 million. By August 2024, however, the cost to train an advanced large language model, GPT-4o, had plummeted to under $10 million!
Moore’s Law, an empirical observation by Intel co-founder Gordon Moore in 1965, predicted that the number of transistors on an integrated circuit would double every 12 months while the cost remained the same. Later, Moore revised his prediction, extending the doubling timeline to 18–24 months, essentially meaning that development costs would halve roughly every 1–2 years.
To illustrate Moore’s Law, there is often a humorous analogy: if the aviation industry had progressed at the same exponential rate, a Boeing 767 would now cost just $500, circle the globe in 20 minutes, and use only 19 liters of fuel.
While Brett Winton doesn’t make such daring analogies, his research suggests that AI is now advancing at rates 4–6 times faster than Moore’s pace. Winton forecasts that if the current trend continues, by 2026 it could be possible to generate GPT-4-level outputs even on a smartphone.
This rapid reduction in costs has profound implications for both startups and established tech companies. Now, small businesses—and even individual developers—can afford to experiment with and implement AI innovations. This is a critical factor allowing them to compete with large corporations.
The transformative power of AI is spreading far beyond the tech industry, finding applications in healthcare, finance, manufacturing, and more.
In fact, as Brett Winton notes in his research, AI is now being used across all sectors of the global economy to varying extents. This innovation platform is not only driving new businesses but also shaping entirely new industries.
The growing cross-industry impact of AI can be tracked in quarterly and annual reports from a wide range of sectors. More and more companies in energy, healthcare, manufacturing, and consumer goods are integrating AI into their development strategies.
Does AI present risks as it enters sectors previously untouched by high-tech innovation?
Yes, but primarily for those that have either under-invested in or entirely ignored new trends.
On the other hand, cross-sector AI expansion brings vast opportunities outside traditional tech, creating a powerful competitive advantage.
Winton illustrates this point with the examples of finance and healthcare, two industries he sees as long challenged by inefficiencies.
Finance and healthcare traditionally work with vast amounts of data, and relying on conventional tools often leads to large stores of unused or underutilized information. Human specialists simply can’t process everything, meaning that much of the data is left untouched.
AI-based tools, however, can analyze the entire data spectrum. In healthcare, this could revolutionize diagnostics, treatment planning, and patient care.
In finance, AI systems can optimize operations, improve fraud detection, and provide personalized financial advice.
In short, AI has the power to turn immense amounts of data into actionable insights.
The Mega-Tech Dilemma: Can Tech Giants Keep Pace with Progress?
Brett Winton’s research highlights the existential dilemma now confronting tech giants like Google and Apple. For decades, these companies built their strategies around stable, proven business models and technologies. But with the rapid rise of AI, they now face difficult decisions.
Can they integrate AI into their ecosystems? And, perhaps more critically, should they?
For these giants, these questions aren't merely theoretical. Multibillion-dollar revenue streams rely on current business models, and the risks of financial and reputational loss are clear.
Historically, traditional tech companies have depended on a “fast follower” strategy.A fast follower waits for someone else to innovate and, if the launch succeeds, uses its substantial resources to quickly release a similar product, seizing an advantage with fewer competitors.
By letting startups identify and optimize new technologies, major tech companies “skimmed the cream,” using their financial power and technical infrastructure to adopt and scale up innovations.
But what worked in the past may no longer be enough.
The dramatic drop in AI costs and the rapid growth in performance have made the "fast follower" strategy increasingly challenging, if not outright unviable.
For example, Google has fallen behind OpenAI in deploying a large language model (LLM). Despite continued efforts, Google’s AI models have consistently been more expensive and less efficient.
Meta’s Llama 3 model, an open-source alternative, has also struggled to keep up, while Apple is notably delayed in launching its own LLM. Apple’s first AI platform release came in October 2024, with full access only expected by spring 2025.
However, as Winton points out, the slow adoption of AI among tech giants isn’t simply a question of technical expertise (which these giants have in abundance).
Companies like Google and Apple could actually risk losing more if they rush into AI implementation.
The reality is that AI systems are still unpredictable, prone to “hallucinations,” and often produce inaccurate results. Although each new iteration reduces these flaws, they haven’t been fully overcome.
Such gaps in AI performance could significantly damage the reputation of established brands. For companies with billions of dollars on the line, the risks of deploying untested AI models are exceptionally high.
Yet, these companies seem forced to choose between two “bad” options.
By holding back on AI implementation, tech giants might risk even more. Startups and emerging competitors, facing far fewer risks, are moving ahead by integrating innovations and developing AI platforms.
From The Art of War, we know that one of the most formidable opponents is the one with nothing to lose.
AI as a Platform for Innovation: A New Breakthrough
The most crucial aspect of AI’s transformative potential lies in its role as a foundation for further innovation.
Artificial intelligence has become a key catalyst for technological convergence, driving advancements across diverse industries—from autonomous vehicles to cutting-edge energy storage.
