Artificial Intelligence (AI) is transforming the way we live and work. From using ChatGPT to create holiday itineraries to travelling in driverless cars and receiving AI-assisted healthcare diagnoses, there’s no denying AI is revolutionizing many aspects of society. 

While fears of widespread job displacement, the spread of misinformation, and the potential loss of human connection exist around the technology, many are embracing the potential of AI, eager to utilize its use cases for the better.

For those who want to get ahead of the curve, studying a Master’s in AI could be a great fit. So, where can you study for a master’s degree in AI? And which leading business schools offer this highly specialized program?

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What is a Master’s in AI? 

Before we pinpoint exactly where you can study this innovative program, it’s important to establish exactly what defines a Master’s in AI.

Master’s programs in AI are, at their core, designed to develop future leaders who can efficiently navigate AI-powered business environments, equipping them with the skills and knowledge to succeed in an ever-evolving technological world of business.

With AI shaping companies and business culture across the globe, a Master’s in AI is particularly relevant to those looking to launch an international career.

A Master’s in AI is often paired with, or encompasses, business analytics—the application of AI and machine learning to analyze business data, identify patterns, and predict future trends that can then inform business decisions. This combination is designed to provide students with a well-rounded, future-ready skill set that will empower them for the world of business.

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Where can I study a Master’s in AI? 

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Apsley Business School

Program: MSc in Artificial Intelligence
Location: London, UK

The MSc in Artificial Intelligence at Apsley Business School, London, a non-profit business school dedicated to promoting sustainable management, is designed for professionals looking to integrate the latest AI trends into their businesses.

With a focus on the quality of assessed work rather than volume, the programme is extremely flexible, allowing participants to balance their studies alongside work and family commitments. Assessed work includes written assignments, usually around 5,000 words for the two core units, as well as a research dissertation of up to 40,000 words.

Students typically complete the program within nine months, although they can take up to 12 months if needed.

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©Apsley Business School – London / Facebook

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Arizona State University, WP Carey School of Business

Program: Master of Science in Artificial Intelligence in Business (MS-AIB)
Location: Arizona, United States

The WP Carey School of Business, part of the prestigious Arizona State University, offers a Master of Science in Artificial Intelligence in Business. Incorporating an applied curriculum alongside career coaching, the course aims to prepare students for success in emerging AI roles across a range of industries.

Typically, the program is completed over two semesters, consisting of 30 total credit hours. However, MS-AIB students have the option to extend their studies by taking a third summer semester. This additional semester allows them to choose from five optional tracks in complementary disciplines: Technology Management and Consulting, Financial Technology, Marketing, Accounting, and Supply Chain Management. It is important to note that students who decide to enrol in the optional third semester will face additional costs.

As a STEM degree, the program offers eligible graduates on student visas the opportunity for Optional Practical Training (OPT) extensions for up to 36 months. This is a particularly appealing benefit for international students seeking to gain work experience in the US or launch their careers there.

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Dublin Business School (DBS)

Program: MSc in Artificial Intelligence
Location: Dublin, Ireland

Formulated to match the increasing demand for AI experts, the MSc in Artificial Intelligence at Dublin Business School includes a comprehensive curriculum that combines theory with practical skills.

Each semester takes a different focus, allowing students to approach AI from multiple angles. The first semester focuses on foundational concepts and skills, including machine learning, while semester two covers more advanced AI topics such as natural language processing. The final semester culminates in a capstone project—either an applied research project or a dissertation.

The program is delivered via both online and face-to-face learning, offering flexibility for students.

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EDHEC Business School

Program: MSc in Data Analytics and Artificial Intelligence
Location: Lille, France

The MSc in Data Analytics and Artificial Intelligence at EDHEC Business School hones the critical thinking skills required to solve real-world data science problems, while also equipping students with core competencies in data science, programming, and AI.

The course is taught in person at the school’s Lille campus, running from September to May. The final term then consists of off-campus practical work experience, such as an internship.

Students are given the option to choose one certificate to complete or take part in a learning expedition. This provides them with the opportunity to broaden their expertise and make the most of their master’s experience.

