Report

Agentic AI - Finance & the ‘Do It For Me’ Economy

An artificial intelligence that can make autonomous decisions without human intervention. Meet agentic AI.&nbsp;<o:p></o:p>AI, and agentic AI could have a bigger impact on the economy and finance than the internet era.<o:p></o:p>This paradigm shift in AI is powered by a combination of technological breakthroughs in contextual understanding, memory, and multi-tasking abilities.<o:p></o:p>Agentic AI effectively turbocharges the Do It For Me (DIFM) economy. In financial services, users will have their own bots or AI agents helping them choose products and execute transactions. Competition will tick-up as starts-ups grow.<o:p></o:p>The nature of work could change. Those tasks that are outsourced today to contractors or third parties will be increasingly done by agentic AI.<o:p></o:p>The financial services sector is the second largest consumer of GenAI after the telecom and media sector. Historically, banks are the biggest spender on technology (outside of the tech industry). This trend will likely continue with GenAI and agentic AI too.<h5>Agentic AI Use Cases Across Financial Services</h5><img style="border-radius:10px;" src="/rcs/v1/media/citigpa/akpublic/storage/retrieveMediaFile/678a4e5e4a24ce43ec43ec8d.png" alt="Chart showing different use cases in the financial industry." width="900" height="693"><o:p></o:p><div class="lmn-container infographics-template" style="color:#000000;font-family:Citi-Sans-Text-Regular;margin:auto;"><div style="background-color:#F0F5F7;border-radius:20px;margin:auto;padding:48px;"><h3 style="font-family:Citi-Sans-Text-Regular;font-size:16px;line-height:24px;margin-bottom:24px;">Detailed infographic description</h3>This infographic, titled "Agentic AI Use Cases," is a table outlining the applications of Agentic AI across four key sectors of the financial industry: Wealth Management/Retail Banking, Corporate Banking, Institutional Investor and Insurance. The use cases are organized into seven distinct categories, each marked by an icon. The seven categories are: Personalized Offers, Customer Engagement, Operational Efficiency, Risk and Underwriting, Financial Forecasting, KYC / Onboarding and Fraud Prevention.</div></div>&nbsp;Agentic AI is largely in an experimental phase – and this report offers up insights gleaned as we looked under the hood on what is being built now for future production.<o:p></o:p>We spoke to 30+ AI startup founders, BigTech executives and other external experts in preparing this GPS report.<o:p></o:p>It highlights some of the key use cases of agentic AI in financial services, ranging from compliance, deepfake and fraud prevention, onboarding and KYC, wealth, credit, treasury workflows, and much more.<o:p></o:p>

Finance & the ‘Do It For Me’ Economy

An artificial intelligence that can make autonomous decisions without human intervention. Meet agentic AI.&nbsp;&nbsp;<o:p></o:p>

Agentic AI

Transcript

Citi GPS Report

In this episode of The Citi Institute podcast, host Alex Miller is joined by Shenwei Zhu, Managing Director, Investment Banking Division, Citi and Felix Zhang, CEO and Founder, Pudu Robotics to discuss the possibilities and challenges that come with the rapid advancements in the field of robotics, the rise of AI-powered robots that can see, learn, move, talk, and take actions, and what that means for the future.Shenwei Zhu, Managing Director, Investment Banking Division, Citi highlights the importance of data availability and techniques like virtual simulation to train physical AI models and the potential impact of robotics on the labour force.Felix Zhang, CEO and Founder, Pudu Robotics offers advice for aspiring robotics entrepreneurs, emphasising the importance of technology, long-term vision, customer collaboration, and global expansion.Our guests share insights on the growth of the robotics industry, emerging policy and regulatory considerations, including implications for the labour force.Citigroup (“Citi”), and Pudu Robotics are not affiliated and are independent from each other, Although Pudu Robotics is a client of the Banking sector. The speakers’ views are their own and may not necessarily reflect the views of Citi or any of its affiliates. All opinions are subject to change without notice. Neither the information provided, nor any opinion expressed constitutes a solicitation for the purchase or sale of any security. The expressions of opinion are not intended to be a forecast of future events or a guarantee of future results.

