“If you want to get promoted, you’ve got to do the things that we do to operate Accenture. These are the new tools to operate a company. We didn’t go from zero to ‘you won’t get promoted’ in a month. It’s over three years of getting used to the technology, making sure it’s user-friendly, making sure we have the right workbench for people to use, and then saying, Hey, this is Accenture and how we operate,” Julie Sweet said during the Rapid Response podcast, hosted in March 2026.

Asserting that AI is now the main tool for work, the Accenture CEO compared the technology’s push to the use of computers in offices.

“No one would have said that requiring someone to use a computer is coercion. It’s how the companies were going to get work done. Today, AI at Accenture is how we do work,” she said.

In September 2025, Accenture invested more than USD 865 million in a six-month business optimisation programme. The roadmap advocated for reskilling thousands of employees with AI, while removing those who declined to adapt to the tech. In fact, as Generative AI emerged in 2023, with the launch of OpenAI’s ChatGPT, the IT giant unveiled an ambitious three-year plan worth USD 3 billion to bring the technology into the core of its organisational fold. As part of this initiative, the venture set out a goal of expanding its AI talent pool to 80,000 professionals, through new hiring, strategic acquisitions and large-scale training programmes. Today, the consulting giant has a workforce of more than 770,000 employees worldwide.

During her interaction with the podcast host Bob Safian, Julie Sweet also acknowledged the challenges of adapting to a new technology. Further stating that organisations prefer practicing caution about bringing AI into their operations, the Accenture boss stated that companies cannot simply add the technology to their outdated systems and expect quick results. Instead, they need to redesign their workflows around the technology.

Julie Sweet’s Vision

Born in 1967 in Tustin, California, Julie Sweet started her academic career at Claremont McKenna College, where she majored in international relations and earned a Bachelor of Arts (BA) degree. She later enrolled at Columbia Law School to learn more about law and international affairs. In 1992, she graduated with a Juris Doctor from that institution, a significant achievement that influenced the beginning of her professional career.

In 2023, Julie Sweet became the highest-paid woman CEO, earning approximately USD 34 million, after starting her career as an attorney at the prestigious law firm Cravath, Swaine & Moore and eventually becoming the CEO of Accenture. Her career success has also translated into personal wealth, with a net worth estimated to be between USD 75 million and USD 126 million, reportedly based on public filings and her stock holdings.

Under her leadership, Accenture has doubled down on AI as the core of its business principles. In March 2026, it made an investment in DaVinci Commerce, a leader in agentic AI-powered commerce, where AI systems increasingly shape the discovery, evaluation and purchase of products.

With AI becoming a primary interface for shopping, commerce is transforming from a human-led search to browsing to AI agents, which take over the tasks like researching, recommending and transacting on behalf of consumers.

“Accenture will now work with DaVinci Commerce to help clients operationalise agentic commerce across the full value chain—from discovery and merchandising through checkout, fulfilment and loyalty. The collaboration reflects growing demand from brands looking to modernise owned commerce platforms and commerce media strategies for AI-driven engagement,” the IT giant announced.

Accenture has also agreed to acquire Ookla, a global leader in network intelligence, competitive benchmarking and customer experience analytics. By integrating Ookla’s data products like Speedtest, Downdetector, Ekahau, and RootMetrics, Accenture will help Communications Service Providers (CSPs), hyperscalers, and enterprises optimise the mission-critical Wi-Fi and 5G networks that power their digital core.

While the AI era is witnessing rapid layoffs in tech sectors, as companies pursue lean, productive and performance-driven organisational structures, Accenture is increasing its recruitment of entry-level professionals across global markets, with Julie Sweet holding the view that recent graduates may actually be better prepared to thrive in an AI-powered workplace.

Given that these graduates are tech-savvy in their academic and professional routines, the Accenture CEO believes that familiarity with technology will give Accenture’s fresh hires an advantage over the legacy workforce. Instead of following the trend of eliminating junior roles in exchange for AI, the IT venture is restructuring job responsibilities to align with the technology’s growing use.

Along with automating repetitive tasks, Accenture is placing greater push on human-driven capabilities such as problem-solving, strategic thinking and communication, with Julie Sweet telling the Rapid Response podcast that entry-level roles will remain essential, as they serve as the foundation for developing future experienced professionals.

“Training programmes for new hires have also been redesigned. The company is placing a stronger focus on communication skills, strategic thinking and the practical use of AI technologies,” she added.

AI has also emerged as Accenture’s growth engine, as it reported advanced AI bookings of about USD 2.2 billion in the first quarter of FY26, reflecting strong year-on-year growth, while revenues related to the technology reached roughly USD 1.1 billion. Since FY25, the firm has secured around USD 11.5 billion worth of advanced AI projects, earning about USD 4.8 billion.

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If you’re looking to elevate your leadership skills without breaking the bank, this list highlights affordable and free AI-led courses tailored to diverse needs. These programmes are designed to help leaders stay ahead in a rapidly evolving technological environment.

Here are some affordable and free AI-led courses for leaders to up their game without spending a fortune, so you can see which ones suit your needs.

Google’s AI For Everyone

The search engine giant’s free course introduces the basics of AI, making it ideal for business leaders who want to grasp the potential of artificial intelligence without prior technical knowledge. Offered by one of the world’s leading technology companies, the course reflects its mission to organise the world’s information and make it universally accessible. The company is also known for pioneering innovation in AI and democratising technology through free resources and tools.

AI For Leaders By Great Learning

This free course gives executives and managers a basic understanding of the business applications of artificial intelligence. With a focus on professional and higher education, “Great Learning” is a global education company that offers a variety of programmes aimed at preparing students for roles that are ready for the future.

Elements Of AI By The University Of Helsinki

The well-known free course “Elements of AI” aims to demystify AI for both leaders and novices. The oldest and biggest academic institution in Finland, the University of Helsinki, works with MinnaLearn to provide accessible, interesting content for a variety of audiences and to advance AI literacy.

AI For Decision-Making By Udemy

This affordable course focuses on useful tools that leaders can use to incorporate artificial intelligence into their decision-making. Udemy is a global online learning marketplace that links educators and learners by providing flexible, reasonably priced courses in a wide range of subjects to help students advance their skills and accomplish their objectives.

Generative AI For Leaders By Coursera

An introduction to generative AI and its uses in leadership is given in this course. To provide online courses, specialisations, and degrees, Coursera collaborates with top universities and institutions. It seeks to provide top-notch education to students everywhere, encouraging lifelong learning and professional advancement.

The post Want to AI-proof your career? Check out the list of affordable leadership courses appeared first on International Finance.

Approximately 86% of 16,000 professionals worldwide surveyed by Adobe in 2025 reported actively using AI in their creative workflows. It’s no longer futuristic; it is the reality of our times. One would imagine that AI tools would free people from the difficulties of labour and prolonged work hours.

However, the opposite is happening. Instead of leisure, workers around the world are met with demands for unyielding speed and inhuman productivity.

We must decide whether this universal integration signifies genuine technological progress or whether it simply marks the moment human artistic labour becomes economically mandatory to execute at the pace dictated by Silicon Valley’s algorithms.

There is an immense economic pressure forcing creative professionals to comply or face immediate market obsolescence. The data confirms that AI is deeply integrated into creative workflows, yet this utility must not be mistaken for ethical merit or long-term soundness.

Creative professionals do see genuine, tantalising opportunities, with over half reporting that AI helps them explore new mediums and a remarkable 46% believing it helps them create higher-quality work.

This is the lure, the captivating promise of instantaneous enhancement and boundless efficiency, a promise designed to mask the underlying erosion of value and independence. The current analytical view of AI’s labour impact is dangerously complacent, focusing almost exclusively on macro-economic trends while entirely ignoring the microscopic, fundamental erosion occurring at the individual creator level.

Technophiles often point to recent analyses showing that the broader labour market has not experienced a discernible disruption since the public release of major generative AI systems, a finding that allegedly undercuts fears of immediate mass job losses across the entire economy.

This fact is often presented as reassurance, suggesting a measured, benign adoption trajectory, yet it hides a critical, predatory truth, namely that AI first displaces value and incentive long before it ever displaces employment.

Copyright and corporate capture

To understand the core immorality of the generative AI revolution, we must look no further than the fuel source that powers it, which is the massive, unprecedented datasets of human creative expression upon which these models are trained. These datasets, which developers use as a neutral shorthand for copyrighted works, are the products of millions of human lives, careers, and artistic struggles.

The training process, executed often without explicit permission, licensing, or any financial compensation, represents the original, defining sin of this entire industry, effectively turning the intellectual property and life’s work of millions of artists into free, disposable energy for a burgeoning multi-trillion-dollar technological complex.

The fear among creators is profoundly visceral and absolutely justified, because unlicensed training will fatally corrode the creative ecosystem, permitting AI-generated content to directly and unfairly compete in the marketplace with the very artists whose works were ingested and repurposed without consent.

The US legal system is currently caught in the paralysing gridlock of this crisis, embroiled in dozens of high-stakes lawsuits that specifically focus on the strained application of copyright’s fair use doctrine to the mass ingestion required for AI training.

These legal challenges have exposed the staggering scale of the alleged infringement, including claims against powerful entities like Meta for allegedly using its corporate IP addresses to download nearly 2,400 copyrighted adult movies via BitTorrent for the explicit purpose of training its AI systems, a transgression that puts the potential damages well over $350 million.

The stakes in these legal battles are existential, with some developers arguing that requiring formal licensing would irreparably throttle a transformative, world-changing technology, while creators fear, with equal passion, that allowing this unlicensed exploitation will mean the inevitable death of the human creative community. The public interest demands striking an effective balance, one that allows technological innovation to flourish without dismantling the thriving community of creators who feed it.

In terms of intellectual property protection, the American courts have established one clear and critical legal marker, confirming that human authorship is a foundational, bedrock requirement for copyright protection, thereby establishing a critical and necessary distinction between a human using a sophisticated tool and the tool itself attempting to claim the rights to its output.

This decision affirms the principle that intellectual property rights must apply to works generated by humans. The ruling addresses only the resulting output, leaving the foundational injustice of the mass, uncompensated training data capture entirely unresolved, a loophole large enough to drive a generative AI truck through.

Crowding out true innovation

The deployment of generative AI has led to a fundamental economic revaluation of creative labour, posing an existential threat to the long-term health of the artistic community. When AI provides sophisticated tools that enable individuals without traditional, hard-won artistic skills to produce high-quality, technically sound work in fields like illustration, design, or digital music, it fundamentally lowers the barrier to entering the market.

While accessibility sounds like a profound social good, the immediate economic consequence is brutally clear: this widespread capability devalues the artistic skills honed over years of craft, study, and sacrifice, diminishing their perceived market worth and making the professional’s work less appreciated or undervalued.

This devaluation sets the stage for the most dangerous economic outcome, the widely observed “crowding out” effect. Generative AI excels at creating high-volume, low-variance, and highly formulaic work at nearly zero marginal cost, making these formulaic outputs significantly cheaper than traditional human creations.