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EU Business School

Program: Master’s in Artificial Intelligence for Business
Location: Barcelona, Spain

The Master’s in Artificial Intelligence for Business at EU Business School gives students the chance to explore various aspects of AI, from data analysis and product development to corporate responsibility in the age of AI.

Located in Barcelona—home to more than 2,100 tech-based startups and some of the world’s largest multinational companies, such as Airbnb—the course offers wide-ranging access to networking and career opportunities.

Upon completing the program, students will also have the chance to earn an additional qualification from London Metropolitan University, providing an opportunity to maximize the value of their degree.

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ESMT Berlin

Program: Master’s in Analytics and Artificial Intelligence (MAAI)
Location: Berlin, Germany

Recognizing the growing influence of AI, the Master’s in Analytics and Artificial Intelligence at ESMT Berlin aims to build the next generation of leaders, equipping them with in-demand skills.

This two-year program offers plenty of opportunities for practical experience, including a mandatory global immersion trip that allows students to take advantage of internships in Germany and abroad. Students also have the option to complete a semester abroad at one of the school’s top-ranked partner institutions.

In the second year, students undertake an Analytics Consulting Project (ACP), collaborating directly with companies to solve real, data-focused business problems. Alternatively, they can participate in a research project with an ESMT faculty member.

International students are also offered optional German language classes, helping them integrate into life in Berlin.

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Fordham University, Gabelli School of Business

Program: Master’s in Artificial Intelligence in Business
Location: New York, United States

The new STEM-designated Master of Science in Artificial Intelligence in Business at Fordham University’s Gabelli School of Business is built to prepare students for the future world of business.

Alongside a range of innovative electives, the program includes four foundational courses: Artificial Intelligence, Machine Learning for Business, Quantitative Foundations in AI, and Law and Ethics of AI. Students may also choose between two optional tracks: the Finance Industry Track or the Technical Track.

Taught at Fordham’s Lincoln Center Manhattan campus—only minutes from Wall Street—the program offers a dynamic and exciting atmosphere.

As one of Fordham’s newest programs, students also have the unique opportunity to help shape its trajectory and join its very first alumni cohort.

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©Fordham University / Facebook

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HEC Paris

Program: Master of Science Data Science and AI for Business X-HEC
Location: Paris, France

Taught in collaboration with École Polytechnique, a leading institute for science and technology, the Master of Science in Data Science and AI for Business at HEC Paris seeks to foster the next generation of changemakers.

The two-year program combines mathematics, statistics, and data visualization classes, providing students with a comprehensive skillset to carry with them into the future of business.

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Henley Business School

Program: MSc Applied AI for Business
Location: Reading, UK

The MSc in Applied AI for Business at Henley Business School aims to produce graduates who are fully equipped to thrive in all things AI, enabling them to pursue long, prosperous careers in the digital management sector.

The program draws on research from both Henley Business School faculty and the University of Reading’s Department of Computer Science, while also emphasizing real-world applications.

Offer holders can also complete pre-study courses, delivered through a series of short online modules on the FutureLearn platform, developed by Henley faculty.

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NEOMA Business School

Program: MSc Artificial Intelligence for Business
Location: Rouen, France

Addressing the growing talent gap for AI professionals, the MSc in Artificial Intelligence for Business at NEOMA Business School aims to develop individuals who can bridge the gap between AI technology and business applications.

Students with a four-year bachelor’s degree or a three-year bachelor’s degree plus one year of professional experience can complete the course in one year. Those with only a three-year bachelor’s degree will follow a two-year pathway.

The program includes an internship and other practical opportunities, enabling students to gain valuable real-world insights. These professional experiences can be undertaken in France or abroad.

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Nottingham Trent University, Nottingham Business School

Program: MSc Business Analytics and Artificial Intelligence
Location: Nottingham, UK

Nottingham Business School, part of Nottingham Trent University, offers an MSc in Business Analytics and Artificial Intelligence, where students learn from industry leaders, network globally with peers, and receive personalized career support.

Modules include Prescriptive Analytics and AI, Ethics and Governance of AI Systems, AI Management and Leadership, and Data Management and Database Design.

The program is typically taught over one year, but students may extend it to two years to earn a placement diploma in industrial experience, adding a full year of work experience to their degree.