<ul><li>AI-driven robotics are revolutionizing how machines learn, act, and integrate into industry and daily life.</li><li>Virtual simulation and data-driven training are accelerating robotics innovation.</li><li>The discussion examines workforce impacts, policy shifts, and what drives success in global robotics ventures.</li></ul>

Felix Zhang is the founder and CEO of Pudu Robotics, the world's leading service robotics company. Felix studied mechanical engineering as an undergrad and attended Hong Kong University of Science and Technology's master's program for computer science. At the early age of 20, Felix placed first in a prestigious Asian-Oceanian college robot competition, and second overall in the APAC region. This was the catalyst for Felix to launch a series of startups including the leading tech news outlet in China (leiphone.com) and a home robotics company, as well as his current company, Pudu Robotics, in 2016. Under Felix's stewardship, Pudu grew into a global leader in the service robotics sector, with over 90,000 units deployed globally in 60+ countries and regions, over 800 employees, and over one billion yuan in funding from Tencent, Sequoia Capital China, and other top investors.

Felix Zhang, Founder and CEO of Pudu Robotics

Shenwei Zhu is a Managing Director in Citigroup's Investment Banking Division. Shenwei currently is based in New York covering Industrial Technology (with a focus on automation, robotics, machine vision, Industrial IoT, industrial safety, advanced mobility and hydrogen fuel cell) companies globally. Previously he was based in London, focusing on industrial M&amp;A across the EMEA region. Prior to that, Shenwei was part of Citi’s Global Industrials Investment Banking group in New York. &nbsp;Shenwei earned an MBA with honors from Cornell University, a Master of Mechanical and Aerospace Engineering with honors from Illinois Institute of Technology and graduated cum laude from the University of Notre Dame with a B.S. in Mechanical Engineering.Shenwei is a native Mandarin speaker.Shenwei has advised on over $50bn of M&amp;A, IPO and financing transactions, his transaction experiences include:<ul style="list-style-type:disc;"><li>Advisor to Symbotic on its $550mm acquisition of Walmart Advanced Systems and Robotics</li><li>Advisor to Nordic Capital on its ~$1bn sale of Consilium Safety Group to Antin Infrastructure Partners</li><li>Capital Markets Advisor to Nikola’s $900mm equity capital raise ($200mm private convertible + $600mm At-the-Market (ATM) equity offering +$100mm Follow-on)</li><li>Advisor to Softbank on its $5.5bn De-SPAC merger with Symbotic </li><li>Advisor to Nikola’s acquisition of Romeo Power</li><li>Capital Markets Advisor to FuelCell Energy’s At-the-Market (ATM) equity capital raise</li><li>Joint Bookrunner to AutoStore’s $2.45bn IPO on Oslo Stock Exchange</li><li>Advisor to SK Holdings on its $1.6bn Investment in Plug Power </li><li>Advisor to John Deere on its $250mm acquisition of Bear Flag Robotics</li><li>Lead Left underwriter to Mason Industrial Technology’s $500mm IPO on NYSE</li><li>Lead Left underwriter to Hennessy Capital Investment Crop V’s $345mm IPO on NASDAQ</li><li>Lead Left underwriter to Pivotal Investment Corp III’s $275mm IPO on NYSE</li><li>Advisor to Xerox on its $35bn public hostile takeover of HP (transaction terminated) </li><li>Advisor to Fortna on its $290mm sale to Thomas H. Lee Partners</li><li>Advisor to Xerox on its $7.7bn merger with Fujifilm and defense against shareholder activist Carl Icahn (transaction terminated) </li><li>Advisor to Korber on its acquisition of HighJump Software for an undisclosed amount</li><li>Advisor to Sibanye-Stillwater on its sale of gold and palladium streaming rights to Wheaton Precious Metals for $500mm</li><li>Advisor to Louis Dreyfus Company on its sale of Global Metals business to a consortium led by China Molybdenum for $450mm</li><li>Advisor to Sibanye Gold Limited on its acquisition of Stillwater Mining Company for $2.2bn</li><li>Lead Left underwriter to AquaVenture Holding’s $135mm IPO on NYSE</li><li>Joint lead bookrunner for the $3bn recapitalization of R.R. Donnelley’s spinoff of LSC Communications and DFS</li><li>Advisor to TE Connectivity on its sale of Circuit Protection Devices business to Littelfuse for $350mm </li><li>Advisor to TE Connectivity on its acquisition of Measurement Specialties for $1.7bn</li><li>Advisor to Eaton Plc on its sale of Aerospace Power Distribution and Cockpit Solutions business to Safran for $270mm</li><li>Advisor to R.R. Donnelley on its acquisition of Consolidated Graphics for $700mm</li></ul>