The lower cost of this technically proficient content then acts as an economic steamroller, systematically forcing out the more costly, experimental, and risky human creations that are essential for driving long-term innovation and stylistic evolution in culture. This phenomenon is not theoretical; the marketplace is already providing clear warning signs, with consumers sometimes showing a direct taste for the influx of AI-generated images, selecting them over human-generated works, confirming that increased competition and variety for buyers come at the devastating cost of financially crippling the creators who fuel the market.

The ultimate psychological and financial violation faced by creators is the commodification of their unique artistic style. Creative professionals are acutely aware of this profound threat, which is why surveys indicate a significant majority express keen interest in being paid specifically to license their unique artistic style (58%) or getting paid for having the models trained on their specific body of work (55%).

Generative AI seeks to distil the most subjective, intangible, and unique element of an artist, his/her individual aesthetic footprint, into a fungible, replicable, and licensable commodity.

If a distinct style can be captured, licensed, and then replicated infinitely by a machine for a small fee, the intrinsic, irreplaceable value of the human hand, the individual struggle, and the unique history behind that style, everything, gets tragically erased.

Yet here lies the supreme, glaring irony, the self-defeating nature of the AI developers’ exploitation. The fundamental truth of machine learning is that the output of these complex models is fundamentally limited by the volume and, more importantly, the quality of the input, the human-generated works they ceaselessly ingest.

Suppose the economic displacement and devaluation of human creators continue unabated, and their financial incentives diminish to the point of collapse. In that case, the flow of new, high-quality, experimental, and challenging human work, the raw fuel of the entire system, will inevitably degrade. Machines are capable of regurgitation. They can modify existing work. But the true fuel of the creative economy is raw, high-quality human work. And this model ensures that there will be recycling and no innovation or radical experimentation in the field of creative arts. It demonstrates that a thriving and compensated creative community is necessary for technological advancement, not merely an optional luxury.

It’s important to recognise that not all creatives oppose technology. They are simply asking to be remunerated for the work they put in. A massive 83% of creative professionals think genuine transparency around whether artwork was created using generative AI is essential, and the same high percentage demands transparency about the specific data used to train the models.

This urgent need for verifiable provenance has spurred important initiatives, such as the Coalition for Content Provenance and Authenticity (C2PA), which now provides open technical standards for publishers, creators, and consumers to establish the origin and edits of digital content, thereby providing verifiable assertions about content origins and, most importantly, ensuring a necessary baseline of trust in this increasingly murky digital marketplace.

The advent of these transparency tools, which allow users to know the source of the information they are receiving, is the only possible path toward stabilising an ethical market where human and machine creations can coexist.

Ghost in the machine

AI can make skills slightly redundant. But true creativity and imagination come from intentionality and lived experiences. Human imperfection mixed with imagination is necessary for art. It can be mimicked, but machines cannot create anything new that is also relatable to the human psyche. We must draw a clear and forceful distinction between sophisticated computation and genuine, conscious creation.

Marvin Minsky, one of the foundational pioneers of AI, famously imagined machines capable of complex human reasoning. Yet the 21st-century generative AI has emerged primarily as the product of immense computational capacity and sophisticated algorithms, fundamentally departing from that initial, perhaps overly optimistic, vision.

The core difference remains immutable. Human creativity is intrinsically rooted in genuine vision derived from living within a specific physical world, from experiencing the emotional complexity of loss, the transformative power of joy, and navigating complex cultural nuances.

AI may function as a superb mimic and an incredibly fast learner, generating complex linguistic experimentation if prompted, but mimicry is not the same as true insight, and the resulting art risks lacking the genuine human depth that separates mere image generation from soulful expression.

Philosophical analysis strongly suggests that mass AI-generated artifacts cannot be legitimately defined as bona fide “art” because they fundamentally lack the sort of intentional control that is plausibly accepted as a necessary precondition for the label of “arthood.”

The aesthetic experiences created by mass-produced AI are often similar to those found in inorganic nature, relying solely on formal properties. Because the work is the result of statistical probability and algorithmic iteration rather than struggle, conscious choice, or personal commitment, it risks meaning nothing to the AI and consequently risks meaning substantially less to us, the audience. This absence of a discernible consciousness or intentional struggle creates an aesthetic void.

Imperative of human accountability

We stand at a profound cultural and economic precipice, facing an existential crisis that must be addressed with clarity and legislative courage. The problem isn’t the technology, which promises genuine improvements to people in all fields of life. As usual, the culprit is corporate greed and unchecked power that boardrooms wield.

These entities have ruthlessly leveraged this transformative capability to systematically dismantle existing legal and economic frameworks for their own profit, establishing an innovation structure that demands the consumption of past creativity while vehemently refusing to compensate the millions of creators whose labour and intellectual property fuel their systems.

The question we face today is fully comparable in its magnitude and complexity to the social shifts that accompanied the advent of the printing press centuries ago, demanding that society urgently debate and establish entirely new, robust frameworks for genuinely rewarding creativity and ensuring that information provenance is transparent and trustworthy.

We cannot possibly maintain a functioning, free creative ecosystem if the people in possession of the truth and the facts, the creators whose work defines our culture, are unable to win the necessary legal and rhetorical argument against powerful, highly capitalised corporate interests.

To effectively preserve the unique and irreplaceable value of human creativity and ensure a stable future for the arts, our political and regulatory response must be swift, comprehensive, and absolute, demanding three non-negotiable elements.

The first essential requirement is transparency and provenance, mandating the full, detailed disclosure of training data used by all generative models. Furthermore, we must implement verifiable authentication systems, such as the standards offered by C2PA, to provide immediate, verifiable confirmation of content origins, allowing both consumers and competitive creators to know exactly when the output is the result of a machine and statistical inference. This clarity is the minimum requirement for a fair market.

The second non-negotiable element is compensation and licensing, requiring an immediate end to the cynical reliance on tenuous fair use arguments for mass, systematic data ingestion. Governments must proactively establish robust collective licensing organisations or statutory compensation mechanisms that ensure genuine financial arrangements for all artists whose work is used to train these models. Creator participation must be predicated on appropriate financial arrangements, recognising that they hold the key intellectual assets that allow the algorithms to function.

The third critical element is the preservation of authorship, legally reinforcing the established principle that copyright ownership must belong only to human beings, recognising the inherent distinction between human creation and machine replication. This ensures that the unique human elements, including personal stories, genuine emotional resonance, and complex cultural nuance, remain the legally protected, recognised, and invaluable core of the creative economy, serving as the ultimate differentiator against the sea of machine-generated competence.

Not too long ago, we envisioned artificial intelligence handling the mundane tasks, like data entry, dishwashing, and manual labour, allowing us to focus on pursuits such as poetry, painting, and philosophy. However, the exact opposite has occurred. We are now automating creative endeavours like poetry and painting for profit, while humans are left to deal with the administrative remnants.

We are at risk of building a culture where the act of creation is viewed as an inefficiency to be solved. We thereby alienate ourselves from the process of creation. It becomes merely a product. A machine can generate a tear-jerking story, but it cannot know what it means to cry.

When we read a book or view a painting, we are unconsciously searching for the hand of the maker, seeking validation that our own joy, suffering, and confusion are shared by another living being. Without that shared resonance, we are simply staring into a mirror of statistical probabilities, profoundly alone. There is a need to fight for these protections to save jobs and to ensure that the future of human culture remains, quite literally, human.

The post The fight for creative rights appeared first on International Finance.

Starting with early rule-based programmatic trading in the 1970s and 1980s, finance firms have long applied statistics and computing to markets. In the 1990s and 2000s, machine learning and neural networks added sophistication.

For example, hedge funds like Renaissance Technologies hired PhDs to use AI for pattern recognition. Today, we stand at a new inflexion point with generative AI and large language models that can process massive streams of text and data and even suggest novel trading ideas. As one Wharton finance expert notes, AI’s evolution “from algorithmic trading to personalised advice” has made finance “fertile ground for AI innovation.”

Applications of AI in finance

AI is now embedded in many financial processes. Broadly, AI serves in trading, analysis, and operations. In trading, automated systems place orders faster than any human can. High-frequency trading algorithms, often powered by machine learning (ML), make thousands of small trades every second to exploit tiny price discrepancies. Many of the largest trading venues are dominated by such “automated trading” in highly liquid assets. In other domains, AI systems read and summarise information.

For example, NLP tools scan newsfeeds and social media to gauge market sentiment, a process known as sentiment analysis. A sudden burst of negative tweets about a company might trigger selling by algorithms. In risk modelling and compliance, AI churns through vast data to calculate creditworthiness or portfolio risk in real time.

Advisors and insurers use AI to predict defaults or claims, while banks deploy chatbots to handle customer queries. In short, AI touches everything from trade execution to loan approvals and is effectively “democratising” access to analytics that only big institutions once had.

The influence of AI and algorithms is clearest in a few headline-grabbing episodes. In January 2021, the GameStop saga showed the power of social sentiment and automated strategies. A surge of retail traders on Reddit’s WallStreetBets sent the share price of the video-game retailer GME skyrocketing over several days.

Hedge funds that had short positions in the stock rushed to close them. Eventually, trading apps temporarily halted trading, igniting a political firestorm. Researchers note that “retail investors using the Robinhood platform” collectively drove the sharp price swing. Although that episode was driven by human coordination online, it attracted algorithmic responses, with some trading bots detecting the rapid price trend and either piling in or pulling out, amplifying volatility.

AI-driven trading has also featured in the activity of quantitative hedge funds. Firms like Renaissance Technologies, Two Sigma, DE Shaw, and others have long used machine learning to devise strategies. A 2019 survey identified those four as pioneers in AI-driven investing. These firms process vast alternative datasets, from satellite imagery of retail parking lots to aggregated price patterns, looking for subtle predictive signals.

For example, AI can spot that a retail chain’s lawns are greener or read thousands of local news sites to update earnings estimates. In late 2022, Reuters reported Renaissance’s quant funds using models to target returns. Although strategies are secretive, experts agree that AI “provides a competitive advantage” in systematic trading.

AI and social media can also combine in troubling ways. Studies and news accounts warn of sentiment manipulation using bots. In a recent report, experts imagined hundreds of AI-generated social media profiles pushing a narrative about a stock. Real people reacting to the buzz drive the price up or down, while those who detect the narrative profit.

The danger is that neither the promoter nor some of the manipulators even realise they’re part of a larger AI-driven scheme, making enforcement hard. In practice, regulators have seen smaller-scale attempts in crypto and DeFi, where “malicious actors…deploy AI bots” on platforms like Telegram to hype assets.
These examples highlight how automated sentiment analysis and engagement can influence markets, sometimes legitimately, with bots surfacing true trends and at other times through coordinated pumping.

Speed, scale and smarter markets

The attraction of AI in finance is clear, as it does things humans cannot. Speed and automation are paramount. Machines can execute orders in microseconds and monitor markets around the clock, far faster than any trading floor. This rapid processing tightens bid-ask spreads and improves liquidity in normal times.