These fields are already attracting massive investments. In 2024, a third of global venture funding (more than $90 billion) was directed toward AI-focused companies.
In the U.S. alone, AI development accounted for 40% of all venture capital investments.
Investor confidence is only growing in AI as the next major frontier for technological breakthroughs.
This surge of AI-driven innovation poses a direct threat to established tech giants. Companies that do not embrace AI as a foundational platform for future technologies risk falling behind. Even if giants like Google and Apple succeed in integrating AI into their ecosystems, the pace of AI advancement could soon escape their control, rendering traditional business models obsolete.
A New Technological Era
In October 2024, the report "Is AI Truly Disruptive?" was released, based on research conducted by ARK Invest’s Chief Futurist, Brett Winton.
Winton’s study aims to explore and analyze the nature of artificial intelligence and its revolutionary impact on the established order within the tech industry. The report specifically examines the unprecedented acceleration of AI development and its potential consequences for major players like Google and Apple.
Brett Winton has been with ARK since its inception, overseeing ARK’s long-term forecasts and evaluating the potential impact of various innovations on the stock market, crypto assets, and the global economy.
Prior to joining ARK, Winton served as Vice President and Senior Analyst in the Strategic Change Research Group at AllianceBernstein, where his research focused on global energy under carbon emission regulations, social media, financial services, and more.
The insights of an expert like Winton deserve attention, no matter how bold they may be.
Winton’s assessment is clear: AI is a disruptive technology, evolving faster than any other in human history!
ARK Invest’s Investment Management. Source: Wealth Management
ARK Investment Management LLC, or simply ARK Invest, founded by the renowned Cathie Wood in 2014, focuses on investing in advanced technologies: artificial intelligence, DNA sequencing, CRISPR gene editing, robotics, electric vehicles, energy storage, fintech, 3D printing, blockchain technology, and cryptocurrencies.The firm manages nine exchange-traded funds and the venture-oriented ARK Venture Fund.
Brett Winton has been with ARK since its inception, overseeing ARK’s long-term forecasts and evaluating the potential impact of various innovations on the stock market, crypto assets, and the global economy.
Prior to joining ARK, Winton served as Vice President and Senior Analyst in the Strategic Change Research Group at AllianceBernstein, where his research focused on global energy under carbon emission regulations, social media, financial services, and more.
The insights of an expert like Winton deserve attention, no matter how bold they may be.
Winton’s assessment is clear: AI is a disruptive technology, evolving faster than any other in human history!
Brett Winton. Source: Bloomberg
Winton explains that one of the most intriguing aspects of technological breakthroughs like AI is their ability to level the playing field in markets (similar to the famous phrase about the Colt revolver: “God created men, but Colonel Colt made them equal”).
Through these innovations, resource-limited small companies can now not only challenge established industry giants but even overtake them.
These technologies share three key characteristics:
- They drastically reduce previous costs.
- They offer cross-sector applicability.
- They drive the creation of innovative platforms.
Together, these characteristics set AI apart as a transformative force in the global economy.
AI’s Cost Reduction: Making Revolutionary Accessibility Possible
One of AI’s most striking features today is its rapid reduction in operational costs.
According to an exponential trend mapped out by Winton, training an AI model like GPT-4 in 2020 would have cost an estimated $6 billion, with a dedicated machine for generating outputs priced around $40 million. Technologically feasible but economically impractical, such costs made this step unattainable at the time.
Generating high-quality GPT-4 outputs became feasible only in 2023, when training costs dropped to a still substantial but much lower $100 million. By August 2024, however, the cost to train an advanced large language model, GPT-4o, had plummeted to under $10 million!
Exponential Decline in AI Model Training and Deployment Costs Source: ARK Invest
The futurist estimates that the cost of training and deploying AI models is, on average, halving every four months—a rate that significantly outpaces the famous Moore’s Law, which has long served as a benchmark for semiconductor computing power growth.
Moore’s Law, an empirical observation by Intel co-founder Gordon Moore in 1965, predicted that the number of transistors on an integrated circuit would double every 12 months while the cost remained the same. Later, Moore revised his prediction, extending the doubling timeline to 18–24 months, essentially meaning that development costs would halve roughly every 1–2 years.
To illustrate Moore’s Law, there is often a humorous analogy: if the aviation industry had progressed at the same exponential rate, a Boeing 767 would now cost just $500, circle the globe in 20 minutes, and use only 19 liters of fuel.
While Brett Winton doesn’t make such daring analogies, his research suggests that AI is now advancing at rates 4–6 times faster than Moore’s pace. Winton forecasts that if the current trend continues, by 2026 it could be possible to generate GPT-4-level outputs even on a smartphone.
This rapid reduction in costs has profound implications for both startups and established tech companies. Now, small businesses—and even individual developers—can afford to experiment with and implement AI innovations. This is a critical factor allowing them to compete with large corporations.