Shenwei Zhu, Managing Director, Investment Banking, Citi 

Our guests share insights on the growth of the robotics industry, emerging policy and regulatory considerations, including implications for the labor force.&nbsp;

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The Rise of AI Robots

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AI in  Investment Management - Beyond Efficiency Gains

The way artificial intelligence (AI) is used by the investment management industry is rapidly shifting from a focus on operational efficiency to enabling more sophisticated investment-centric applications. This is being driven by AI's ability to handle vast amounts of data, generate insights, and automate complex tasks.This report is a follow up to our June 2024 report AI in Investment Management – the Pursuit of a Competitive Edge. &nbsp;

<ol style="list-style-type:decimal;"><li>Generative AI and agentic AI are fundamentally reshaping investment management, moving beyond simple efficiency gains to enable advanced research, analysis, and decision-making by processing unprecedented data volumes and automating complex workflows.</li><li>The focus of AI adoption in investment management has evolved from primarily improving operational efficiency to actively contributing to alpha generation through sophisticated data analysis and autonomous execution of tasks.</li><li>AI applications are expanding across investment, distribution, and operations, with new use cases emerging and a growing “wishlist” for future potential uses, including research assistants, predictive analytics, and automated decision-making support.</li><li>AI and GenAI are pivotal in accelerating the electronification of financial markets, transforming trade execution, data handling, and streamlining voice-to-electronic communications, &nbsp;particularly in less electronified asset classes like fixed income and derivatives.</li><li>Despite rapid advancements, significant challenges persist, including concerns about over-reliance and cognitive debt, confirmation bias, data privacy, security, and the need for robust regulatory frameworks and talent upskilling.</li><li>Investment firms face strategic decisions regarding developing AI capabilities in-house versus partnering with fintechs – the “buy vs. build” dilemma. AI accelerates the consideration for modularization of investment processes and leveraging external expertise where efficient.</li><li>Future developments in AI for investment management include applications in longer-term strategic signal generation, deploying agentic AI for time series forecasting and uncovering complex relationships through graph neural networks.</li></ol>

Key Takeaways 

The way artificial intelligence (AI) is used by the investment management industry is rapidly shifting from a focus on operational efficiency to enabling more sophisticated investment-centric applications. This is being driven by Ai’s ability to handle vast amounts of data, generate insights, and automate complex tasks.

AI in Investment Management - Beyond Efficiency Gains

<ul><li>More than just a menace: Deepfakes have evolved from social media entertainment to powerful tools of deception, with nearly 5% of all fraud attempts in 2024 estimated to involve deepfake elements.</li><li>Workplace infiltration: AI and deepfakes are also infiltrating the workplace. At Money20/20 USA (October 2025), a leading technology firm revealed that up to 50% of job applications it receives are fake.</li><li>Trust no-one: Traditional identity checks are no longer sufficient and the focus must shift to zero-trust communication and training for employees and law enforcement to stay ahead of sophisticated fraud techniques.</li></ul>