As the IMF notes, technology has “improved price discovery, deepened markets, and often dampened volatility” in normal periods. AI also excels at scalability and data processing. Financial markets generate enormous volumes of data on prices, news, social posts, filings, and satellite images, and AI can sift through it all.

Advanced neural networks and LLMs (Large Language Models) can turn unstructured text into structured signals. For instance, a generative model can instantly read a regulatory filing or earnings call transcript, flagging risks or opportunities. The IMF notes that generative AI lets investors “process very large amounts of unstructured, often text-based, data,” which can improve forecasts and price accuracy.

Another benefit is pattern recognition and precision. AI algorithms can spot complex statistical patterns that humans cannot see, such as nonlinear relationships or high-dimensional correlations.

In portfolio management, for example, deep-learning models and reinforcement learning (RL) can adapt trading rules over time. Quantitative analysts now use RL to optimise asset allocation dynamically, a method well-suited for constantly shifting markets.

These models “identify complex patterns in large datasets” by using millions of parameterised rules, going far beyond traditional formulae. In effect, AI can tailor strategies to ever-changing conditions, learning minute details of market microstructure.

This leads to efficiency and consistency, and routine tasks like compliance checks or customer service get automated via RegTech tools and chatbots, freeing humans for higher-level thinking. In trading, even a tiny improvement can be valuable. A recent AI pilot by HSBC reportedly found a quantum-enhanced model that improved trade-fill predictions by 34% over classical methods.

Finally, AI can open new markets and lower costs. According to the IMF, AI tools are reducing barriers to entry and making it feasible for smaller firms or even individuals to analyse less-liquid markets like emerging debt or certain commodities. By automating research, coding, and data gathering, generative AI might lower the expertise needed to trade exotic assets.

In retail finance, AI-powered robo-advisors have democratised wealth management. One report notes that about half of retail investors say they would use ChatGPT or similar AI to choose or rebalance investments.

This suggests AI is making advanced analysis available to “anyone,” not just Wall Street. Overall, proponents argue these gains, faster reactions to news, more thorough analysis, and automation, should make markets more efficient and investors more informed.

Herding, black boxes and volatility

AI in finance may sound like an interesting and exciting concept, but it is not risk-free. A key concern is model correlation or “monoculture.” When many firms use similar data and algorithms, their trades tend to move together. Regulators and economists warn that this can amplify swings.

For example, if numerous deep-learning models all see a similar signal, they might simultaneously sell stocks, creating a cascade. The Bank of England and the SEC have warned that advanced AI’s “hyper-dimensionality” and shared data sources could lead to just a few dominant models or data providers. In practical terms, a “monoculture” of strategies can increase market correlations and herding. In stressed markets, this may cause liquidity to evaporate suddenly.

A recent IMF analysis noted that many algorithmic funds include safety mechanisms that can all activate at once, causing feedback loops. The 2010 “Flash Crash” is a cautionary example of an automated sell order in one market leading to a chain reaction, briefly knocking 1,000 points off the Dow within minutes.

Though that crash predated today’s AI, it illustrates the danger of automated systems acting in unison. Experts now worry AI-driven trading could produce even faster and larger moves.

Closely related is model opacity and explainability. Modern AI models are often “black boxes” that even their designers cannot fully explain how a specific trading decision was reached. This poses problems for oversight. If an AI fund suddenly accumulates a large position in an obscure asset, regulators might not understand why.

The IMF notes that market participants insist on human oversight and explainable strategies, avoiding purely “black box” approaches. Likewise, a recent Sidley (law firm) report warns that deep-learning and reinforcement-learning systems can have “emergent behaviour” that current market rules aren’t built to catch.

For example, if an AI learnt to detect fraud or manipulate prices in some non-obvious way, standard surveillance systems might miss it. The opacity also raises ethical concerns. How do we verify that AI decisions are fair and unbiased? Finance is littered with historical biases, so an AI trained on past records might perpetuate discrimination. Wharton researchers point out that “bias in AI models is particularly pertinent” in finance, especially lending and insurance.

There are also privacy and manipulation issues. Bad actors can use AI to tailor scams or spread disinformation. SEC Chair Gary Gensler warns that AI-driven narrowcasting can facilitate fraud by zeroing in on individuals’ vulnerabilities. Indeed, regulators have already flagged concerns about AI-generated “deep fakes” of company announcements or rumours that could jolt markets.

Finally, there is the risk of systemic volatility. Many worry that AI might make crises worse by speeding up decision-making. In turbulence, when computers pile into or out of trades in milliseconds, prices can swing violently.

The Sidley report cites the IMF in noting that many AI strategies include circuit-breaker logic that all trigger together under unprecedented moves, risking a sudden freeze of liquidity. In other words, while AI may “damp down” routine volatility by making markets more efficient, it might also set the stage for faster, sharper shocks. Small errors or adversarial attacks on widely used models could propagate quickly across markets. There’s also a concentration risk, and just a few tech firms provide the most advanced AI services and cloud infrastructure, so outages or cyberattacks could disrupt financial systems more broadly.

Governance meets technology

Awareness of these issues is growing. Governments and regulators worldwide are moving to govern AI in finance. In the EU, for example, the new AI Act will classify many financial AI systems as “high-risk” and impose strict obligations.

Practices like AI-based credit scoring or risk pricing will have to meet transparency, data quality, and audit requirements. The stated goal is “consistency and equal treatment in the financial sector.”

Financial institutions are also starting to set their own AI governance. Many banks now require human sign-off on automated strategies. Investment funds maintain “model risk management” teams to test how strategies behave under stress. After the GameStop episode, social platforms began cracking down on stock-promo groups. And financial regulators update rules in light of faster trading speeds.

Still, experts say more will be needed. For example, regulators worry about a lack of transparency when nonbanks use cutting-edge AI outside full supervision. There are calls for international coordination, like the Financial Stability Board surveying AI preparedness in different countries.

Another trend on the horizon is quantum computing. While today’s AI uses classical computers, quantum machines promise even more power. If scalable quantum computers arrive, they could revolutionise optimisation and simulation problems in finance.

Banks are already experimenting. In 2025, HSBC announced a pilot with IBM showing that a quantum algorithm could predict bond trade outcomes 34% better than classical methods.

UBS, Citigroup, and others are researching quantum for portfolio optimisation and risk analysis, and analysts estimate the “quantum technology” market could reach $100 billion by 2030.

In plain terms, quantum computing could solve certain portfolio or pricing problems much faster than today’s fastest supercomputers. However, practical quantum advantage remains in early stages, and much of that promise is years away. Even so, finance leaders like HSBC’s quantum head call this a “new frontier” in computing for markets.

Tale of two traders

The AI wave affects big institutions and small investors differently. Large financial firms such as banks, hedge funds, and trading firms have the resources to develop sophisticated AI. They run vast data centres, hire machine-learning experts, and deploy cutting-edge models.

These institutional players have led the AI adoption for over a decade as they’ve used automated algorithms in HFT and complex derivatives trading. They also invest in AI for risk management and compliance. Because of their scale, they have an edge in computing speed and data access.

Retail investors have lagged but are catching up. The same chatbots and analysis tools that institutions use are now available to individuals in a lighter form. As one industry report noted, about half of retail investors say they would use AI tools to pick or adjust investments, and around 13% already do. User-friendly platforms now offer AI-driven advice and portfolio screening.

For example, retail-friendly robo-advisors automate investing for individuals with modest accounts. Even individual day traders are experimenting with off-the-shelf AI bots or sentiment-tracker apps. Indeed, the widespread curiosity about ChatGPT and AI has “democratised” access to analysis once reserved for big banks. One former UBS analyst remarked that using ChatGPT for stock research was akin to “replicating many workflows” of an expensive Bloomberg terminal.

Balancing innovation and stability

AI’s role in finance is growing fast. As the IMF puts it, generative AI is the “latest stop on a journey” where technology incrementally improves markets. Its benefits in faster processing, new insights from data, and lower costs have already transformed many aspects of trading and investment.

But the journey is not without bumps. Our analysis shows that there are real risks that correlate with AI models, as they could unintentionally synchronise market behaviour, create opaque algorithms, trigger flash crashes, and mislead investors.

Addressing these issues will require vigilance and innovation on their own part. Regulators are awakening to the challenge, calling for AI governance frameworks and updating rules for our faster, more complex markets.

Financial firms are instituting controls on things like explainability requirements and kill switches for trading bots. Meanwhile, new technologies on the horizon, like quantum computing, promise even more powerful tools.

In the end, the AI transformation in finance mirrors other revolutions by creating opportunities and pitfalls. The central question will be how these systems are deployed. Used wisely, they can make markets more efficient and accessible to more people. Used recklessly, they could amplify our worst crashes or widen inequalities.

For investors and policymakers alike, the task is to harness AI’s ingenuity while keeping our collective financial system resilient. Industry leaders must ensure AI markets remain “transparent, fair, and inclusive,” even as the algorithms get ever smarter.

The post AI drives change in global markets appeared first on International Finance.

Mohammed Al-Qarni, an academic and consultant on AI for business, said, “This is a quantum jump in potential productivity, yet history warns us that success hinges entirely on the political and organisational will to design frameworks capable of seizing, not squandering, this opportunity.”

The chilling reality is that this transition is arguably more disruptive than the Software-as-a-Service revolution that preceded it. But what happens when corporations lack the courage to lead? The historical record of digital transformation is littered with the corpses of once-dominant giants, and Kodak serves as the perpetual, damning example.

Despite pioneering digital technology, the company’s strategic reluctance to scale its own innovation, driven by a fear of cannibalising its immensely profitable film business, proved a fatal weakness. Such a protectionist approach and internal cultural resistance led to a catastrophic delay, allowing competitors like Canon and Sony, which had adopted flexible and responsive digital strategies, to capture significant market share.

Survival in a disruptive era demands a willingness to disrupt your own established, profitable business models actively. The transformation required is a radical and holistic overhaul.

Today, the same pattern of institutional failure is visible. While 88% of organisations report utilising AI in at least one business function, showing a clear awareness of the threat, the majority remain dangerously vulnerable. Nearly two-thirds of them confess they have yet to begin scaling the technology across the enterprise, remaining confined to the experimentation or piloting phase.

Such a gap between acknowledgement and action is the single most dangerous vulnerability, demonstrating a failure to establish the strategic and organisational frameworks necessary to manage the disruption. For those who do manage to scale, the financial verdict is already in.

Organisations report an average return on investment of 1.7x on AI and generative AI investments, alongside cost reductions ranging from 26% to 31% across core functions like supply chain and finance. Executives cite tangible improvements, reporting 10% to 20% gains in accuracy, productivity, and time-to-market.

The barriers preventing such scaled adoption are not rooted in technical limitations but in human frailty and strategic myopia. The most frequently cited obstacle is the inability to define clear use cases or establish demonstrable business value.

