JPMorgan Chase & Co. has conducted a study on artificial intelligence (AI) agents capable of reallocating capital among stocks based on fluctuating market dynamics. According to a Bloomberg report published on July 10, all eight AI agents outperformed both the conventional 60/40 investment portfolio and the bank’s proprietary model that operates under a rules-based market regime.

In a note shared by strategists, including Thomas Salopek, on July 9, they emphasized that the findings stem from historical simulations and should not be interpreted as definitive evidence that AI can consistently surpass market performances.

This marks JPMorgan's inaugural effort to create an AI agent capable of recognizing different market regimes. The strategists expressed their optimism about the potential of AI agents but also voiced caution regarding complete reliance on these agents for asset allocation decisions.

The report from PYMNTS Intelligence, titled "Financial Services Pulls Ahead in the Enterprise AI Race," highlights that the financial services and insurance sectors are heavily investing in AI, integrating it into a wider range of operations compared to other industries. 

The incorporation of AI within financial services encompasses areas such as revenue recognition, credit scoring, and sales forecasting. The report noted, “These environments allow for verifiable outcomes that can be justified to regulators and traced through clear data pipelines.” Such conditions are conducive for AI utilization since the regulations are established, focusing mainly on safeguarding a firm’s financial assets, credit risk, and income streams.

In related news, Coinbase announced in June that users can now link their accounts with an AI agent, enabling the agent to manage trades, payments, and workflows for them. This feature, termed Coinbase for Agents, allows these agents to access comprehensive information about the users' financial situations and take actions on their behalf.

Similarly, Robinhood revealed in May that its customers could delegate certain activities to AI agents. This capability emerged following the launch of Agentic Trading and the Agentic Credit Card, which empower AI agents to perform trades and execute credit card transactions for customers automatically.

JPMorgan's AI Agents Outperform Conventional Investment Portfolios

A bright trading floor flooded with warm morning light: a sleek silver AI android in a sharp suit stands beside a human trader at a glass desk, both studying rising green line charts on dual monitors. The human points excitedly at the upward trend as the AI nods confidently. Behind them, the JPMorgan logo is emblazoned prominently on a backlit wall panel, conveying optimism and technological triumph.

JPMorgan AI Agents Beat Traditional Investment Portfolios in Historical Simulations - PYMNTS.com

- JPMorgan Chase tested eight AI agents that outperformed traditional investment strategies in simulations.  
- Results caution against assuming AI will consistently outperform markets based on historical data.  
- Financial services are increasingly adopting AI for enhanced revenue recognition, credit scoring, and forecasting.  

JPMorgan Chase & Co. tested artificial intelligence (AI) agents that allocate capital between stocks in response to changing market conditions, and it found that all eight agents beat both the traditional 60/40 portfolio and the bank’s own rules-based market regime model, Bloomberg reported Friday (July 10).

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The bank’s strategists led by Thomas Salopek shared the results in a Thursday (July 9) note, cautioning that the results are based on historical simulations and should not be seen as proof that AI can consistently outperform markets, according to the report.

This is JPMorgan’s first attempt to build an AI agent that can identify market regimes, the report said.

“We are enthusiastic about the possibilities of agentic AI, even as we are wary to hand off asset allocation decision-making to an agent,” the strategists wrote, per the report.

The PYMNTS Intelligence report “Financial Services Pulls Ahead in the Enterprise AI Race” found that financial services and insurance firms are going all in on AI, scaling the technology across more tasks than many other enterprise sectors.

The tasks in which the financial services sector has embedded AI include revenue recognition, credit scoring and sales forecasting, according to the report.

“These are environments where outcomes can be verified, defended to regulators and traced back through clean data pipelines,” the report said. “AI thrives here precisely because the rules are known. These are also, notably, tasks oriented toward protecting what a firm already has: its books, credit exposure and revenue pipeline.”

Coinbase announced in June that the users of its exchange can now connect their AI agent to their account so the agent can trade, pay and execute workflows on their behalf. The new Coinbase for Agents facilitates these connections and enables agents to get full context about the user’s financial life and act on the user’s behalf.

Robinhood announced in May that its customers can now turn some of their activity over to AI agents. This capability resulted from the company’s launch of its Agentic Trading and the Agentic Credit Card, which allows AI agents to make trades and credit card purchases on a customer’s behalf.

JPMorgan AI Agents Beat Traditional Investment Portfolios

This narrative is derived from a discussion with Chris Domanski, a 41-year-old business management and development director at Renau, a manufacturing plant located in California. Domanski resides in Thousand Oaks with his family. The following content has been edited for brevity and clarity.

According to Domanski, ChatGPT has significantly eased his workload by acting as a study companion for his children.

The motivation to utilize AI was ignited by the progress reports from his daughters, aged 8 and 6, which indicated that one was having difficulty with math while the other faced challenges in reading. While their performances weren't poor, they were not meeting the district's expectations.

Recognizing the need to support their educational growth, Domanski felt uncertain about his ability to effectively teach specific concepts, such as TouchMath, a unique math curriculum he was unfamiliar with.

In an innovative approach, he began taking photos of their assignments and using AI to clarify concepts. By leveraging ChatGPT's voice mode, he found that it could engage his daughters with quizzes, tailor questions to their needs, revisit misunderstood topics, and explain ideas using multiple methods—all without showing signs of frustration.

Initially, Domanski questioned whether the time spent setting up the AI assistance would be worthwhile. However, just five minutes into the process, his daughters were discussing "KPop Demon Hunters," prompting him to make some adjustments to their focus.

Through a mix of experimentation, the AI system became a valuable asset for their educational journey, improving their grades while saving time. Although he is uncertain how it will adapt to increasingly complex schoolwork, the current arrangement seems effective.

Domanski capitalizes on AI to ensure his daughters fully comprehend their study material.

With both girls preparing for weekly math and spelling exams, he routinely informs ChatGPT of their test requirements. By uploading images of their study sheets and instructing the AI to maintain focus amidst the inevitable distractions from his daughters, he crafts a structured learning environment.