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NYU Stern School of Business

Program: Master of Science in Business Analytics and AI
Location: New York, United States

NYU Stern School of Business offers a Master of Science in Business Analytics and AI, designed to help students deepen their understanding of the role data plays in business decisions and learn how to leverage AI for innovation.

The program follows a part-time, flexible model consisting of six on-site modules, each lasting between two and seven days, allowing students to integrate their studies alongside professional commitments.

Before applying, prospective students are invited to submit a preliminary form to declare their interest and confirm eligibility. This includes providing details on professional experience and uploading a resume.

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SKEMA Business School

Program: MSc Artificial Intelligence for Business Transformation
Location: Paris, France

As part of its AI School for Business, SKEMA Business School offers an MSc in Artificial Intelligence for Business Transformation, aiming to prepare professionals who understand AI both technically and strategically.

Half of the program is delivered by ESIEA, a French engineering school, focusing on AI algorithms, computer programming, and IT infrastructure. The other half is taught by SKEMA faculty, with a focus on the management of AI.

The program also includes applied projects in data science and AI, often in collaboration with industry partners such as Microsoft.

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©SKEMA Business School / Facebook

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S P Jain School of Global Management

Program: Master of Artificial Intelligence in Business
Location: Dubai, UAE or Sydney, Australia

The Master of Artificial Intelligence in Business at S P Jain School of Global Management is taught over two years, covering both AI foundations and business disciplines such as economics and marketing—offering students a balance between technical expertise and business acumen.

Students can choose to study at either the school’s Dubai or Sydney campus, though costs vary. For example, those studying in Dubai must pay an additional $1,500 in visa and healthcare charges on top of tuition fees.

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UBI Business School

Program: Master’s in Management of AI and ML
Location: Brussels, Belgium

Produced in collaboration with Microsoft, this program—described by the school as ‘avant-garde’—focuses on AI tools such as generative AI, data modeling, and edge computing.

It is particularly suited to working professionals, offering flexible formats including in-person, hybrid, and virtual learning. This setup mirrors professional environments, making it easier for students to balance work and study.

Thanks to UBI’s partnership with Microsoft, students also gain access to Microsoft certifications and become members of the Microsoft Learning for Educators platform.

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Vlerick Business School

Program: Master’s in Business Analytics and AI
Location: Ghent, Belgium

Vlerick Business School offers a Master’s in Business Analytics and AI designed to help students become data-savvy decision-makers.

Through an active learning approach, the program incorporates small-team projects such as a Hackathon, where students collaborate to put their skills into practice.

Students also take part in a study trip to Amsterdam, visiting leading companies such as Google and Capgemini, alongside networking events and guest lectures.

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Montpellier Business School (MBS)

Program: MSc Big Data and Artificial Intelligence for Business
Location: Montpellier, France

The MSc in Big Data and Artificial Intelligence for Business at Montpellier Business School explores how data science, big data, and AI can be used to improve decision-making in business.

This program is suited for those aspiring to careers such as data scientists, business intelligence analysts, or database managers. Many graduates successfully secure such roles upon completion.

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University of Edinburgh Business School

Program: Master’s in AI for Business
Location: Edinburgh, Scotland

The University of Edinburgh Business School’s Master’s in AI for Business is built to help students understand AI and its potential to drive business transformation.

The first semester covers compulsory courses such as Python Programming and Storytelling with Data. In the second semester, students select two or three optional courses, including Change Management, AI, Imagination and Creativity, and Digital Business: Competing in the Age of Platforms.

The program concludes with a summer dissertation, allowing students to bring together everything they have learned.

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University of Rochester, Simon Business School

Program: MS in Artificial Intelligence in Business
Location: New York, United States

The Simon Business School, part of the University of Rochester, offers an MS in Artificial Intelligence in Business, focusing on cutting-edge AI technologies that are reshaping industries worldwide.

Students can complete the program in two semesters without an internship, or in three semesters with the extended internship track, which provides invaluable real-world experience.

Over 90% of Simon Master’s students receive some form of scholarship support, making the program more financially accessible.

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©Simon Business School / Facebook

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Key takeaways:

⇨ Master’s programs in AI are offered at several leading business schools, including NYU Stern, EDHEC, and HEC Paris. 