Such a pattern reflects a failure of imagination, rooted in trying to apply AI to traditional, inefficient problems rather than focusing on “AI-native problems,” which are challenges that become uniquely tractable or profitable only through AI-first thinking.

Compounding this strategic deficit is the internal “human firewall,” and nearly half of CEOs report that employees are resistant or even hostile to AI adoption, often driven by profound anxiety over job security. To overcome this resistance, leadership must invest in upskilling, rewire organisational culture, and establish governance that instils confidence and trust.

Furthermore, even where the will exists, the technical foundation often fails. Businesses consistently identify data quality, availability, and the management of silos as the paramount technical barriers to implementation.

“Without clean, well-organised, and accessible data, advanced models underperform, undermining the entire investment. Agentic AI systems, which require continuous refinement, are particularly dependent on real-time data pipelines and robust governance capabilities often incompatible with rigid, older legacy infrastructure,” Al-Qarni stated.

Strategic autonomy

In an era of accelerating technological competition, the AI transition is fundamentally a geopolitical contest, where national strategy is the new competitive differentiator. The global economic benefits are colossal. There is $19.9 trillion projected to be injected into the global economy through 2030, a figure accounting for 3.5% of global GDP that year.

That injection is projected to create a permanent increase in economic activity, with compounded GDP levels potentially 1.5% higher by 2035. But here is the critical economic context: global growth is projected to decelerate, slowing from 3.3% in 2024 to 3.2% in 2025, while major development finance providers are cutting aid and adopting a markedly more transactional, geopolitical approach to investment.

The United States, the United Kingdom, France, and Germany have all simultaneously cut aid for the first time in nearly thirty years. Consequently, nations can no longer rely on traditional development finance; they must secure resources and advanced infrastructure through massive, proactive investment and strategic partnerships.

Moreover, the pace of AI innovation is inextricably linked to the regulatory landscape, and flexible regulatory environments, such as that in the United States, are already projected to outperform those with more rigid frameworks, confirming that policy itself is a critical competitive lever.

Against this backdrop of global competition and shrinking fiscal space, Saudi Arabia’s comprehensive strategy, anchored in the ambitious economic diversification strategy named “Vision 2030,” provides a clear, state-led template for achieving strategic autonomy and leapfrogging competitors.

Artificial intelligence is positioned as the core technology driving economic diversification beyond oil and building a knowledge-based economy. The National Strategy for Data and AI (NSDAI), established in 2020 by the Saudi Data & AI Authority (SDAIA), sets extremely aggressive, non-negotiable targets to rank among the world’s top 15 nations in AI by 2030.

Massive financial and infrastructural commitments underpin that ambition. The Kingdom aims to attract SAR 75 billion ($20 billion) in AI investments by 2030, covering both local funding and foreign direct investment (FDI) for data and AI initiatives.

Such committed capital is necessary to secure the foundational computational power, demonstrated by strategic partnerships already accelerating the buildout, including the $10 billion, five-year collaboration between AMD and Humain to deploy up to 500 megawatts of AI infrastructure by early 2026, and a $5 billion-plus “AI Zone” partnership with Amazon Web Services (AWS) and Humain.

By aggressively attracting billions in investment from global leaders, the Kingdom is designed to mitigate dependency on transactional global aid and secure continuous access to advanced chip technology, thereby establishing critical strategic autonomy in the global AI race.

Critically, the NSDAI also prioritises policy flexibility, aiming to enact “the most welcoming legislation” for data and AI businesses and talent, including fast-track approvals and IP protections.

Furthermore, recognising that infrastructure is meaningless without talent, the strategy mandates training over 20,000 data and AI specialists to transform the national workforce. Such a comprehensive approach to investment, infrastructure, policy, and human capital serves as the blueprint for securing strategic advantage.

Human-AI value shift

To capture the true value of AI, organisations must discard incrementalism and adopt an AI-first operating model rooted in autonomy. The process begins with an “automation-first mindset,” redesigning processes to embed AI capabilities as core mission enablers, while ensuring systems are modular and interoperable to avoid vendor lock-in.

The primary goal is to streamline workflows and reduce manual effort, unlocking operational savings that can be strategically reinvested into high-value, mission-critical areas. The real disruption lies in embracing agentic AI. There are autonomous agents capable of complex decision-making and orchestrating workflows that rigid legacy systems simply cannot support.

The transition requires disciplined execution; the failure of projects like Volkswagen’s Cariad highlights the danger of strategic overreach, where an attempt is made to deliver a complete, custom technology stack without necessary sequencing and ruthless scope control.

The economic consequences of the transition are profound, resting on the fundamental restructuring of service value. As automation commoditises efficiency, the value proposition shifts dramatically. Professional services will become the most valuable service line, transitioning from transactional execution to strategy-first advisory, guiding organisations on how to architect and implement these complex, autonomous systems.

Simultaneously, managed services will ascend to focus on autonomous orchestration, while support services experience heavy automation at the core, refocusing human expertise onto the premium edges—complex diagnostics and bespoke problem-solving that require critical thinking.

For nations like Saudi Arabia, targeting the training of 20,000 specialists, this predictive shift confirms that training must prioritise advanced advisory, architectural, and integration skills, the core competencies of the high-value professional services sector, to ensure the nation captures the top tier of economic value.

Such a transformation is fundamentally about engineering a robust partnership between human judgment and machine intelligence, establishing systems that are more creative, resilient, and adaptable than either could be in isolation.

While AI excels at processing vast datasets and identifying patterns, it cannot critically apply human judgment, question assumptions, and navigate ethical complexities. Consequently, the most valuable human skills in the AI era will be critical thinking, ethical reasoning, and domain expertise, which assess, refine, and guide AI outputs.

Crucially, the strategic deployment of AI acts as a powerful mechanism for improving overall workforce performance. Studies show AI tools provided a 43% performance increase for lower-performing consultants, compared to 17% for high performers, demonstrating their power to lift the operational baseline of the entire organisation. To realise these systemic productivity gains, organisations must move beyond informal “shadow IT” use.

For chief strategy officers and chief digital officers, the path forward is clear. They must redesign for autonomy, prioritise human-AI complementarity by formalising adoption and reskilling the workforce, and govern and measure strategically.

Only by moving beyond basic ROI and aggressively tracking “Trust and Adoption Velocity” can organisations ensure they are building sustainable, resilient competitive advantage in the new economic epoch.

The post The AI leadership test appeared first on International Finance.

That evening at Gaga’s, Akkaraju found himself sitting next to an investor in Stability AI, the company that launched the wildly popular text-to-image generator “Stable Diffusion” in 2022. Despite its early success, Stability AI came precariously close to the situation of being shut down. The unnamed investor told Akkaraju: ‘You should take Stability and make it into the Hollywood-friendly AI model.’

In 2022, Hollywood was facing headwinds: the number of films and TV shows produced in the United States had dropped by about 40%, due to ballooning production costs, competition from overseas, and widespread labour disputes.

AI promised to bring the numbers back up by speeding production and slashing costs, while letting computers automate the grunt work of translating dialogue, adding visual effects frame by frame, and editing boom microphones out of a zillion shots.

But then came another fear: What if AI starts writing scripts and maybe ends up acting as well? And this “what if” led to two of the industry’s biggest unions conducting strikes to obtain assurances that generative AI wouldn’t replace union jobs in the near term.

In May 2023, the Hollywood writers’ strike over pay broke out, but the bigger issue was the refusal of studios like Netflix and Disney to rule out AI replacing human scribes in the future. The Writers Guild of America (WGA) asked for binding agreements to regulate AI’s use.

The association’s proposal was as follows: nothing written by AI could be considered “literary” or “source” material, which are industry terms that decide who gets royalties, and scripts written by WGA members cannot “be used to train AI.”

However, studios rejected it and allegedly countered with an offer merely to meet once a year to “discuss advancements in technology.”

WGA members further felt that Hollywood executives, where Silicon Valley companies have upended many traditional practices such as long-term contracts for writers, may seek to cut costs further by getting computers to write their next hit shows.

OpenAI’s release of ChatGPT 3.5 at the end of 2022 not only disrupted the tech sector and the broader economy but also captured the public’s attention by excelling at precisely the kinds of non-routine skills (including creative tasks) long considered quintessentially “human.”

And Hollywood writers became the first and most visible face of the resistance to generative AI, speaking volumes about the nature of the new technology and the kinds of livelihoods that it will impact most.

Their victory in 2024, in securing first-of-their-kind protections, now offers important lessons for other unions and professional organisations, policymakers, and workers across a range of occupations who may face similar disruptions to their careers.

After the writers, it was the turn of the Hollywood actors, whose union SAG-AFTRA in August 2024 signed a deal with online talent marketplace Narrativ that enables actors to sell advertisers’ rights to replicate their voices with AI.

The concern arose from the fear that AI could commonly misuse artists’ likenesses. The new agreement now seeks to ensure actors derive income from the technology and have control over how and when their voice replicas are used.

Narrativ is known for connecting advertisers and ad agencies with actors to create audio ads using AI.

As of 2025, AI is becoming the new normal in Hollywood, with Stability AI, once in a precarious position, rewriting the industry’s “creativity rulebook” through its innovative solutions.

Stability AI almost floundered

Major studios and streaming services are currently competing to develop their own “AI Strategies.” Since 2022, several startups, including Luma, Runway, and Asteria, have begun creating tools to support these efforts. Akkaraju, back in 2024, saw the opportunity in front of him. Stability AI had the technology. It just needed a Hollywood finish. As far as he could tell, there was only one problem. Didn’t the company already have a CEO?

When Emad Mostaque, a former hedge fund manager, founded Stability AI in 2020, the company’s mission was to “build systems that make a real difference” in solving society’s toughest problems. By 2022, the system Mostaque felt he needed to build was a cloud supercomputer powerful enough to run a generative AI model. OpenAI was gaining traction with its closed-source models, and as per the American tech journalist Zoe Schiffer, Mostaque wanted to make an open-source alternative—“like Linux to Windows.”

“He offered up the supercomputer to a group of academic researchers working on an open-source system where you could type words to generate an image. The researchers weren’t going to say no. In August of that year, they launched Stable Diffusion in partnership with Mostaque’s company,” the scribe recollected.

The text-to-image generator was a breakout hit, garnering 10 million users in two months.

“It was fairly close to state-of-the-art. It allowed researchers to essentially extend the model, fine-tune it, and it spurred a whole community into action in terms of creating enhancements and add-ons,” said Maneesh Agrawala, a computer science professor at Stanford University, while noting that openness was core to the model’s success.

By October 2022, Stability AI had only 77 employees, but with thousands of times that many people in the wider Stable Diffusion community, it could compete with its bigger rivals. Mostaque raised $101 million in a seed round from venture capital firms and hedge funds, including Coatue and Lightspeed (the final million, he tells me, was for good luck). The company became a unicorn.