He prompts ChatGPT with clear expectations like: "You are a tutor supporting grade schoolers with math and spelling studies. You will receive study guides and will continue to work with the students until they can confidently and correctly answer all questions."

To keep the lessons on target, he provides additional guidance, instructing ChatGPT to express interest in other topics but redirect the discussion back to the studies at hand. This ensures that if his daughters can quickly solve equations, such as determining 9+3, they’ll confidently record their answers during tests.

Chris Domanski works closely with both his children and ChatGPT to optimize their learning process.

Domanski remains present during these sessions to monitor both his daughters' responses and ChatGPT’s input, allowing him to promptly intervene if he notices any misconceptions.

AI has proven to be a resourceful tool in maintaining their attention, adjusting to their immediate thoughts, and repeating concepts multiple times without any feelings of frustration for the children. This adaptability has considerably reduced educational friction in their home.

Since introducing this approach, his daughters’ progress reports have improved from "slightly below satisfactory" to "above satisfactory" levels. While both have shown academic improvement, the real benefit lies in creating a more engaging and less confrontational study experience. Domanski feels more equipped to observe their challenges instead of solely administering questions from worksheets.

Additionally, he incorporates AI to digitize their school handouts into a more manageable calendar system.

Every Monday, Domanski reviews their take-home folders filled with school correspondence, scanning them to extract important dates, deadlines, and necessary supplies. ChatGPT processes this information and organizes it into actionable calendar entries, dramatically simplifying what would typically be a cumbersome task.

His prompt to the AI is straightforward: "You will receive images of take-home folders. Identify key dates and action items that require follow-up, then add relevant tasks to my calendar." ChatGPT efficiently exports these events as calendar files that Domanski can integrate into Outlook.

So far, ChatGPT has functioned reliably, eliminating the stress of last-minute project discoveries. The family now avoids late-night scrambles to prepare for assignments due the next day.

Lastly, the Domanski family enjoys the creativity that AI brings to their lives.

In their artistic endeavors, they often turn to ChatGPT for generating custom images for drawings. Initially asking for character mashups like Disney's Ariel and Elsa, they encountered copyright issues. However, after requesting more generic, open-source alternatives, they found a compromise everyone could enjoy.

Now, several times a week, both children create art featuring their preferred character combinations.

Ultimately, AI does not just create additional free time but rather enhances the quality of their existing time together. They can focus more on projects, artwork, games, or simply enjoying each other's company, minimizing distractions and the need to decipher instructions from scratch.

I created a voice-activated AI tutor for my kids to assist with spelling and math, freeing up time for more fun together.

A cozy living room bathed in warm morning sunlight: an adult sits on a soft couch holding a tablet showing colorful spelling letters and simple math equations, with gentle voice waveforms rising from the screen. Two small empty children’s chairs and backpacks sit nearby on a rug strewn with crayons and worksheets beside an open board game, hinting at playtime off-screen. The adult smiles, turning toward unseen laughter—natural light, warm tones, story of tech freeing up family fun.

I made my kids a voice-mode AI tutor to help them with spelling and math. It saves me time to have more fun with them. - Business Insider

- Chris Domanski uses ChatGPT to assist his daughters with studying math and reading.  
- AI helps manage school-related paperwork, organizing important dates and tasks efficiently.  
- Generative art prompts from ChatGPT encourage creativity, allowing for fun family drawing sessions.  

This as-told-to essay is based on a conversation with Chris Domanski, 41, the director of business management and development at Renau, a manufacturing facility in California. He and his family live in Thousand Oaks, California. This interview has been edited for length and clarity.

More than anything, ChatGPT has saved me time and drama by being a study partner for my kids.

What spurred this were the progress reports that my daughters, ages 8 and 6, got from school last year. I found out that one struggles with math, and one struggles with reading. Not that they're doing poorly, but maybe below where the district thought that they should be.

I knew we could try to spend more time on these areas. But at the end of the day, I thought I might not be the best person to explain a concept like TouchMath, a multi-sensory math curriculum, which I'd never heard of until I had to help the girls with homework.

Domanski's daughters, ages 8 and 6, use AI for homework help and customized drawing prompts. Chris Domanski

I decided to take a picture of their homework and have AI explain some concepts to them. Using voice mode, ChatGPT now quizzes them, varies the questions, revisits what they missed, and explains the same concept several different ways without becoming impatient.

Related video How AI takes a toll on the freelancers who train, test, and refine it

At first, I wasn't sure if the time it took to set this up was worth it. Five minutes in, they were talking to ChatGPT about "KPop Demon Hunters." I needed to revise it a little.

Through trial and error, it became something that really saved time and I think improved their grades. I'm not sure how it's going to look as things get more and more complicated with their schoolwork, but at least for now, it works well.

I programmed an AI tutor to make sure they truly understand the material

Domanski uploads his daughters' homework and has ChatGPT create custom quizzes to test their knowledge. Chris Domanski/ChatGPT

Both my daughters have weekly math and spelling tests at school.

In general, I tell ChatGPT what kind of test my daughters are preparing for, take a picture of the study sheet, and instruct it to stay focused — despite the inevitable interruptions by my daughters.

I prompt ChatGPT by saying something like: "You're a tutor helping grade schoolers study for math and spelling tests. You'll be given images of the study guides, and you'll work with the students until they can confidently and accurately answer all questions."

I also add some guardrails: "You will not be distracted if other topics come up. Express interest in those topics, make them relevant to what you're discussing, but keep it on the task at hand."

Many of the instructions are for ChatGPT to politely and firmly make sure my daughters are comfortable and confident in the material, not engaging just for the sake of engaging. If they're able to quickly say what 9+3 is, they're able to write that on a test.

An example of Domanski's instructions to ChatGPT. Christ Domanski/ChatGPT

I also make sure I'm present to hear both what they're saying and what ChatGPT is saying. If I hear that something is very clearly wrong, I can jump in and steer things back to the task at hand.

Overall, AI has been pretty efficient at maintaining their attention, adapting to how they are thinking in that moment, and repeating something for the sixth time without making it feel like punishment. It's surprisingly useful at absorbing some of that friction.