⇨ Students can pursue a Master’s in AI in various locations around the world, such as the UK, France, Belgium, Sydney, and Dubai.

AI in Optometry: Revolutionizing Eye Care Through Innovation

Artificial intelligence is revolutionizing optometric practice by enhancing diagnostic capabilities, streamlining workflows, and creating personalized patient experiences. Modern eye care professionals increasingly leverage modern technology in mining principles to improve clinical outcomes while optimizing practice efficiency and patient satisfaction.

Diagnostic Enhancement and Clinical Decision Support

AI systems now analyze retinal images with remarkable precision, detecting subtle signs of conditions like diabetic retinopathy, glaucoma, and age-related macular degeneration often before they become clinically apparent. These sophisticated algorithms identify patterns invisible to the human eye, providing optometrists with valuable diagnostic assistance during routine examinations.

Advanced algorithms process optical coherence tomography (OCT) scans to distinguish between pathological and non-pathological cases with accuracy rates of 88-90% in clinical studies. This technology significantly reduces interpretation time while maintaining high diagnostic reliability, allowing optometrists to examine more patients without compromising care quality.

Personalized Treatment Planning

By analyzing comprehensive patient data, AI helps optometrists develop individualized treatment plans tailored to each patient’s unique needs. These systems can predict disease progression patterns based on similar patient outcomes, enabling more proactive intervention strategies.

AI-powered tools assist in creating precise vision correction prescriptions by simultaneously analyzing multiple data points including corneal topography, wavefront aberrometry, and pupillometry. This comprehensive approach results in more accurate and personalized solutions for patients with complex refractive needs, particularly those with irregular astigmatism or higher-order aberrations.

What Administrative Efficiencies Does AI Bring to Optometry Practices?

Beyond clinical applications, AI transforming mining industries has parallels in how it transforms practice management by automating time-consuming administrative tasks and optimizing operational workflows, allowing staff to focus more on patient care and practice growth.

Reducing Administrative Workload

Real-world implementations demonstrate that AI tools for appointment scheduling, inventory management, and billing can reduce administrative workload by up to 40%. One notable example comes from See Optometry in South Australia, where Principal Optometrist Martin Diep implemented AI solutions that dramatically improved efficiency and freed staff time for enhanced patient care.

AI-powered medical scribes automatically document patient encounters, converting consultation audio into structured clinical notes. Tools like Patient Notes capture essential details including visual acuity measurements, lens prescriptions, and examination results without requiring manual data entry. This technology also includes optometry-specific prompts that can be customized to match individual practice workflows.

Streamlining Communication with Healthcare Providers

AI tools generate comprehensive referral letters and reports for other healthcare providers with remarkable efficiency. Optometrists using these technologies report time savings of approximately 70% when creating communications for GPs, ophthalmologists, and other specialists.

Jing Chen, an optometrist and co-founder of WorkRex and Locum.ly in Melbourne, uses AI to summarize large amounts of information and generate professional reports for referrals. By inputting de-identified clinical findings into AI systems with carefully crafted prompts, she creates comprehensive documentation that enhances inter-professional communication while maintaining patient confidentiality.

How Does AI Enhance Patient Education and Engagement?

Artificial intelligence is transforming how optometrists communicate with and educate their patients about eye conditions and treatment plans, leading to better understanding and treatment adherence.

Personalized Patient Education Materials

AI generates customized educational materials based on specific patient conditions and treatments. These tailored resources help patients better understand and remember information discussed during consultations, improving compliance with treatment recommendations.

Optometrists can quickly create personalized handouts explaining conditions, treatment options, and home care instructions. As Chen notes, “It’s hard for patients to remember everything discussed during a consultation,” and AI-generated materials address this challenge by providing clear, consistent information patients can reference at home.

Improved Patient Experience

By automating routine documentation tasks, AI allows optometrists to dedicate more time to meaningful patient interactions. This shift enhances consultation quality and strengthens the practitioner-patient relationship, a critical factor in treatment adherence and patient satisfaction.

AI mill drive optimization concepts are being applied to predict patient needs based on historical data, enabling proactive care approaches. This anticipatory care model improves overall satisfaction while potentially identifying emerging health concerns before they become problematic.