Projected AI Training Cost Reduction Through 2030: -75% Annually. Source: ARK Invest
Cross-Sector Breakthrough: Expanding AI’s Reach
The transformative power of AI is spreading far beyond the tech industry, finding applications in healthcare, finance, manufacturing, and more.
In fact, as Brett Winton notes in his research, AI is now being used across all sectors of the global economy to varying extents. This innovation platform is not only driving new businesses but also shaping entirely new industries.
The growing cross-industry impact of AI can be tracked in quarterly and annual reports from a wide range of sectors. More and more companies in energy, healthcare, manufacturing, and consumer goods are integrating AI into their development strategies.
Does AI present risks as it enters sectors previously untouched by high-tech innovation?
Yes, but primarily for those that have either under-invested in or entirely ignored new trends.
On the other hand, cross-sector AI expansion brings vast opportunities outside traditional tech, creating a powerful competitive advantage.
Winton illustrates this point with the examples of finance and healthcare, two industries he sees as long challenged by inefficiencies.
Finance and healthcare traditionally work with vast amounts of data, and relying on conventional tools often leads to large stores of unused or underutilized information. Human specialists simply can’t process everything, meaning that much of the data is left untouched.
AI-based tools, however, can analyze the entire data spectrum. In healthcare, this could revolutionize diagnostics, treatment planning, and patient care.
In finance, AI systems can optimize operations, improve fraud detection, and provide personalized financial advice.
In short, AI has the power to turn immense amounts of data into actionable insights.
The Mega-Tech Dilemma: Can Tech Giants Keep Pace with Progress?
Brett Winton’s research highlights the existential dilemma now confronting tech giants like Google and Apple. For decades, these companies built their strategies around stable, proven business models and technologies. But with the rapid rise of AI, they now face difficult decisions.
Can they integrate AI into their ecosystems? And, perhaps more critically, should they?
For these giants, these questions aren't merely theoretical. Multibillion-dollar revenue streams rely on current business models, and the risks of financial and reputational loss are clear.
Historically, traditional tech companies have depended on a “fast follower” strategy.A fast follower waits for someone else to innovate and, if the launch succeeds, uses its substantial resources to quickly release a similar product, seizing an advantage with fewer competitors.
By letting startups identify and optimize new technologies, major tech companies “skimmed the cream,” using their financial power and technical infrastructure to adopt and scale up innovations.
But what worked in the past may no longer be enough.
The dramatic drop in AI costs and the rapid growth in performance have made the "fast follower" strategy increasingly challenging, if not outright unviable.
For example, Google has fallen behind OpenAI in deploying a large language model (LLM). Despite continued efforts, Google’s AI models have consistently been more expensive and less efficient.
Meta’s Llama 3 model, an open-source alternative, has also struggled to keep up, while Apple is notably delayed in launching its own LLM. Apple’s first AI platform release came in October 2024, with full access only expected by spring 2025.
What does that steep cost decline mean for incumbent tech companies? Even small timeto-market delays are likely to cause severe performance gaps, as the speed of cost declines renders the fast-follower strategy less effective,the report explains.
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However, as Winton points out, the slow adoption of AI among tech giants isn’t simply a question of technical expertise (which these giants have in abundance).
Companies like Google and Apple could actually risk losing more if they rush into AI implementation.
The reality is that AI systems are still unpredictable, prone to “hallucinations,” and often produce inaccurate results. Although each new iteration reduces these flaws, they haven’t been fully overcome.
Such gaps in AI performance could significantly damage the reputation of established brands. For companies with billions of dollars on the line, the risks of deploying untested AI models are exceptionally high.
Yet, these companies seem forced to choose between two “bad” options.
By holding back on AI implementation, tech giants might risk even more. Startups and emerging competitors, facing far fewer risks, are moving ahead by integrating innovations and developing AI platforms.
From The Art of War, we know that one of the most formidable opponents is the one with nothing to lose.
AI as a Platform for Innovation: A New Breakthrough
The most crucial aspect of AI’s transformative potential lies in its role as a foundation for further innovation.
Artificial intelligence has become a key catalyst for technological convergence, driving advancements across diverse industries—from autonomous vehicles to cutting-edge energy storage.
These fields are already attracting massive investments. In 2024, a third of global venture funding (more than $90 billion) was directed toward AI-focused companies.
In the U.S. alone, AI development accounted for 40% of all venture capital investments.
Investor confidence is only growing in AI as the next major frontier for technological breakthroughs.
Venture Investment in AI. Source: ARK Invest
Startups like OpenAI, Perplexity, and Humane are not only challenging established companies—they are redefining entire industries. Perplexity and OpenAI are aiming to unseat Google’s dominance in search, while Humane and Rabbit are positioning themselves to disrupt the Apple and Android monopolies in mobile operating systems.
This surge of AI-driven innovation poses a direct threat to established tech giants. Companies that do not embrace AI as a foundational platform for future technologies risk falling behind. Even if giants like Google and Apple succeed in integrating AI into their ecosystems, the pace of AI advancement could soon escape their control, rendering traditional business models obsolete.