Since last year, their progress reports went from being a little "below satisfactory" to being "above satisfactory." Both of their grades have improved, but the more valuable result may be that studying has become more interactive and less adversarial. I am still involved, but I can spend more time observing where they are struggling instead of simply reading questions from a worksheet.

I also use AI to digitize their school handouts into a calendar

Domanski uses ChatGPT to scan paper handouts and transfer the info to his Outlook calendar. Chris Domanski/ChatGPT

AI also helps me manage the constant stream of information that comes with having two grade-school children.

Every Monday, I open their take-home folders and take pictures of school flyers, calendars, permission slips, and classroom notices, then use ChatGPT to extract dates, deadlines, supplies, and action items. It turns paperwork that would otherwise require another round of mental sorting into something much easier to track.

The prompt I use is: "You'll be given images of take-home folders. Please review this for key dates and things that require action or follow-up on behalf of anyone. Add relevant tasks to my calendar." ChatGPT then exports the scanned events as .ics files that I then import into a school calendar in Outlook.

At this point, ChatGPT hasn't really dropped the ball. It is really nice not to discover at 8 p.m. that there is a project due the next morning — and then spend the rest of the evening scrambling to figure out what we need.

AI frees me up to have more fun with my kids

Domanski also uses AI to generate custom images for his daughters to draw. Chris Domanski/ChatGPT

We also do quite a bit of generative art, asking ChatGPT to provide an image for my daughters to draw on their own.

Originally, we'd ask for something like Disney's Ariel and Elsa dancing underwater in a sandcastle. ChatGPT declined the request, citing copyright restrictions. I asked it to provide an open-source alternative, such as a generic red-headed mermaid and blonde ice princess. Everyone was happy.

Now, a few times a week, my daughters can draw or paint whatever character crossover they want.

AI does not so much create new free time as help us get much more out of the time we already have. We spend less time looking for things, figuring out instructions, or trying to understand a new concept from scratch, and more time actually doing projects, making art, playing games, or just having fun together.

I made my kids a voice-mode AI tutor to help them with spelling and math. It saves me time to have more fun with them.

AI systems developed using data from the Ukrainian battlefield are set to play a crucial role in safeguarding UK defense facilities, rail networks, and energy infrastructure from potential threats posed by protesters and foreign adversaries. This initiative comes from an agreement between the UK and Ukraine.

The scheme allows private firms to tap into the extensive data sourced from Ukraine’s Avengers AI lab, marking a pioneering collaboration in the UK's approach to defense technology. A trial will commence at a UK defense location, focusing on creating a model capable of identifying both protests and attacks from hostile entities. This involves utilizing AI-enhanced sensors embedded in fiber-optic cables to monitor various movements.

If the technology proves effective, the government anticipates its future application in airports, prisons, railways, and energy sectors.

The recent decision to proscribe the activist collective Palestine Action came in light of their July 2022 breach at RAF Brize Norton, which was a worrying lapse in security. The group managed to vandalize two military aircraft and leave without apprehension. Palestine Action has defended its actions by claiming it aimed to disrupt Britain’s involvement in actions they view as genocidal and criminal in the Middle East.

Private companies looking to engage with this data will need approval from the Ministry of Defence (MoD) and will utilize a secure platform for access, which has raised concerns among privacy advocates. Initial pilot programs have received backing from three British AI firms: Sintela, Mind Foundry, and Skyral. Sintela, based in Bristol, has previously entered a $35 million contract with the Trump administration focusing on surveillance at the US-Mexico border.

This collaboration not only allows for the incorporation of operational drone datasets and mission footage into British security frameworks but also includes insights into Russian sabotage tactics against vital Ukrainian systems. The comprehensive military data accumulated over the past four years offers a more nuanced view than previous models, which largely relied on publicly available information.

The UK government envisions employing AI models to swiftly assess threats to critical infrastructure, including railways and military bases, by using advanced fiber-optic cable systems capable of detecting distinct patterns of movement around sensitive areas. Sources from the MoD clarified that this monitoring includes identifying potential protests, not solely actions from foreign states.

Prime Minister Andy Burnham emphasized the opportunity for British firms to meld their AI innovations with battlefield insights from Ukraine, thereby enhancing national security while ensuring the protection of vital infrastructures across the nation.

Future initiatives could explore developing new AI chips for drones, robotics, and automated weapon systems aimed at bolstering defense capabilities.

Steven Murdoch, a professor of computer science at University College London, noted that while the underlying technology for movement detection via fiber-optic sensors is established, it's uncertain how the Ukrainian data will enhance its effectiveness. He acknowledged, however, the potential for discovering valuable insights related to emerging threats that may soon challenge Western Europe.

"What's valuable now could be significant for what other nations will confront down the line," he remarked, citing drones as a relevant example.

Defense Secretary Wes Streeting highlighted Ukraine's role in advancing defense innovation in its fight against Russian aggression. He asserted that leveraging the UK's scientific and technological prowess alongside Ukraine’s battlefield experience would bolster defense and security for both countries.

UK plans to utilize battlefield data from Ukraine to enhance AI training for safeguarding sensitive locations.

Dimly lit command center with flickering screens displaying Ukraine battlefield footage and live drone shots, streams of glowing code mapping a fortified site’s blueprint on a central terminal, a Union Jack draped on the wall, military gear casting long shadows in a tense, protective atmosphere

UK to use Ukraine battlefield data to train AI to protect sensitive sites - The Guardian

- UK and Ukraine will use AI models trained on battlefield data to enhance security systems.  
- Private companies can access Ukraine's AI lab data to develop new surveillance technologies.  
- Fiber-optic sensors will detect movements around sensitive sites to prevent potential attacks.  

AI models trained on Ukrainian battlefield data will be used to stop protesters and foreign states targeting UK defence sites, railways and energy plants under a deal struck between London and Kyiv.

Private companies will also be given access to the vast trove of data from Ukraine’s Avengers AI lab to help build new systems, the first agreement of its kind in the UK.