What Marketing Advantages Does AI Offer Optometry Practices?

AI technologies are transforming how optometry practices market their services and connect with potential patients, creating more targeted and effective promotional strategies.

Customer Segmentation and Personalized Marketing

Advanced AI algorithms analyze customer data to create detailed audience segments based on demographics, behavior, interests, and purchase history. This segmentation allows practices to target specific groups with relevant messaging that resonates with their unique needs and preferences.

AI Marketing Strategy	Impact on Optometry Practice
Customer Segmentation	Enhanced audience targeting and relevance
Predictive Analytics	Improved ad performance and click-through rates
Dynamic Content Optimization	Higher conversion rates from personalized content
AI-Powered Advertising	Optimized ad placement and timing
Cross-Channel Personalization	Consistent brand experience across platforms

Real-World Marketing Results

Lifestyle Optical, a luxury eyewear retailer in Sydney, implemented AI-driven marketing strategies with transformative results. Their implementation of AI-powered customer segmentation and personalization strategies delivered impressive outcomes:

• 225% increase in Cartier frame sales during promotional events
• 258% growth in Tom Ford product sales year-over-year
• 188% improvement in Moscot design day performance
• 180% higher email click-through rates
• 1,853% increase in social media reach
• 4,185% jump in link clicks from marketing content

Laura Reale, optometrist and co-owner of Lifestyle Optical, noted a significant shift in customer behavior following AI implementation: “Customers now come into the store asking where specific brands are – not if we carry them – and they come in ready to buy.”

What Challenges Must Optometrists Consider When Implementing AI?

While AI offers significant benefits, optometry practices must navigate several important considerations during implementation to ensure success and compliance.

Data Privacy and Security Concerns

Protecting patient information remains paramount when adopting AI technologies. Practices must ensure all systems comply with relevant privacy regulations including the Privacy Principles of Australia or New Zealand and implement robust security measures including:

• End-to-end encryption for data transmission and storage
• Secure authentication protocols for system access
• Regular security audits and vulnerability assessments
• Clear data retention and deletion policies

Patient Notes, an AI medical scribing solution, addresses these concerns by automatically deleting session data within 30 days, with options for manual deletion at any time for immediate data removal when needed.

Integration with Existing Systems

Successful AI implementation requires seamless integration with current practice management systems and workflows. Optometrists should evaluate compatibility with existing electronic health records and other software before adoption to avoid disruption and maximize efficiency gains.

Training staff on new technologies is essential for maximizing benefits while minimizing disruption to practice operations. A gradual implementation approach with adequate training time helps ensure staff confidence and competence with new systems.

How Can Optometrists Begin Implementing AI in Their Practices?

For many optometrists, the journey toward AI adoption begins with identifying specific practice needs and exploring available solutions that address those particular challenges.

Starting with Quick Wins

Practices new to AI can begin with straightforward applications that deliver immediate benefits:

• Transcription software for meeting notes and patient encounters
• Automation tools for repetitive administrative tasks
• AI-assisted content creation for patient education materials
• Scheduling optimization systems to improve appointment efficiency

These entry points require minimal investment while demonstrating AI’s potential value to the practice. Martin Diep’s approach exemplifies this strategy – he booked a workshop to explore AI applications, focusing on discovering unknown capabilities and transformation potential before making significant investments.

Developing a Comprehensive AI Strategy

As comfort with AI grows, optometrists can develop more sophisticated implementation strategies:

1. Assess current practice pain points and inefficiencies
2. Research AI solutions specifically designed for optometry
3. Evaluate potential return on investment for various technologies
4. Create a phased implementation plan with clear success metrics
5. Establish policies for responsible AI use and data handling
6. Continuously monitor performance and adjust as needed

This structured approach helps practices maximize benefits while managing implementation challenges, ensuring technology serves the practice’s specific needs rather than becoming an end in itself.

What Future Developments in AI Will Impact Optometry?

The evolution of AI technologies continues to create new opportunities for optometric innovation and expanded clinical capabilities.

Advanced Diagnostic Capabilities

Emerging AI systems promise even greater diagnostic precision, potentially identifying eye conditions before clinical symptoms appear. These technologies may enable earlier intervention and improved treatment outcomes for conditions where early detection significantly impacts prognosis.