Former Stability AI employees describe Mostaque as a visionary. He spoke eloquently about the need for a democratic AI. In the not-too-distant future, Mostaque told employees, the company would solve complex biomedical problems and generate season eight of Game of Thrones.

However, Mostaque was way over his head. “I was brand-new to this. With my Aspergers and ADHD, I was like, what’s going on? Mostaque talks fast, his tone matter-of-fact: On the research side, we did really good things. The other side I was not so good at, which was the management side,” said a former employee. Mostaque didn’t think deeply about building a marketable product. His fascination was with building AI models.

“The company’s success brought heightened scrutiny—particularly around how the models were built. Like many text-to-image models, Stable Diffusion 1.5 was trained on LAION-5B, an open-source dataset linked to 5.8 billion images scraped from the web, including child sexual exploitation material and copyrighted work. In January 2023, Getty Images sued Stability AI in London’s High Court for allegedly training its models on 12 million proprietary photographs. The company filed a similar suit in the US weeks later. In the stateside complaint, Getty accused the AI firm of brazen theft and freeriding,” Schiffer said.

In June 2023, Forbes published a story alleging that Mostaque had inflated his credentials and misrepresented the business in pitch decks to his investors. The article also claimed that Mostaque had received only a bachelor’s degree from Oxford, not a master’s. What’s more, Stability AI reportedly owed millions of dollars to Amazon Web Services, which provided the computing power for its model. Though Mostaque had spoken of a partnership, Stability AI’s spokesperson acknowledged to Forbes that it was, in fact, a run-of-the-mill cloud services agreement with a standard discount.

And the article resulted in investors losing confidence. VCs from both Coatue and Lightspeed left the board of directors, followed by the departures of the company’s head of research, chief operating officer, general counsel, head of human resources and the prominent researchers. Mostaque finally left the company on March 22, 2024, just a few weeks after Lady Gaga’s greenhouse soiree.

Akkaraju and Parker saw opportunity

Akkaraju and Parker joined Stability AI, taking over as CEO and chairman of the company’s board. However, industry competition was fiercer, with another startup, Runway, signing the AI industry’s first big deal with a movie studio. Runway would get access to Lionsgate’s proprietary catalogue of movies as training data and develop tools for the studio.

Early on in his tenure, Akkaraju decided that Stability AI would no longer compete with OpenAI and Google on building frontier models. Instead, it would create apps that sat on top of those models, thereby freeing the company from enormous computing costs.

Akkaraju negotiated a new deal with Stability AI’s cloud computing vendors, wiping away the company’s massive debt. Asked for specifics on how this came about, Akkaraju, through a spokesperson, demurred. Investors, however, came flocking back.

Whereas Mostaque painted a picture of AI solving the world’s most difficult problems, Akkaraju is building Stability AI as a software-as-a-service company for Hollywood. The goal is not to generate films, but to use AI to augment the tools that filmmakers already use.

“I really do think that our differentiation is having the creator in the centre. I don’t see any other AI company that has James Cameron on its board,” Akkaraju said.

Yes, the same legendary director, who was leading Hollywood’s charge against the technology. He didn’t appreciate the premise of the streaming platform, the “Screening Room,” which let people watch new releases at home for $50 on the same day they came out in theatres.

Cameron reportedly told a crowd at CinemaCon that he was “committed to the theatre experience.” In the years that followed, none of the major studios publicly announced deals with the Screening Room, and in 2020, the company rebranded as SR Labs.

That same year, Akkaraju and Parker took over Weta Digital, the visual effects studio behind blockbusters such as The Lord of the Rings, Game of Thrones, and Cameron’s Avatar movies. Weta developed virtual cameras that let Cameron see a real-time rendering of the artificial environment through a viewfinder, as if he were filming on location in the fictional world of Pandora.

Then came the meeting between Cameron, Akkaraju, and Parker over dinner, where they discussed how technology was changing the film industry. “The tequila was flowing. A friendship formed. Any tension that had existed over the Screening Room melted away,” Cameron recalled.

“I never really talked with him about it. He knew, and I knew. It was very funny,” Akkaraju told WIRED, while continuing, “So Cameron is on the board, but is the creator in the centre? When I spoke with Parker, he emphasised the importance of using open-source models and spoke of respect for creators and respect for IP. That sounds potentially kind of rich, coming from me, given my past association with Napster and early social media. But it is a lesson learnt.”

Challenges lie ahead

In June 2025, the company scored a major win when Getty dropped its copyright infringement claims from a broader lawsuit as the trial neared a close in the United Kingdom. The US trial is ongoing.

Akkaraju said the company “sources data from publicly available and licensed datasets for training and fine-tuning,” and that when “creating solutions for a client”, it “fine-tunes using the dataset provided by the client.”

When Schiffer asked Akkaraju if the company trained exclusively on licensed data, he responded, “Well, that’s the majority of what we’re using, for sure.”

Even those who are bullish on AI admit that, for the most part, the technology isn’t ready for the big screen. Text-to-image generators might work for marketing agencies, but they often lack the quality required for a feature film.

“I worked on one film for Netflix and tried to use a single shot. The AI-generated footage got bounced back from quality control because it wasn’t 4K resolution,” said an anonymous filmmaker, not wanting to discuss the use of AI publicly.

Another problem with Stability AI’s solution is the issue with consistency. Filmmakers need to be able to tweak a scene in minute ways, but that’s not possible with most of the image and video generators on the market. Enter the same prompt into a chatbot 10 times, and you will likely get 10 different responses.

“That doesn’t work at all in a VFX workflow. We need higher resolution; we need higher repeatability. We need controllability at levels that aren’t quite there yet,” Cameron noted.

“That hasn’t stopped filmmakers from experimenting. Almost every person I spoke with for this story said that AI is already a core part of the previz process, where scenes are mapped out before a shoot. The process can create new inefficiencies,” Schiffer remarked.

“The inefficiency in the old system was really the information gap between what I see and what I imagine I want moving forward. With AI, the inefficiency becomes ‘Here’s a version, here’s another version, here’s another version,” said Luisa Huang, cofounder of Toonstar, a tech-forward animation company.

One of the first people in Hollywood to admit to using generative AI in the final frame is Jon Irwin, the director and producer of Amazon’s biblical epic House of David. He became interested in the technology while shooting the first season of the show in Greece.

“I noticed that my production designer was able to visualise ideas almost in real time. I was like, tell me exactly how you’re doing what you’re doing. What are you using, magician?” he recalled.

Irwin started playing around with the tools himself and ended up making a presentation for Amazon outlining how he wanted to use generative AI in his production. The company was supportive.

“We film everything we can for real—it still takes hundreds of people. But we’re able to do it at about a third of the budget of some of these bigger shows in our same genre, and we’re able to do it twice as fast,” he told WIRED.

A burning-forest scene in “House of David” (historical web series depicting the rise of the biblical figure David) would have been too expensive to do with practical effects. So, AI stepped in.

Despite Irwin showing interest in Stability AI’s tools, he has not been able to use the solutions successfully on a show at scale. Schiffer believes that Stability AI’s text-to-image generators need to cross a few hard yards before Hollywood starts using them professionally.

However, Stability AI can take solace in the fact that the taboo on studios acknowledging their embrace of AI seems to be softening. In July 2025, Netflix co-CEO Ted Sarandos told investors the company had allowed “gen AI final footage” to appear in one of its original series for the first time. He said the decision sped up production tenfold and dramatically cut costs.

Hanno Basse, Stability AI’s chief technology officer, showed Schiffer an image of his backyard in Los Angeles: a grassy lawn surrounded by high hedges, rose bushes crowding a bay window, and a tree in the far left-hand corner. Suddenly, the 2D image unfurled into 3D.

A generative AI model has filled in the gaps, estimating depth (how far away the hedge is from the rose bush, the tree from the window) and other missing elements to make the scene feel immersive. Basse can replicate camera moves by selecting from a drop-down menu: zoom in or out, pan up or pan down, or spiral.

“Instead of spending hours or days or weeks building a virtual environment and rehearsing your shots, the idea here is actually that you can just take a single image and generate a concept,” Basse says.

However, the company admits that its offerings are still in their early days and need perfection.

“I hear artists at VFX companies say, Hey, I don’t want to get replaced. Of course, you don’t want to get replaced! If you guys are going to lose your jobs, you’re going to lose your jobs over the work drying up versus getting bumped aside by these GenAI models,” Cameron said.

Akkaraju and Parker, too, believe that as movies become cheaper to produce, more films will get made and overall employment will rise.

The AI revolution is here and already transforming Hollywood. That collapsing building, that burning forest, that crowd of people the audience sees when streaming a show or going to the movie theatre will be created using a keyboard. Technology will be a creator’s ally to give a project the “larger than life” elevation it deserves, without hurting the purse much.

The post Stability AI rewrites Hollywood’s rulebook appeared first on International Finance.

How does banking sector adopt AI?

Banks have accelerated their AI research and use cases due to the rise of ChatGPT. Legacy institutions are also facing the heat from the fintechs, which are deploying state-of-the-art AI-backed models to take care of functions like customer service, fraud detection, and automation of repetitive tasks. As pioneers in the digital revolution, fintech companies were among the earliest adopters of AI to support financial services and operations.

Now, feeling the heat from these new players, legacy financial institutions like JPMorgan Chase, Bank of America, and Goldman Sachs are going ahead with the technology to reduce costs, boost efficiency, compliance, personalised service, predictive analytics, and increased competitive advantages.

Technologies like machine learning, data analysis, natural language processing (NLP), and computer vision are now widely used in the financial sector when it comes to understanding the financial behaviour and requirements of customers before offering them tailored products.

Talking about banking industry biggies adopting AI in their operations, Morgan Stanley in June 2024 launched its “AI @ Morgan Stanley” suite of GenAI tools for Financial Advisors (FAs). The OpenAI-powered tool, with client consent, generates notes on a Financial Advisor’s behalf in client meetings and surfaces action items. After the meeting, it summarises key points, creates an email for a Financial Advisor to edit and send at their discretion, and saves a note in Salesforce.

Another Wall Street biggie, Goldman Sachs, has taken a measured approach in adopting AI. As of March 2025, half of the 46,000 employees at the investment banking giant have access to the technology. The firm is currently experimenting with agentic AI, which has yet to be deployed across the firm, despite the apparent benefits in the automation of key tasks such as compliance checks or the processing of customer transactions.

Broader use of generative AI within the company came with the launch of GS AI Assistant, which rolled out in 2024 and has been expanded to 10,000 employees, including bankers, traders, and asset managers. This tool, which Goldman Sachs anticipates will be available to nearly all employees by the end of 2025, can summarise documents, draft emails, analyse data, and create personalised content.

However, it is JPMorgan Chase that has been aggressive in adopting AI in its operations. The bank has taken a top-down approach to adoption, adding Chief Data and Analytics Officer Teresa Heitsenrether to its technology leadership team in June 2023 and putting an AI assistant called LLM Suite in the hands of 140,000 employees by October 2024. The company also rolled out ChatCFO, a generative AI tool for finance teams, apart from implementing prompt engineering training for new hires.