The scheme will be piloted at a UK defence site, building a model to identify protesters or a hostile-state attack. It uses AI-optimised sensors in buried fibre-optic cables to detect different kinds of movement.

The government has suggested the technology, if proven workable, could be used at airports, prisons, railways and energy infrastructure.

The controversial proscription of the activist group Palestine Action was sparked after the group breached RAF Brize Norton on scooters last June, a highly embarrassing security lapse as a result of which the group was able to deface two military aircraft with spray paint before escaping the site without being detained.

Palestine Action said at the time that it had targeted RAF Voyager aircraft used for transport and refuelling, and that “activists have interrupted Britain’s direct participation in the commission of genocide and war crimes across the Middle East”.

Private companies will be able to access the data once approved by the MoD on a secure platform, though the data sharing is likely to raise alarm among privacy campaigners.

Pilot projects have already been approved with three British AI companies – Sintela, Mind Foundry and Skyral. Sintela, a Bristol-based company that specialises in fibre-optic sensors, recently struck a $35m (£26m) deal with the Trump administration to provide surveillance on the US-Mexico border.

The partnership with the Ukrainian AI lab means new UK security systems can be trained on operational drone datasets, strike mission footage and flight profiles, likely to include Russian sabotage attempts against critical Ukrainian infrastructure.

The cache of four years’ worth of military data contains a far deeper picture than models outside Ukraine have previously been trained on, which had been mostly restricted to open-source data.

The government eventually hopes to use the AI models to predict quickly when infrastructure such as railways, the power grid or military bases might be coming under threat or stress from hostile actors.

That would primarily come from the laying of fibre-optic cables that are able to identify unique movements of people or vehicles around sensitive sites. Ministry of Defence sources said that would specifically include when a site might be targeted by protesters – not just hostile states.

The prime minister, Andy Burnham, said it would be an opportunity for British companies to combine their AI expertise with Ukraine’s battlefield data, which would “accelerate the development of capabilities that not only strengthen our national security but better protect our critical infrastructure and growth in every postcode here at home”.

Other potential projects could include new AI chips to power drones, robotics and automated weapons systems.

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Steven Murdoch, a professor of computer science at University College London, said technologies to detect movement from sensors in fibre-optic cables had been around for decades and that it was unclear if data from Ukraine would refine their capabilities.

“I haven’t heard anything that is very convincing, but there is a plausible scenario where they could see something that could be predicted easily and isn’t in any publicly known dataset,” he said.

Ukraine, however, has a lot of other data that may be valuable, especially related to new threats that countries in western Europe might face in the short to medium term.

“What’s valuable is something we’re seeing today which other countries will be worrying about in the future,” Murdoch said. “Anything to do with drones, for example.”

The defence secretary, Wes Streeting, said Ukraine was “pushing the frontier of defence innovation as they bravely resist Putin’s illegal invasion”.

“By combining the UK’s world-leading scientists, engineers and tech companies with Ukraine’s unique battlefield experience and data, we will strengthen both of our nations’ defence and security,” he said.

UK to use Ukraine battlefield data to train AI to protect sensitive sites

Amidst the bustling environment of the New York Stock Exchange, a Goldman Sachs partner has expressed concern about the implications of artificial intelligence (AI) proliferating on Wall Street. Chris Churchman, who oversees the bank's prominent AI initiative known as Marquee, cautioned that over-reliance on these advanced systems may diminish critical thinking skills among emerging financial professionals.

In a recent episode of Goldman's "Exchanges" podcast, shared exclusively with CNBC, Churchman highlighted the potential pitfalls of AI integration. He likened the situation to how technological advancements have impacted personal navigation and memory, warning that essential analytical skills could be lost if algorithms take over cognitive tasks.

"Reasoning remains crucial," Churchman emphasized. "The ability to conceptualize and articulate arguments is vital, yet we are increasingly delegating this reasoning."

The ongoing drive to incorporate AI in trading and banking practices presents a double-edged sword for the finance sector. While it enhances current profitability, there is a looming risk of undermining the very talent needed for future growth. The automation of routine tasks, which traditionally enabled new bankers and traders to develop their decision-making skills, threatens to erode the foundational workplace culture that shapes junior employees into experienced professionals.

The trend may even lead to a reduction in the demand for junior bankers. In a report from last year, CNBC noted that Wall Street firms were exploring AI solutions to decrease the number of junior staff in relation to their senior counterparts.

Churchman stressed the necessity for banks to strike a balance between leveraging AI technologies and maintaining the apprenticeship model that has historically defined the industry. Drawing from his experience running currency trading at UBS before joining Goldman in 2021, he stated, "Learning comes from hands-on experience, much of which is based on tacit knowledge that has never been documented."

It is critical for Goldman to safeguard this innate knowledge possessed by its most skilled professionals and ensure that it is passed down to future generations, he added.

For example, junior traders typically gain invaluable insights by responding to client pricing inquiries under the guidance of seasoned risk managers. Churchman acknowledged that while this process could be automated, it raises concerns about the competence of senior traders who may not fully grasp the complexities involved.

He advocated for systems that empower employees to make significant decisions in unpredictable, high-stakes scenarios rather than relegating them to passive roles. Despite being one of the leading investment banks globally, Goldman Sachs has yet to finalize how it will navigate the ongoing transition brought about by AI advancements, Churchman remarked, noting his role as co-chair of the firm's Global Banking and Markets AI working group.

Goldman Sachs partner cautions against the significant risk of allowing AI to take over bankers' critical thinking abilities.

A moody, shadow-filled trading room with a single warm spotlight on a suited banker slumped at his desk, head in hands. Behind him looms a giant metallic AI cog with crawling wires that seem to reach for his brain. On the dark glass wall above towers a large, embossed Goldman Sachs logo in muted gold, casting ominous shadows across the floor.

Goldman Sachs partner warns of 'huge danger' in letting AI replace bankers' reasoning skills - CNBC

- Goldman Sachs partner warns AI could hinder young financiers' reasoning abilities on Wall Street.  
- AI's adoption in finance might reduce junior banker roles and erode essential skills.  
- Firms must balance AI use with preserving the apprenticeship culture for future talent.  