Research suggests future AI revolution insights could analyze subtle changes in retinal vasculature to predict systemic conditions like hypertension and diabetes, positioning optometrists as important contributors to overall health monitoring. This capability could transform routine eye examinations into comprehensive health assessments, expanding the optometrist’s role in preventive healthcare.

Expanded Patient Support Tools

AI-powered smartphone applications are being developed to assist patients with vision impairments in daily life management. These tools can help identify medication bottles, provide medication reminders, and offer other practical support to improve treatment adherence and quality of life.

Virtual assistants specialized for optometry may soon provide patients with personalized care instructions, appointment reminders, and answers to common questions between office visits. These digital companions could significantly enhance patient engagement and satisfaction while reducing call volume for routine inquiries.

Frequently Asked Questions About AI in Optometry

Is AI Meant to Replace Optometrists?

No, AI technologies are designed to augment clinical expertise, not replace it. These tools support optometrists by handling routine tasks, providing decision support, and enhancing diagnostic capabilities. The optometrist’s clinical judgment, experience, and patient relationship remain central to quality eye care.

As Darren Ross, CEO of Patient Notes, emphasizes: “AI helps optometrists access historical patient information quickly, analyze trends, and make informed decisions… it’s an assistive tool. Clinicians should always review the notes as they are your final medical records.”

How Accurate Are AI Diagnostic Systems?

Clinical studies show that leading AI diagnostic systems achieve accuracy rates of 88-90% for conditions like diabetic retinopathy and glaucoma. However, these systems function best as supportive tools rather than standalone diagnostic solutions. Optometrists should always review AI-generated findings before making clinical decisions.

The accuracy of these systems continues to improve as more data becomes available for training and validation. Regular updates ensure AI tools incorporate the latest clinical knowledge and technological advances.

What Are the Costs Associated with Implementing AI?

Implementation costs vary widely depending on the specific technologies adopted. Many AI solutions now operate on subscription models, reducing initial investment requirements. Practices should consider both direct costs (software licenses, hardware upgrades) and indirect costs (staff training, workflow adjustments) when evaluating potential AI investments.

The return on investment typically comes through improved efficiency, enhanced patient care, and new revenue opportunities created by AI-enabled capabilities. Careful analysis of specific practice needs helps identify the solutions that will deliver the greatest value relative to implementation costs.

Conclusion: Embracing AI as a Transformative Force in Optometry

Artificial intelligence represents a significant opportunity for optometry practices to enhance clinical capabilities, improve operational efficiency, and deliver superior patient experiences. By thoughtfully integrating data-driven operations, optometrists can position their practices for future success while maintaining their essential role in eye care delivery.

The most successful implementations will balance technological innovation with the human elements that remain fundamental to optometric practice: clinical expertise, compassionate care, and meaningful patient relationships. As research studies confirm, AI in optometry continues to evolve, practices that embrace these technologies while preserving their core values will be best positioned to thrive in the changing healthcare landscape.

As summarized by industry experts, “the keys to success will be thoughtful adoption, smart integration, and prioritizing patient outcomes” with “data security at the forefront of every decision.”

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According to a recent MIT report, 95% of organizations are seeing no (or very limited) returns from their internal generative AI pilot programs, despite large investments in their implementation. The study has its limitations, especially given the limited sample size of professionals and executives surveyed. But it offers a stark counterargument to the optimistic narratives promoted by OpenAI, Anthropic, and other prominent genAI companies.

Skepticism around their products’ ability to help companies increase revenue and profit, even in the medium to long term, is becoming more common, and not just among AI optimists anymore. This shift has intensified after GPT-5, OpenAI’s latest LLM, failed to live up to the heightened expectations set by Sam Altman himself.

While the use case and profitability of genAI applications is still very much to be proven, the IT industry’s bet on genAI and the companies developing it is already massive. From 2013 to 2020, cloud infrastructure capital expenditure grew from $32 billion to $119 billion, driven mostly by the rise of social media platforms and video content.

Post-Covid, the curve goes wild: in 2024, spending reached $285 billion, and in 2025, the top 11 cloud providers are forecasted to invest almost $400 billion. That’s more than they’ve committed in the past two years combined and the figure mostly stems from the massive compute needs for training and inferring LLM models.