Now, a very compelling question: Can banks use ChatGPT or, in that sense, any other AI chatbot as a personal finance advisor?

Discussing the possibility

In 2024, two universities in the United States analysed more than 10,000 responses to financial exam questions from large language models such as ChatGPT and Bard. They found that AI is not likely to replace human advisors any time soon.

However, the Washington State University and Clemson University study asked the AI tools to provide the reasons behind the answers and compared the responses with those from human advisors. While they found that two versions of ChatGPT performed the best (particularly 4.0, a paid version), there was inaccuracy when topics were more advanced.

The AI responses were best for questions around securities transaction reviews and monitoring market trends, but it struggled with areas such as client insurance coverage or tax status.

Study author DJ Fairhurst of WSU’s Carson College of Business, said, “It’s far too early to be worried about ChatGPT taking finance jobs completely. For broad concepts where there have been good explanations on the internet for a long time, ChatGPT can do a very good job at synthesising those concepts. If it’s a specific, idiosyncratic issue, it’s really going to struggle.”

To prove their point, Fairhurst and co-author Daniel Greene of Clemson University used questions from licensing exams, including the Securities Industry Essentials exam, as well as the Series 6, 7, 65, and 66.

To move beyond the AI models’ ability to simply pick the right answer, the researchers asked the models to provide written explanations apart from choosing questions based on specific job tasks, financial professionals might actually perform.

Of all the models, the paid version of ChatGPT, version 4.0, performed the best, providing answers that were the most similar to human experts. Its accuracy was also 18% to 28% higher than the other models. However, things changed when the researchers fine-tuned the earlier, free version of ChatGPT 3.5 by feeding it examples of correct responses and explanations. After this tuning, the AI model came close to ChatGPT 4.0 in accuracy and even surpassed it in providing answers that were similar to those of human professionals.

“Both models still fell short, though, when it came to certain types of questions. While they did well reviewing securities transactions and monitoring financial market trends, the models gave more inaccurate answers for specialised situations such as determining clients’ insurance coverage and tax status. Fairhurst and Greene, along with WSU doctoral student Adam Bozman, are now working on other ways to determine what ChatGPT can and cannot do with a project that asks it to evaluate potential merger deals. For this, they are taking advantage of the fact that ChatGPT is trained on data up until September 2021 and using deals made after that date, where the result is known. Preliminary findings are showing that so far, the AI model isn’t very good at this task,” reported SciTechDaily in December 2024.

The researchers’ final verdict was that ChatGPT can be better used as a tool to assist rather than as a replacement for an established financial professional. On the other hand, AI may change the way some investment banks employ entry-level analysts, as Fairhurst said, “The practice of bringing a bunch of people on as junior analysts, letting them compete and keeping the winners – that becomes a lot more costly. So, it may mean a downturn in those types of jobs, but it’s not because ChatGPT is better than the analysts, it’s because we’ve been asking junior analysts to do tasks that are more menial.”

Weighing on the topic, Oliver Hackel, Senior Investment Strategist at Kaiser Partner Privatbank AG, said, “In any case, AI certainly doesn’t lack self-confidence, not even when it comes to crafting the right wording. This is demonstrated impressively when the chatbot is asked how Donald Trump would explain Bitcoin. You can hardly get the voice of the former US president out of your head afterwards. But are ChatGPTs from the US-based artificial intelligence research firm OpenAI or its numerous kin also suitable to act as investment advisors? Our virtual mystery shopping tour revealed that chatbots still lack the necessary financial education. Moreover, even more powerful generative language model versions in the future will not be capable of replacing intimate conversations between clients and advisors.”

First-hand experiences

Andrew Lo, director of the Laboratory for Financial Engineering at the MIT Sloan School of Management, sees LLMs (Large Language Models) like ChatGPT as “glorified search engines” that will excel in helping their users to find information fast, apart from being a good source of general advice on how to set up a budget or improve credit score. However, getting accurate answers on specific, sensitive financial questions is where the concerns start.

“Many AI platforms lack domain-specific expertise, trustworthiness, and regulatory knowledge, especially when it comes to providing sensitive financial advice. They might even lead individuals to make unwise investments or financial decisions,” Lo warned.

Still, as per an October 2024 Experian study, many Americans are already turning to AI chatbots for financial management help, and among the 47% who reported doing or considering the practice, 96% have a positive experience.

However, a new study from the broker analysis site, Investing in the Web, found that tools like ChatGPT might not be very good at the job. To prove their point, researchers asked ChatGPT 100 questions related to finance and then had the answers reviewed by industry experts at their company. In the report, AI responded to 35% of financial queries incorrectly, with one in three answers being hallucinated on questions centred on finances and investments.

In response to questions like “How [do I] save for my child’s education?” “How does the average pension compare to the average salary?” and “What are the pros and cons of investing in gold?” the chatbot answered 65% correctly, while 29% were labelled incomplete or misleading, and 6% were found to be completely incorrect.

Pedro Braz, CEO of Investing in the Web, said in a statement that it’s important to cross-check the sources (of the answers generated from an AI tool), especially with financial information that relies on timely data that is subject to change, such as interest rates and daily stock performance.

“ChatGPT has well-recognised issues with up-to-date information. It is best to go to the very source of the information, rather than asking AI chatbots for financial data,” he added.

As Hackel entered OpenAI’s virtual office and asked his first question regarding a suitable investment strategy, the chatbot started out by alerting him that it was not a certified investment consultant and could not give specific investment recommendations.

“But as is the case with so many other subjects, ChatGPT quickly sheds its restraint when we chat about a hypothetical example. Our query asks ChatGPT to construct for an investor with a moderate risk appetite a multiasset portfolio composed of 15 to 20 ETFs that outperforms a simple 50/50 portfolio of stocks and bonds over the long term. Within seconds, the advisory bot recommends a mix of low-correlated asset classes. Stocks, bonds, commodities, and alternative assets are just the ticket, the bot says, and it names corresponding ETFs,” he noted.

After a few more follow-up questions, Oliver Hackel and his team ended up with a portfolio of 25 ETFs that also incorporates small and midcaps, sector-based, factor-based, and thematic strategies as well as exposure to international markets alongside the United States in its equity component.

“The original portfolio also becomes broader and more diversified in its fixed-income component and its allocation to alternative assets in the course of the client advisory conversation. However, the electronic advisor seems a little overwhelmed by a sophisticated client like ourselves,” he noted.

The limits of AI in finance

While stating that AI-powered tools like Perplexity and ChatGPT can help people who are looking for advice on saving and budgeting, investment planning, and credit score improvement, Christina Roman, consumer education and advocacy manager at Experian, terms the technology a great starting point for consumers who otherwise might not be able to afford professional financial advice.

“I don’t think that this is going to make people reliant on AI for these types of services, but I think it’s a great tool that can help them to navigate their financial lives and to understand complex topics like investing and whatnot,” Roman said.

While each prompting experience will differ, an individual can provide relatively simple details about their financial situation, and generative AI can produce a fairly elaborate plan. It has to be well-crafted, something like this: “I need help managing my money. I make $50,000 a year. I have $10,000 in credit card debt on one credit card and $2,500 in debt on another credit card. My rent each month is $750. My car payment each month is $450. I have $150 in other utility expenses. I only have $250 set aside in my emergency fund. Can you help me get on track?”

When Fortune.com put these prompts inside ChatGPT, the AI tool provided separate sections like “Budgeting with a 50/30/20 Rule (Customised for You), including a monthly income estimate,” “Spending Breakdown,” “Debt Repayment Strategy: Snowball or Avalanche,” “Emergency Fund Goal,” “Budget Adjustments” and “Automate Payments and Savings and Example Action Plan for Next Month.”

So, using well-crafted prompts will become the guardrail here, apart from asking AI follow-up questions and adding more details about your financial situation and goals. It will only help the platform understand your unique circumstances and offer resourceful information.

However, Roman advises that people be very cautious with the output. AI platforms hallucinate, and that means the advice they offer may not be grounded in best practices, or even in any sound personal finance reality.

Furthermore, she advises being generic about the information they provide to any generative AI platform since the user may not realise the way his or her information will be saved or used to train the AI model itself.

Generative AI platforms are innovating by the minute, and there is no question that the ChatGPT of 2025 is more accurate and detailed than the version which came out a couple of years ago. But that is exactly what worries some financial experts. Hallucinations can be hidden in plain sight, and individuals without financial expertise or experience may not know the difference.

In Andrew Lo’s recent research paper on using generative AI for financial advice, he cited an example where ChatGPT 3.5 made up the author names for a paper it used to back up its responses. While this may not seem like a serious offence, when it comes to statements that involve financial risk, hallucinations could ruin someone’s finances.

“AI platforms may not always disclose as much source or background information as you might want or need. For example, when asking ChatGPT for investment advice, it recommends investing in companies like Microsoft. While human financial advisors may do the same, everyday users may not realise that Microsoft has invested over $13 billion in OpenAI, the parent company of ChatGPT. The chatbot does not note the conflict of interest to users unless it is pointed out,” Lo suggested.

Future of AI in financial services

Working with a human financial advisor allows for more conversation-based financial planning. Details about one’s financial status and goals can be discussed further to create a personalised plan that includes an understanding of all risks tied to potential money moves.

Generative AI will provide financial advice with just a few minor details, and individuals who take that insight without thinking (or even double-checking things) about their full financial picture could make costly mistakes.

Yes, LLMs are becoming more advanced, and one can imagine a future where generative AI is much more integrated into the financial advising ecosystem.

However, Michael Donnelly, the interim managing director of corporate growth at the CFP Board, says financial professionals may be more capable than others in making a strong case that technology cannot replace human advice. He engaged in a similar conversation a decade ago, during the rise of robo-advisors.

Donnelly advocates that financial advisors learn to accept AI as a tool that is great for things like internal practice management, as the technology will save advisors time better devoted to strengthening personal relationships, a hallmark of the financial planning profession.

“For consumers, AI won’t eliminate the need to work with a human financial planner,” Donnelly said, though Lo expressed concerns for those without access to dedicated financial advisors.

“We don’t have any guardrails yet in terms of how large language models are able to provide advice to consumers. And I think that on the regulatory front, we do need to have more careful guardrails, but on the research front, it really opens up a whole new set of vistas for us to explore,” Lo added.

Lo equated the situation to the fact that consumers largely have access to lower-risk mutual funds or money market accounts, but there are much greater regulations when it comes to who can deal with riskier private equity or hedge fund investments. AI, largely, has no guardrails.

He recommends a three-pronged approach to making AI’s role in finance safer. First, investors must be aware of AI’s tendency to hallucinate and ensure that any response generated by tools like ChatGPT is thoroughly fact-checked. Second, financial institutions adopting AI as a personal finance advisor must build safeguards to detect abuse and misuse. Finally, strong regulatory frameworks must take precedence to guide responsible use.

The post Will ChatGPT be the new private banker? appeared first on International Finance.