A screen displays the the company logo for Goldman Sachs on the floor at the New York Stock Exchange (NYSE) in New York City, U.S., May 7, 2025.

A Goldman Sachs partner leading one of the bank's flagship artificial intelligence projects warned that AI's spread across Wall Street risks hobbling the thinking capabilities of the next generation of financiers.

"There's a huge danger here that in the era of AI, we outsource our reasoning to these models, and we have cognitive atrophy that stops us being able to reason from first principles ourselves," said Chris Churchman, who leads Goldman's digital platform for institutional clients called Marquee.

The comments came during the latest episode of the firm's "Exchanges" podcast, according to a transcript provided exclusively to CNBC.

Just as people lost navigation and memorization skills with modern inventions, bankers risk losing analytical abilities if algorithms handle all the heavy lifting, Churchman said.

"Reasoning is still important," he said. "You still need to reason about [problems] and structure it into an argument, and now we're delegating reasoning."

Wall Street's push to enmesh AI into all of its trading and banking processes could be a kind of devil's bargain: It will make the industry more profitable today while potentially eroding the talent it needs for tomorrow. With AI taking over more of the routine work that has traditionally taught young bankers and traders how to think and make decisions, firms risk sacrificing the culture that turns junior employees into seasoned Wall Street talent.

It could even reduce the need for junior bankers in the first place. Last year, CNBC reported that Wall Street firms were examining ways of using AI to lower the ratio of junior bankers to senior employees.

Banks need to find a balance between using AI and preserving Wall Street's apprenticeship culture, said Churchman, who ran currency trading at UBS before joining Goldman in 2021.

"You learn by doing, and a lot of knowledge is tacit, it was never written down," he said.

Goldman needs "to make sure we don't lose that tacit and intuitive knowledge that some of our best people have today [and] to ensure the next generation have it too," Churchman said.

For instance, junior traders learn by fielding client pricing requests under supervision of experienced risk takers, Churchman said.

"We can absolutely automate that," he said, "but then do we get the senior traders that fully understand?"

Systems must be designed so that employees still call the shots in high-stakes, high-uncertainty decisions rather than becoming passive operators, Churchman said.

Even Goldman, one of the world's top investment banks, hasn't yet "figured out" how it will manage the transition the company has begun, said Churchman, who is also co-chair of the firm's Global Banking and Markets AI working group.

Goldman Sachs partner warns of 'huge danger' in letting AI replace bankers' reasoning skills

Microsoft has introduced a comprehensive framework for AI governance that shifts the focus from static policy documentation to dynamic enforcement mechanisms, continuous monitoring, and verifiable audit trails as organizations integrate AI applications and agents into their operations. This framework encompasses nine key governance areas and four primary functions: policy, control, visibility, and proof, emphasizing the importance of ensuring that governance requirements are actively applied and can be monitored in real time as AI systems are operationalized.

This architecture is designed as a feedback loop for governance. Initially, policies set the groundwork by defining requirements and classifying risks. These are translated into actionable runtime rules through controls, while observability mechanisms track system performance. Continuous evaluations assess both quality and safety, and audit processes convert this operational data into concrete evidence for compliance and retrospective incident analysis.

Manasa T. Ramalinga, a Cloud Solution Architect at Microsoft, elaborated on the inspiration behind this architecture in a LinkedIn post. She emphasized that organizations transitioning AI workloads to production are rethinking their core frameworks to create safer systems, rather than relegating governance to an afterthought.

Microsoft underscores the principle that effective scaling depends on the ability to govern successfully. The governance framework identifies nine distinct domains: policy, data governance, model governance, observability, evaluations, security, identity and access management, audit and compliance, and agent governance. This ensures that runtime controls can encompass all interactions involving users, agents, models, tools, APIs, MCP servers, and enterprise systems.

The governance architecture merges Microsoft Foundry with essential services like Microsoft Purview, Microsoft Entra ID, Defender, and Azure API Management. The AI Gateway within Foundry establishes a secure runtime boundary for tasks including authentication, token constraints, quotas, and policy enforcement. Additionally, Microsoft’s documentation details how this gateway manages MCP tools with centralized authentication, rate limiting, IP restrictions, and audit logging, all without altering MCP servers or agent software.

Evaluations are essential at both the pre-deployment stage and during ongoing operations. Microsoft Foundry provides support for assessing AI applications and agents against various datasets through built-in and customizable evaluators, allowing teams to ensure quality and safety before launch and monitor behavior afterward.

Anthony Bartolo, a Principal Cloud Advocate at Microsoft, highlighted this operational distinction in a LinkedIn post, claiming, "Your AI policy is not governance until production can prove it." He outlined the governance loop as starting from policies that define rules, followed by runtime controls that enforce them, observability that monitors behavior, evaluations that assess quality and safety, and audits that convert telemetry into evidence.

This governance model extends beyond a Microsoft-centric control plan. It integrates aspects of the NIST AI Risk Management Framework and Generative AI Profile, which offer a vendor-neutral strategy for managing AI risks throughout their lifecycle, including governance, measurement, evaluation, and risk reduction. Microsoft’s architecture effectively aligns these governance needs with specific platform controls and operational telemetry.

In addition to the general governance framework, agent governance introduces protocols governing agent identity, access, activity, and workflow checkpoints. Microsoft’s open-source Agent Governance Toolkit enhances the security of autonomous agents by providing capabilities for policy enforcement and establishing interception points. The architecture also includes an Agent Control Specification, which acts as a framework for implementing checkpoints across agent inputs, model calls, tool executions, and outputs, requiring human approval for more critical actions when necessary.

Microsoft Advances AI Governance from Policy-Making to Real-Time Enforcement

A bright, high-tech operations room flooded with warm natural light: on the left, transparent glass panels display static policy documents sketched in white outlines; on the right, live dashboards stream real-time enforcement logs in green and orange. In the center a large Microsoft logo glows subtly on a sleek monitor above a glass control console. A lone professional silhouette stands behind the console, hands resting on the glass as the policy panels visually morph into active runtime alerts, capturing the shift from policy to enforcement.