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A Complex Answer

The fundamental disconnect between the money companies are spending to compete in the AI race and the potential return on investment is widening as fast as their capex. A growing cadre of experts is asking the same fundamental question: with this uncertainty around the effectiveness of its real-world business application, will gen AI as a market ever be able to make money?

In a moment of profound change, the answer is complex and open to interpretation. On one side, we need to take the “inevitabilism” of Altman, Amodei, and other AI maximalists with a sizable grain of salt. It’s simply not true that a future where their particular flavor of genAI dominates the workplace and integrates into our lives at all levels is “inevitable”, despite what they’d like us to take at face value as they scour for even more funding dollars.

On the other hand, it’s undeniable that, despite the debate about its applicability, genAI represents a technological revolution. The technology itself is formidable, and its positive impact on at least personal productivity is undeniable. Yet it remains vastly unclear, as the MIT study demonstrates, whether it will ever be able to justify its technological costs.

A Bubble?

These two sides of the same coin can be true at the same time: genAI is one of the most important technologies of all time, and we’re in a bubble regarding its potential and applications. Remarkably, this admission comes from Sam Altman himself. OpenAI’s CEO, in a recent interview with US reporters, tried to deflate expectations he helped set up in the past:

“Are we in a phase where investors as a whole are overexcited about AI? My opinion is yes. Is AI the most important thing to happen in a very long time? My opinion is also yes.”

To make matters worse, all generative AI services we’re currently using personally and at work (including ChatGPT, Claude, Cursor, Microsoft Copilot, and Google Gemini) are heavily subsidized by either investors’ or companies’ money. While the combined number of their active users already exceeds one billion, both OpenAI and Anthropic will close 2025 reporting billions in revenue and even more in losses.

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Like Uber?

The playbook we’re seeing unfold isn’t far removed from that of other hyperscale platforms like Uber. A moment will come when prices must rise to start returning capital to the large number of investors. Uber delayed that moment for years, collecting eager investors’ money with the far-fetched promise of autonomous driving—until that didn’t work anymore.

OpenAI and Anthropic are doing the same, dangling the promise of AGI (artificial general intelligence) or ASI (artificial super intelligence) to collect billions in funding while waiting long enough for the technology to become indispensable. But while ride-hailing had immediate benefits in disrupting an established industry with lower cost solutions, the AI startups’ bet is far bolder and definitely way more expensive to maintain.

A Skeptic at Goldman Sachs

Jim Covello, the Head of Global Equity Research at Goldman Sachs, is among the genAI skeptics. He says that to earn a relevant return on investment, gen AI should be able to solve extremely complex problems that justify its immense cost.   

In a Goldman Sachs report published in 2024, Covello explains:   

“We estimate that the AI infrastructure buildout will cost over $1tn in the next several years alone, which includes spending on data centers, utilities, and applications. So, the crucial question is: What $1tn problem will AI solve? Replacing low- wage jobs with tremendously costly technology is basically the polar opposite of the prior technology transitions I’ve witnessed in my thirty years of closely following the tech industry. While the question of whether AI technology will ever deliver on the promise many people are excited about today is certainly debatable, the less debatable point is that AI technology is exceptionally expensive, and to justify those costs, the technology must be able to solve complex problems, which it isn’t designed to do.”

One year after Goldman Sach’s report, we are still very much hearing the same narrative, with AI companies swearing by the scaling myth, saying all they need is just a bit more data, just a bit more training, and just a bit more investor money to get all that.   

A $1 Trillion Question

It’s a $1 trillion question: what happens when the financial realities can no longer be delayed, with investors and companies realizing that the chasm between costs and applications can’t be filled?

History suggests that bubbles burst when the gap between investment and practical returns becomes unsustainable. The dot-com crash of 2001 offers a sobering reminder of what occurs when investor enthusiasm dramatically outpaces actual utility, even though the fundamental technology (the Internet) was so important that it would later become ubiquitous.

If businesses begin demanding concrete returns on their AI investment and find them lacking a significant market correction could follow. Companies that have built their valuations on AI promises may face a harsh reckoning with reality, negatively affecting the global economy as a consequence.

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