The reason for this could be that generative AI was first marketed as a productivity tool, which made it closely linked to workforce and cost reductions. Recognising the danger, 42% of workers polled in 2024 expressed concern that their position might disappear within the next ten years.

It makes sense that there would be more opposition than excitement if there was no training or upskilling to fully utilise the technology. An “immune response” may occur in organisations, where managers and staff alike oppose change and seek explanations for why AI “won’t work” for them, much like antibodies fending off a foreign body.

One possible explanation for this could be that generative AI was initially promoted as a tool for increasing productivity, which strongly associated it with workforce and cost savings. 42% of workers surveyed in 2024 acknowledged the risk and voiced worry that their job might be eliminated in the ensuing decade.

“If there was no training or upskilling to make the most of the technology, it makes sense that there would be more resistance than enthusiasm. In organisations, managers and employees may have an immune response in which they resist change and look for reasons why artificial intelligence won’t work for them, like how antibodies protect against an alien invader,” say Vinciane Beauchene, Managing Director and Partner at BCG (where she serves as Global Lead on Human x AI) and Allison Bailey, a senior partner and Managing Director at BCG (where she serves as Global Vice Chair for People and Organisation Practise).

However, study reveals that regular users of generative AI can already save five hours per workweek, which they can use to pursue new projects, continue experimenting with the technology, work with colleagues in novel ways, or just finish earlier. Therefore, the task for company executives is to highlight these possible advantages and offer advice on where to reallocate one’s time to optimise value creation.

“A global healthcare provider recently implemented generative AI for all 100,000 of its employees. For all employees to benefit from the technology, it developed a scalable AI learning programme with three goals: compliant usage; a wide range of artificial intelligence tools for all work scenarios; and high AI literacy throughout the company. Because of this all-encompassing strategy, the business quickly increased both employee satisfaction and productivity,” Beauchene and Bailey stated.

However, adopting artificial intelligence is about more than just saving time. It’s about reimagining work for the good of the company and its workers. Businesses that view generative AI as a time-saving tool are more likely to pursue piecemeal use cases, such as “10 minutes saved here, 30 minutes saved there,” which won’t have a significant effect on the company’s overall operations. Small-scale AI applications that result in diffuse productivity gains are, after all, challenging to reinvest in or record on a profit and loss statement.

Instead of radically enhancing the way work is done, organisations run the risk of optimising discrete tasks if they don’t have a comprehensive plan to restructure their core processes around artificial intelligence.

Too frequently, the outcome is that bottlenecks are merely moved to different stages of the value chain or process, which reduces overall productivity increases. In software development, for instance, an AI that expedites coding may result in more difficult debugging or other delays, offsetting any efficiency gains.

Using AI throughout the whole development lifecycle yields real benefits. An even more significant problem is brought up by this example: Too many businesses aim for scale without first rethinking the workflows and structures required to capitalise on cumulative gains.

The usual outcome is a lost chance since time savings that are not carefully reinvested eventually evaporate. Companies should pursue a few major transformational projects aimed at reimagining work for people instead of taking a let-a-hundred-flowers-bloom approach.

To realise the “golden triangle” of value—productivity, quality, and engagement/joy—generative AI holds great promise. Rethinking workflows to remove inefficiencies, enhancing decision-making and processes to promote creativity and innovation, and improving work rather than automating it are all important components of an AI strategy.

Artificial intelligence is more likely to be enthusiastically embraced by workers when it reduces monotony, stimulates creativity, and speeds up learning. When upskilling is properly addressed, technology will enhance human potential, increasing job satisfaction and workplace engagement.

By prioritising engagement and experience quality in addition to productivity, organisations can shift from a cost-driven viewpoint to one that adds greater value for the company, its workers, and its clients. If businesses implement a thorough plan for implementing AI, it can be much more than just an automation tool.

There are five imperatives that company executives should remember. Prioritising the largest value pools with the most clearly defined business cases for incorporating artificial intelligence is the first step. The second is not just optimising work, but reimagining it. AI should not only automate a few steps but also completely change workflows.

Third, managers need to spend money on upskilling so that everyone is aware of the capabilities of the technology. Fourth, the golden triangle should be the golden rule for businesses because it strikes a balance between quality, productivity, and employee happiness.

Finally, companies ought to gauge value in ways other than cost reductions. The most successful companies using generative AI will monitor how it affects not only operating expenses but also employee empowerment, agility, and new revenue streams.

By following these guidelines, businesses can use artificial intelligence as a tool for innovation rather than merely increasing productivity. They will also set the standard for the upcoming business era in the process.

The post IF Insights: How companies should & should not deploy artificial intelligence appeared first on International Finance.

Its outcome, the EU AI Act, is the first major piece of legislation controlling AI, including ‘generative AI’ chatbots, which have become the Internet’s new craze since ChatGPT’s inception in late 2022.

Two days later, French startup Mistral AI unveiled Mixtral 8x7B, a new large language model (LLM) for generative AI. Its unique setup of eight expert models makes it better than proprietary alternatives despite being smaller. Worse, the Act’s harsher regulations do not apply to its open-source code, presenting regulators with new issues.

Mixtral’s disruptive potential exemplifies policymakers’ struggles to rein in AI. Tech companies believe self-regulation is the answer. Given their inclination to prematurely enforce restrictive laws, former Google CEO Eric Schmidt believes governments should leave AI regulation to tech corporations.

How to control something that changes so fast is a question for most policymakers.

Setting EU law

The first attempt to answer that question is the AI Act, which will take effect in May 2025. Given the bloc’s regulatory powerhouse status, it intends to develop a European and possibly worldwide regulatory framework by encompassing practically all AI applications.

RPC partner Helen Armstrong said, “Large, multi-jurisdictional businesses may find it more efficient to comply with EU standards across their global operations on the assumption that they will probably substantially meet other countries’ standards.”

It is also the first attempt to handle foundation models, or General Purpose AI models (GPAI), which power AI systems.

All models must be horizontally compliant, including detecting AI-generated content, or face fines of up to 7% of the miscreant’s global revenue. How do you control rapid change? The Act tiers risk and responsibility for activities and AI models.

GPAIs with systemic risk must undergo rigorous reviews, incident reporting, and advanced cybersecurity procedures, including ‘red teaming,’ a simulated hacker attack. It’s called “systemic risk” because of two main factors: the amount of computation used to train the model (more than 10^25 “floating point operations”), which shows how big the industry is; and the model having more than 10,000 business users in the EU.

It appears only ChatGPT-4 and probably Google’s Gemini fit these criteria. Not everyone finds these criteria effective. Nigel Cannings, the founder of Intelligent Voice, stated that some high-capacity models may be relatively benign, while others may use lower-capacity models in high-risk contexts. The computing criterion may encourage developers to find workarounds that technically meet the threshold without reducing risks.

Cutting data requirements to achieve favourable outcomes is the goal of current AI research. Patrick Bangert, a data and AI expert at Searce, a technology consulting firm, said, “Classifying models by the amount of compute they require is only a short-term solution. These efforts are likely to break the compute barrier in the medium term, thus making this regulation void.”

The Act’s final draft was fiercely negotiated. France, Germany, and Italy initially resisted binding foundation model legislation, fearing it would hurt their startups. The Commission proposed horizontal regulations for all models and codes of practice for the most powerful as a compromise.

“There was a feeling that a lower threshold could hinder foundation model development by European companies, which were training smaller models at the time,” said Philipp Hacker, an AI regulation expert at the European New School of Digital Studies.

Hacker argued that this was entirely incorrect, as the rules only codify the bare minimum of industry practices—even falling short by some measures. Domino Data Lab AI expert Kjell Carlsson said, “We chose the threshold after extensive lobbying, resulting in an imperfect outcome. Others think the Act is too broad. It’s far more effective to regulate use cases instead of the general technologies that underpin them.”

Many European startups and SMEs say the limitations could hurt them compared to competitors.

The Future Society, an AI governance think tank, discovered that foundation model suppliers that invest much in training data—1% of their development costs—find compliance easier. Sceptics argue that this solution serves as an additional barrier to the EU’s regulatory framework, hindering innovation in an area where Europe desperately needs success stories.

Compared to the United States and China, the EU has created few AI unicorns and lagged in research. Nicolai Tangen, head of Norway’s $1.6 trillion sovereign wealth fund, which uses AI in its investment decision-making, has publicly criticised the EU’s approach: “He said, “I am not saying it is good, but in America, you have a lot of AI and no regulation; in Europe, you have no AI and a lot of regulation.”

European firms face a fragmented market, stricter data protection regulations, and difficulty retaining AI professionals. Hacker says the Act’s unjustified “bad reputation” may make things worse.

“It is not particularly stringent, but there has been a lot of negative coverage, and many investors, especially from the international venture capital (VC) scene, treat the Act as an additional risk. This will hinder European unicorns’ fundraising,” he said.

Some disagree with this assessment. The Act’s rules require VCs to add a new criterion to their scorecard: Is the company building a model or product that is and will remain EU compliant?

Dan Shellard, partner at Paris-based venture finance firm Breega, said regulation might offer regtech opportunities. Some believe it will boost innovation.

Chris Pedder, Chief Data Scientist at AI-powered edtech firm Obrizum, said forcing corporations to be more open and responsible will certainly spur innovation.

The special installation of fans’ recreations of Johannes Vermeer’s ‘Girl with a Pearl Earring’ includes Julian van Dieken’s AI-powered piece. Another issue is that technology is evolving faster than legislation. The Act doesn’t regulate open-source models like Mixtral 8x7B unless they pose a systemic risk. Making them public aims to increase transparency and accessibility, but it also poses significant safety risks.

Open-source models offer a broader range of computational capabilities, allowing many users to utilise local computing resources instead of expensive cloud-based ones.

Iain Swaine of BioCatch, a digital fraud detection startup, noted that in a decentralised system, it becomes easier to create malware, phishing sites, and deepfakes.

America is divided

The United States is behind in regulation despite its commercial AI dominance. Multiple federal agencies regulate AI, creating a fragmented regulatory framework. An executive order requires federal agencies to investigate AI usage, require AI system developers to assure ‘safe, secure, and trustworthy’ systems and share safety test results with the US government.

Donald Trump has vowed to reverse it, but it may fail without Republican support in Congress. Congress’ bipartisan AI task force has yielded little. Due to partisanship, any compromise before the November elections is improbable. Since American governments value innovation and economic progress, we project US regulation to be less severe than European regulation. Europe has no AI and lots of regulation, while America has lots of AI and little regulation.

Morgan, Lewis & Bockius partner David Plotinsky said, “AI will be an area in which both Congress and the executive branch take a very incremental approach to regulating AI—including by first applying existing regulatory frameworks to AI rather than developing entirely new frameworks.”