Microsoft Moves AI Governance From Policy to Runtime Enforcement - infoq.com

- Microsoft introduces an AI governance architecture that emphasizes runtime enforcement and continuous evaluation.  
- The framework consists of nine governance domains and focuses on operational compliance during AI deployment.  
- Microsoft's architecture includes tools for agent governance and aligns with NIST AI Risk Management Framework.  

Microsoft has outlined an AI governance architecture that moves governance from documented policies toward runtime enforcement, continuous evaluation, observability, and audit evidence as organizations deploy AI applications and agents in production. The framework spans nine governance domains and four functions: policy, control, visibility, and proof, addressing the need to verify that governance requirements are enforced and observable during AI system operation.

Microsoft AI governance architecture (Source: Microsoft Blog Post)

The architecture treats governance as a continuous operational loop. Policies establish requirements and risk classifications, controls translate them into access and runtime rules, observability captures system behavior, and evaluations test quality and safety. Audit processes then turn operational telemetry into evidence for compliance and incident investigation.

Manasa T. Ramalinga, Cloud Solution Architect at Microsoft, described the motivation in a LinkedIn post accompanying the architecture. She added that organizations moving AI workloads into production are re-architecting their foundational structures to build safer systems, rather than treating governance as an afterthought.

Organizations cannot scale what you cannot control

Microsoft identifies nine governance domains: policy, data governance, model governance, observability, evaluations, security, identity and access, audit and compliance, and agent governance. Runtime controls can span interactions among users, agents, models, tools, APIs, MCP servers, and enterprise systems.

The architecture combines Microsoft Foundry with services including Microsoft Purview, Microsoft Entra ID, Defender, and Azure API Management. Foundry's AI Gateway provides a runtime boundary for authentication, token limits, quotas, and policy enforcement. Microsoft also documents using the gateway to govern MCP tools, providing centralized authentication, rate limiting, IP restrictions, and audit logging without modifying MCP servers or agent code. Microsoft Foundry AI Gateway documentation, Microsoft guidance for governing MCP tools

Evaluations are positioned both before deployment and in production. Microsoft Foundry supports evaluation of AI applications and agents against datasets using built-in and custom evaluators, allowing teams to assess quality and safety before release and monitor production behavior afterward.

Anthony Bartolo, Principal Cloud Advocate at Microsoft, described the operational distinction in a LinkedIn post

Your AI policy is not governance until production can prove it. He outlined the loop as policy defining rules, runtime controls enforcing them, observability capturing behavior, evaluations testing quality and safety, and audit converting telemetry into evidence.

AI governance lifecycle from policy and risk classification (Source: Microsoft Blog Post)

The approach is broader than a Microsoft-specific control plane. The NIST AI Risk Management Framework and Generative AI Profile provide a vendor-neutral framework for managing AI risks across the lifecycle, including governance, measurement, evaluation, and risk mitigation. Microsoft's architecture maps those governance concerns into concrete platform controls and operational telemetry.

Agent governance adds controls around agent identity, access, activity, and workflow checkpoints. Microsoft's open-source Agent Governance Toolkit provides runtime security capabilities for autonomous agents, including policy enforcement and interception points. The architecture also describes the Agent Control Specification as a mechanism for checkpoints across agent inputs, model calls, tool execution, and outputs, with higher-impact actions able to require human approval

Microsoft Moves AI Governance From Policy to Runtime Enforcement

In Taipei, prosecutors have initiated charges against nine individuals, including former staff members from Nvidia and Super Micro Computer, for their roles in facilitating the shipment of sophisticated artificial intelligence servers to China. This move comes as Taiwan intensifies its efforts to prevent advanced technology from reaching the mainland.

Among those indicted, eight face serious allegations, including breach of trust and forgery, while one person is accused of illegally diverting funds from a firm involved in the case.

In an effort to impede China's access to cutting-edge Nvidia chips, Washington has implemented strict export controls. As a result, some Chinese companies have turned to unauthorized channels to acquire these vital components. Taiwan, known for its production of high-tech computing hardware, is now under increased scrutiny to implement stringent measures against such practices.

According to prosecutors in Keelung, two defendants falsely represented the intended use of 130 AI servers purchased from Super Micro, which were equipped with advanced Nvidia chips. They claimed the equipment would remain in Taiwan, assuring authorities that the servers would not be sold or exported to entities facing U.S. sanctions and that all transactions would adhere to American export control laws.

Furthermore, the indictments reveal that an employee from Super Micro disclosed confidential internal vetting procedures and enlisted the help of an Nvidia staff member to secure server allocations.

Taiwan Accuses Nine Individuals of Smuggling AI Servers to China

A dimly lit port warehouse at night, rows of half-open wooden crates stamped “AI Servers,” a Taiwan flag draped on a concrete pillar, uniformed officers escorting nine shadowy figures in handcuffs, flickering overhead lamps casting deep, tense shadows across the scene.

Taiwan Charges Nine in Connection With Smuggling of AI Servers to China - WSJ

- Nine individuals, including former Nvidia and Super Micro employees, were indicted in Taiwan.  
- Charges include breach of trust and forgery related to AI server shipments to China.  
- U.S. export controls lead to Chinese firms seeking Nvidia chips through illicit channels.  

Chinese companies have sought access to Nvidia chips through backdoor routes. Vernon Yuen / Nexpher Images / ZUMA Press

TAIPEI—Taiwan prosecutors charged nine people, including former employees of Nvidia and Super Micro Computer, with aiding the shipment of advanced artificial-intelligence servers to China, as Taiwan tries to limit technology reaching the mainland.

The indictment charges eight of the individuals with crimes including breach of trust and forgery. One individual was charged in connection with allegedly siphoning funds from a company involved in the case.

Washington is trying to block China from getting access to the latest Nvidia chips and has imposed export controls. That has pushed some Chinese companies to seek the chips through backdoor routes. Taiwan, which manufactures advanced computing components, is under pressure to find ways to clamp down.

Two of those charged made false claims to secure approval for the purchase of 130 AI servers from Super Micro equipped with high-end Nvidia chips, including by saying the hardware would remain at a facility in Taiwan, prosecutors in the port city of Keelung said Monday.

Defendants said the servers wouldn’t be sold or exported to U.S.-sanctioned entities and that the hardware would comply with U.S. export-control regulations, prosecutors said.

According to the indictments, a Super Micro employee leaked the U.S. company’s internal vetting procedures and asked an Nvidia employee to help secure server quotas.

Taiwan Charges Nine in Connection With Smuggling of AI Servers to China

Mario specializes in the intersection of technology and healthcare, focusing on the FDA's oversight of artificial intelligence, Medicare's reimbursement for health technologies, the integration of AI in clinical practices, mental health chatbots, and consumer wearable devices. Additionally, he co-writes the STAT Health Tech newsletter, which is distributed for free twice a week. You can contact Mario through Signal at mariojoze.13.

The Food and Drug Administration is currently working on a strategy to regulate medical devices that incorporate generative artificial intelligence. 

According to Rick Abramson, the director of the FDA's Digital Health Center of Excellence, the agency will publish documents detailing its policies on this emerging technology. 

In a conversation with STAT, Abramson stated, "Our aim is to establish formal policy guidance. The ecosystem is seeking clarity, and we intend to provide that. In addition to general guidance on generative AI, the ecosystem can also anticipate specialized advice on specific topics of interest or complexity within this realm."

FDA digital health chief pledges forthcoming regulatory guidance on generative AI.

A modern FDA headquarters bathed in warm morning light, a poised digital health leader in business attire walking up the steps with a tablet in hand, above the building a translucent blueprint of AI circuitry and policy text swirling into a clear pathway, hopeful atmosphere.

FDA digital health leader promises generative AI regulatory guidance is coming - STAT

- FDA is planning regulations for medical devices utilizing generative artificial intelligence technology.  
- Agency aims to release formal policy guidance on generative AI in health care.  
- Guidance will include both broad and specialized regulations for complex AI topics.  

Mario covers technology in health care, including FDA regulation of artificial intelligence; how Medicare pays for health tech; the use of AI in clinical care; mental health chatbots; and consumer wearables. He’s also the co-author of the free, twice weekly STAT Health Tech newsletter . You can reach Mario on Signal at mariojoze.13.

The Food and Drug Administration says it is developing a plan for how to regulate medical devices that use generative artificial intelligence.

The FDA will eventually release documents spelling out agency policies regarding the technology, Rick Abramson, director of the agency’s Digital Health Center of Excellence, told STAT.

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“Our goal is formal policy guidance,” he said in an interview. “The ecosystem is expecting clarity. We seek to provide that clarity, and I’ll also throw in that I think the ecosystem can expect not only broad guidance on the overall topic of generative AI, but also some more narrowly constructed specialty guidance on particular generative AI topics of special interest or special complexity.”

FDA digital health leader promises generative AI regulatory guidance is coming

In a recent interview broadcast on Sunday evening, President Donald Trump expressed strong support for the expansion of artificial intelligence data centers across the United States, asserting that communities resisting their establishment are making an ill-informed choice. This assertion comes amid escalating public dissent and pushback from governors across the political spectrum. 

During his discussion with former aide Michael Cohen, Trump stated that the U.S. has a significant advantage over China in the AI sector. He emphasized his support for AI companies to independently develop their own power facilities, claiming these data centers are not drawing from the already strained power grid. “Communities that refuse to welcome a data center are making a mistake,” he stated, noting that many other communities are eager to host them. He elaborated that these centers would create jobs and yield considerable tax revenue for local governments, asserting that the "smart ones" recognize the benefits and are advocating for their development within their areas. Last week, Trump articulated his support for data centers while hosting leaders from the cryptocurrency industry at the White House, indicating that he would be thrilled to see one established in his own locality, whether in a mayoral or gubernatorial capacity.

In contrast, Texas Governor Gregg Abbott voiced significant concerns regarding the AI industry’s impact, stating on ABC News that companies in the sector had caused their own backlash by failing to coordinate with state or local officials. Abbott’s critique marks one of the strongest rejections of data centers from a GOP leader, a sentiment echoed by various Democratic governors. Recently, he declared a temporary halt on new data center approvals until there can be a thorough audit and verification of their implications on the state’s power grid. Similarly, New York has imposed a moratorium on large new data center constructions as it seeks to develop new environmental regulations and assess their effects.

As Trump champions these data centers, concerns are mounting that growing public opposition could pose challenges for Republicans in upcoming midterm elections. An internal memo from the National Republican Senatorial Committee highlighted this potential risk, noting that Democratic candidate Sherrod Brown has made his opposition to data centers a focal point of his campaign against incumbent Republican Senator John Husted in Ohio. The memo cautioned that data centers could be a significant liability for Husted, stressing that if he loses, the blame linked to data centers could resonate nationwide.

Polling data further underscores the contentious nature of data centers. A Gallup survey released earlier this year noted that 71% of Americans expressed some level of opposition to having data centers in their communities, surpassing the 53% who oppose nuclear energy facilities. The resistance is particularly high among Democrats, with 75% opposing such constructions, but it also includes a substantial number of Republicans (63%) and Independents (74%) who share the same sentiment.

Trump Claims Those Against AI Data Centers Are Making an 'Error'

Donald Trump stands at a podium draped with a U.S. flag, bathed in warm sunset light, confidently pointing toward a vast AI data-center complex behind him—its rows of server racks glowing softly through glass walls—while a small group of protesters at the scene hold “Stop AI Data Centers” signs, their faces in shadow. Natural lighting, cinematic news-thumbnail style.

Trump Defends AI Data Centers—Says Opposing Them Is A ‘Mistake’ And ‘Smart Ones’ Want Them - Forbes

- Trump defended AI data centers, claiming they benefit communities with jobs and tax revenue.  
- Texas Governor Abbott criticized AI companies for lack of coordination, supporting a data center moratorium.  
- A Gallup survey revealed 71% of Americans oppose data centers in their communities.  