States could potentially fill this void. He said the risk is a “patchwork of regulations that may overlap in some areas and also conflict in others.” Apocalyptic predictions that an omnipotent AI may threaten humanity inform the debate. Some, like Elon Musk, want AI development stopped. However, mundane matters seem more urgent. The advent of monopolies, especially in generative AI, is a serious concern, but multiple ChatGPT competitors have allayed concerns that OpenAI, the business behind ChatGPT, will monopolise.

‘Our Planet Powered by AI’ author Mark Minevich said, “The industry’s high barriers to entry, such as the need for enormous data and computational power, mean that only a few huge incumbents, such as top big tech companies, could dominate.”

As AI becomes a flashpoint in the United States-China relationship, policymakers are also concerned about how legislation affects US competitiveness. In another executive order, US President Joe Biden ordered the Treasury to prohibit outbound AI investment in countries of concern and to review AI technologies for security vulnerabilities.

Plotinsky, acting chief of the US Department of Justice’s Foreign Investment Review Section, predicted that Washington would need to adopt a risk-based approach to foundation models. He also said that any risk-based approach would have to consider whether the foundation model was created in the United States or another trusted country, as well as what controls and other safety measures might be needed to keep China’s potentially powerful goals from causing concerns. National AI leadership is the government’s goal for 2030, with substantial funding.

China produces most AI research

Its Global AI Governance Initiative, which includes creating a new international AI governance organisation, shows its desire to influence global regulation. The initiative also urges “opposing drawing ideological lines or forming exclusive groups to obstruct other countries from developing AI,” a reference to US legislation restricting US investment in China’s AI business.

According to Wendy Chang, a technology analyst at the Mercator Institute for China Studies, China aspires to participate in international forums and influence the global development of AI regulation.

Domestically, Beijing’s tightly managed censorship regime needs to be maintained, often openly, by requiring generated text content to ‘reflect communist basic values.’ The EU launched a global AI standards race. Although the government encourages Chinese enterprises to build Gen AI tools to compete internationally, these beliefs may hinder China’s AI leadership. Baidu and Alibaba unveiled their AI-powered chatbots last year.

The country’s early generative AI standards required developers to verify the ‘truth, correctness, objectivity, and diversity’ of training data, a high standard for models trained on online content. Recent regulatory changes allow Chinese enterprises to ‘elevate the quality’ and ‘strengthen truthfulness’ instead of ensuring training data honesty, but hurdles remain.

One working group suggested a proportion of model-rejectable answers. Given chatbots’ potential to spread falsehoods, such regulations may require Chinese corporations to develop their models with restricted firewalled data. Chinese companies and citizens cannot use ChatGPT. After an AI tool criticised Mao Zedong, iFlytek’s founder apologised publicly. Chang said Beijing’s domestic information regulation is a major issue for AI developers.

Compliance would be difficult for tech companies, especially smaller ones, and may deter many from entering the field. We already see tech companies focusing on corporate solutions rather than public products, which the government desires.

A full AI law is due from the Chinese government, which has published specific AI regulations. The 2021 recommendation algorithm regulation was driven by concerns over their role in information dissemination, a perceived threat to political stability, and China’s concept of ‘cyber sovereignty.’ Importantly, the rule created a list of algorithms with “public opinion properties,” which means developers had to explain how their algorithms were trained and how they were used. It now covers AI models and training data, with the first LLMs passing these reviews released in August.

China’s internet authority recently issued guidelines for AI-produced deepfakes, and its deep synthesis regulation, finalised five days before ChatGPT’s debut, requires synthetically generated content to be labelled. Who owns this photo? Another rising battleground is foundation model data IP ownership.

Generative AI has stunned creative workers, prompting legal action and strikes in industries like Hollywood that were previously impervious to technological innovation. Many artists have sued generative AI platforms for creating unlicensed derivative works.

Stock image seller Getty Images sued image generation platform Stable Diffusion for copyright and trademark infringement. Financial authorities face new AI problems. Risk modelling, claims management, anti-money laundering, and fraud detection in finance increasingly use AI, posing serious hazards.

Trouble in the EU

In 2022, the Bank of England and FCA reported that 79% of UK financial services organisations used machine learning, with 14% deeming it essential. The “black box” problem, which involves algorithmic decision-making without transparency or accountability, is a major issue.

According to regulators, AI may increase systemic risks, including flash crashes, market manipulation by deepfakes, and convergent models causing digital cooperation. The industry has promised improved ‘explainability’ in how AI is used for decision-making, but this remains elusive, and regulators may fall victim to automation bias when overusing AI systems.

Scott Dawson from DECTA, a payment solutions provider, suggests that while transparency appears advantageous in theory, financial institutions often hide certain elements of their processes for legitimate reasons.

He cited fraud prevention as an example where more transparency about how financial services firms use AI systems could be counterproductive: “Telling the world what they are looking for would only make them less effective, leading to fraud.”

Another issue is algorithmic prejudice. AI in credit risk management can make loans harder to get or worsen their terms for marginalised groups. The EU’s planned Financial Data Access law, which allows financial institutions to exchange consumer data with third parties, may hurt vulnerable borrowers.

The EU AI Act classifies banks’ AI-based creditworthiness operations and life and health insurance pricing and risk assessments as high-risk activities, requiring them to comply with stricter regulations.

“New ethical challenges are triggering unintended biases, forcing the industry to reflect on the ethics of new models and think about evolving towards a new, common code of conduct for all financial institutions,” said Dun & Bradstreet head of banking and financial services, Sara de la Torre.

The platform’s proprietors responded by allowing artists to opt out and protect their IP.

Such legal action has raised the question of who owns AI-generated material—AI platforms, downstream providers, content creators, or users. Solutions include paying content creators, sharing revenue, and using open-source data.

EIP counsel Ellen Keenan-O’Malley said, “In the short term, I expect organisations to place greater reliance on contractual provisions, such as a broad intellectual property indemnity against third-party claims for infringement.”

Only the European Union has adopted a clear position; the AI Act requires model providers to take ‘adequate measures’ to safeguard copyright, including releasing full training data summaries and copyright rules. Synopsys data specialist Curtis Wilson said banning copyrighted photos for AI training will prevent AIs from mass-producing custom art.

However, the expert commented, “But it would also ban image classification AI that detects cancerous tumours.”

Europe and China want a piece of America’s tech superiority, making AI deployment geopolitical. Because AI models are growing so quickly and different approaches are used in different major economies, only bilateral agreements can work. For this reason, the tech industry thinks that global regulatory frameworks are too optimistic.

A recent Biden-Xi conference agreed to begin talks without specifics. Following a similar US-UK agreement to reduce regulatory divergence, the EU and US have agreed to strengthen AI-based technology cooperation, focusing on safety and governance.

Delivered during the first global AI summit in November at the United Kingdom’s Bletchley Park, the Bletchley Declaration called for international cooperation to mitigate AI concerns. Action has not yet followed. As politicians and tech businesses face the same headwinds that are fragmenting the global economy in an era of increasing deglobalization, unified AI regulation seems unlikely.

The EU has set the global AI standards with horizontal, and some say overly strict, rules for AI systems; the US, hampered by pre-election polarisation and the success of its AI firms, has taken a ‘wait-and-see’ approach that gives the tech industry a free hand; and China, as usual, censors domestically while trying to influence the global regulatory framework.

Morgan Wright, Chief Security Advisor at SentinelOne, an AI-powered cybersecurity platform, said, “The challenge going forward is not allowing China to dictate what standards are or promote policies regulating AI that favour them over everyone else.”

However, keeping up with technology is harder. If talkative chatbots surprised the world in 2022, the next waves of AI-powered innovation have left experts dumbfounded by their disruptive potential.

“The field is moving so fast, I am not sure that even venture capital firms not deeply immersed in the field for the last decade fully understand AI and its implications,” said Fluent Ventures founder Alexandre Lazarow.

According to Plotinsky from Morgan, Lewis & Bockius, regulators may be at a disadvantage.

He said, “The technology has evolved too rapidly for lawmakers and their staff to fully comprehend both the underlying technology and the related policy issues.”

The rapid growth of AI technology has created a complex challenge for regulators worldwide, with varying approaches emerging in the EU, US, and China. While the EU has implemented the AI Act to set global standards, its broad and stringent regulations have raised concerns among startups and investors. In contrast, the US takes a more cautious, innovation-driven stance, creating regulatory uncertainty. China, balancing innovation with tight censorship, seeks to influence global AI governance.

As AI technology advances quickly, international cooperation and adaptable regulatory frameworks are crucial. The future of AI regulation will likely hinge on finding a balance between fostering innovation and addressing the emerging risks of AI, with each region contributing its own approach to the global conversation.

The post EU AI Act: A struggle to keep up with tech appeared first on International Finance.

OIA Chairman Abdulsalam Al Murshidi has termed the move in line with the Authority’s strategy to invest in advanced technologies across diverse sectors. XAI ranks among the top five in its field, having achieved milestones such as the creation of a massive data centre in the United States and the launch of the upgraded Grok 2 platform.

The venture, in the coming days, aims to lead AI technology by understanding and analysing real-time data, a feature lacking in many competing models.

“The platform provides advanced tools and comprehensive analytics to help users explore and improve AI models, processing a wide range of visual data, including documents, charts, graphs, and photographs,” Al Murshidi added.

Speaking on the occasion, Elon Musk, through video conference, said that the partnership with XAI would yield results in developing superintelligent AI capable of solving numerous problems. The tech boss, whose social media platform X played a crucial role in ensuring Republican Donald Trump’s win in the recently concluded US Presidential Elections, also revealed that the start-up was nearing completion of training the ‘Grok 3’ model, which will be the smartest AI model in the world.

The AI company recently raised USD 6 billion in funding, with some 97 investors donating a minimum of USD 77,593 (as per the media reports). The new cash brings xAI’s total raised to USD 12 billion, adding to the USD 6 billion tranche the start-up raised in 2024. CNBC reported in November that xAI was aiming for a USD 50 billion valuation, double its valuation as of six months ago.

According to the Financial Times, only investors who’d backed xAI in its previous fundraising round were permitted to participate in this one. Reportedly, investors who helped finance Elon Musk’s Twitter acquisition were given access to up to 25% of xAI’s shares.

Elon Musk formed xAI in 2023. Soon after, the company released Grok, a flagship generative AI model that now powers a number of features on X, including a chatbot accessible to X Premium subscribers and free users in some regions.

He has projected Grok as an alternative to ChatGPT and other AI systems that the billionaire tech boss believes are “too woke.” He’s also referred to Grok as “maximally truth-seeking” and less biased than competing models. Grok has become an established feature in X (formerly Twitter). Due to an integration with the open image generator Flux, Grok can generate, and analyse images on X, apart from summarising news and trending events.

As per the reports, Grok will be further empowered to handle even more X functions in the future, from enhancing the micro-blogging platform’s search capabilities and account bios to helping with post analytics and reply settings.

As it takes on ChatGPT and other generative AI models, xAI launched an API in October 2024, allowing customers to build Grok into third-party apps, platforms, and services. There were reports about xAI preparing to release a standalone consumer app similar to OpenAI.

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