Topline President Donald Trump defended the ongoing buildout of AI data centers across the country and said the communities opposing them were making a mistake, as he continued to embrace the controversial buildout of AI infrastructure despite growing public backlash and opposition from governors from both parties. US President Donald Trump speaks as he meets with lifeguard Ryder Williams in the Oval Office of the White House in Washington, DC. AFP via Getty Images

Key Facts

In his interview with former fixer Michael Cohen, which aired in full on Sunday evening, Trump claimed the U.S. was “leading China in AI by a lot.” The president said he was letting the AI companies “build their own power plants” and claimed these data centers were not taking “power from the grid,” which he described as old and “tired.” Trump said, “Frankly, communities that don’t take a data center are making a mistake,” and claimed that there’s “plenty of communities” that want them. He argued that data centers will lead to more jobs and “tremendous tax revenue coming into the towns and cities,” and said that the “smart ones” are calling for them to be built in their communities. Last week, while hosting several crypto industry executives at the White House, the president said he would “absolutely want” a data center in his area if he were given the chance, whether as a mayor of a town or the governor of a state.

What has the Texas Gov. Said About Data Centers?

The president’s comments in support of data centers were aired shortly after Texas’ Republican Gov. Gregg Abbott appeared on ABC News on Sunday morning and said the AI industry, “basically dug their own grave for the problem that’s been caused for them, and that’s why they got the backlash they deserve.” Abbott blasted the companies, saying they did not coordinate with state or local governments. Abbott’s comments are the most serious pushback that data centers and AI companies have faced from a prominent GOP leader—and it echoes similar stances taken by several Democratic Governors. Earlier this month, Abbott announced a moratorium on the approval for new data centers until authorities can “comprehensive verification and audit of all data centers,” being added to the state’s grid. Last month, New York also instituted a moratorium on large new data center buildouts until it develops new environmental regulations and examines their impact on the power grid, among other things.

Tangent

Trump’s remarks about data centers come amid concerns that the growing public opposition to them could negatively impact the Republicans in the midterms. Last week, the National Republican Senatorial Committee sent out an internal memo warning that Democrat Sherrod Brown had made his opposition to data centers the “centerpiece” of his campaign against incumbent Republican Sen. John Husted in Ohio. The memo warned: “More than any other thing in this race, data centers are the anchor hanging around Husted’s neck. If he loses and data centers get the blame, politicians across the country will take notice.”

What does polling data about data centers show?

A Gallup survey published earlier this year found that 71% of Americans are somewhat or strongly opposed to data centers in their communities, a higher share than the 53% who oppose nuclear energy plants. Opposition to data centers is strongest among Democrats, with 75% somewhat or strongly against such build-outs. A majority of Republicans (63%) and Independents (74%) also oppose data center construction.

further reading

People Would Rather Have Nuclear Power Plants In Their Area Than AI Data Centers (Forbes)

Trump Says Those Opposing AI Data Centers Are Making A ‘Mistake’

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Dr. Dre Reveals Why He’s ‘Embracing’ AI In Making Music - Yahoo

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Alibaba's stock experienced a significant decline of up to 10% in Hong Kong on Monday, following the announcement of an extensive share placement worth 80 billion Hong Kong dollars (approximately $10.20 billion) targeted at non-U.S. investors.

The tech powerhouse outlined its intention to allocate all proceeds from this move toward bolstering its comprehensive AI capabilities, which includes strengthening and expanding its AI infrastructure.

The company will issue 710 million new shares priced at HK$112.70 each, a reduction compared to the stock's previous closing price of HK$123 on Friday. As a result, shares were trading down 8.4% at HK$112.70.

This share placement, set to finalize on Wednesday, comes shortly after Alibaba revealed a staggering 75% drop in profits for the quarter ending in June, largely attributed to substantial investments in AI technology. Capital expenditures surged by 75% to reach 67.7 billion yuan during this period.

Alibaba's shares fall sharply following the announcement of a $10.2 billion share placement to support its AI initiatives.

A dimly lit trading floor bathed in deep shadows, walls lined with screens flashing red Alibaba stock tickers plunging; overhead, a massive illuminated Alibaba logo hangs like a looming giant. A jagged neon‐red arrow slashes downward across one screen, while shredded share certificates drift through the air like ash. In the foreground, a lone trader’s silhouette clutches his head, under a single warm spotlight that heightens the tense, dark atmosphere.

Alibaba plunges after announcing $10.2 billion share placement to fund AI push - CNBC

- Alibaba's shares fell 10% after announcing an 80 billion HKD share placement.  
- The funds will be used to improve Alibaba's full-stack AI capabilities and infrastructure.  
- The company's profit dropped 75% last quarter due to high AI-related expenditures.

Night view of Alibaba's headquarters building located by the Huangpu River in Shanghai, China on Nov. 16, 2025.

Alibaba shares plunged as much as 10% in Hong Kong on Monday after the Chinese tech giant priced an 80 billion Hong Kong dollar ($10.20 billion) placement of newly issued shares to non-U.S. investors.

The company said it plans to use all of the net proceeds to invest in its full-stack AI capabilities, including expanding and enhancing its AI infrastructure.

Alibaba will issue 710 million new shares at HK$112.70 apiece, compared with the stock's Friday closing price of HK$123. Shares were last trading 8.4% lower at HK$112.7.

The share placement, expected to close on Wednesday, comes just days after Alibaba reported a 75% drop in profit for the June-quarter as heavy AI spending weighed on its results. Capital expenditure jumped 75% to 67.7 billion yuan.

Alibaba plunges after announcing $10.2 billion share placement to fund AI push

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Former Texas Governor Rick Perry claims there is a prevalence of 'misinformation' regarding AI data centers.

A dimly lit data-center hall with rows of server racks receding into shadow. Ghostly red digital fragments—“hoax,” “fake,” “misinfo”—swirl like smoke overhead. In the foreground, former Texas Gov. Rick Perry stands at a simple wooden lectern, arms raised defensively under a warm spotlight. A small Texas flag drapes the lectern.

‘Misinformation’ about AI data centers is widespread, former Texas Gov Rick Perry says - Fox News

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