AI Deepfakes - When Seeing and Hearing Can’t be Trusted

<hr>1 Gartner, Gartner Survey Shows Just 26% of Job Applicants Trust AI Will Fairly Evaluate Them, 31 July 2025. &nbsp;

AI deepfakes are the new face of deception. They are voices, images, videos or even text created by artificial intelligence (AI) that look and sound indistinguishably real. Once mere entertainment novelties, they have evolved into powerful tools of manipulation and fraud, marking a new era in financial crime.This wave of AI-driven deception is now infiltrating the workplace and recruitment processes, with some estimates suggesting that by 2028, one in four candidate profiles worldwide could be fake.1This Citi Institute report outlines the extent of deepfake infiltration and highlights what corporates can do to counteract the threat.

<ul><li>Physical AI is at an inflection point for industrial markets, marked by abundant capital, maturing technology and a diversifying ecosystem</li><li>While humanoid robots have attracted ample attention, this form factor may not be ideal for many Physical AI applications, with battery power and manual dexterity among the challenges</li><li>Warehouse automation is seeing a transition from rigid Automated Guided Vehicles to flexible Autonomous Mobile Robots, but the future of this competition is less about robotic hardware than about the software ecosystem orchestrating that hardware</li></ul>

In a new Super-Sector Analysis from Citi Research, Martin Wilkie and a team of analysts explore the rise of Physical AI — defined as any physical process learning from and applying AI — for industrial markets. We see Physical AI as at an inflection point, with abundant capital, maturing technology and a diversifying ecosystem. AI-enabled edge devices (those at the “edge” of a network as opposed to the central data center or cloud) could see growth in double-digit percentages, as could design and simulation software.&nbsp;Overall, we see adoption of Physical AI adding percentage growth in the mid-single digits to annual customer capex growth as capital replaces labor, with additional growth fueled by M&amp;A in software and connected hardware. Similar to how Large Language Models (LLMs) need huge amounts of text, industrial AI needs industrial data, of both real-world and simulated varieties. Industrial companies’ large data-gathering physical installed bases and domain-specific know-how loom as both potential key enablers of adoption and moats against disruption.The growth of networked machines that can connect to the Internet, aka the “Internet of Things,” has already augmented on-device intelligence for industrial products, but we now think the next driver will be AI, as featured in product and process design as well as in edge intelligence. Emerging use cases for industrials include preventative maintenance, product design and operations monitoring.&nbsp;In recent years, AI investment for industrial names has been dominated by the impact of Generative AI (GenAI) and LLMs on data centers and related infrastructure. Physical AI is different in that it’s domain specific, requiring separate adoption by end markets that each have unique requirements. That means spending patterns will be defined not by hyperscalers’ capex plans, but by the pace of adoption in each end market.&nbsp;We see three pillars of success for industrial companies: digital twin models (which are virtual representations of physical processes), real-world data gathering through edge devices, and simulation. We’re at the beginning of this journey, as development of the technology is nascent and the industrial cycle will play a role. But companies are already preparing for what’s coming, and we expect investment to continue to pivot to Physical AI adoption.To illustrate the opportunity, there are some 4 million industrial robots installed globally, with penetration varying enormously by region and industry. If such robots displace 30% of manufacturing tasks over the next decade (running at three shifts a day and doubling human productivity), the installed base will grow to almost 30 million in 10 years, a compound annual growth rate (CAGR) above 20%. That’s almost three times the growth rate predicted by the International Federation of Robotics. We also note that increased adoption of AI arguably tilts spending from labor to capital investment; in the factory P&amp;L, employee cost is replaced by depreciation. We estimate the related capex requirements could augment capex growth by mid-single digits.&nbsp;<h2>Phases of AI adoption</h2>We see several phases of AI adoption. GenAI is most prevalent, used in industry to combine “knowledge” (such as service documentation) and data, and we see GenAI primarily as an efficiency tool. Agentic AI, with AI decision-making in industrial applications, is also emerging for specific tasks, but adoption is very early-stage, including in areas such as purchasing. “Digital twin” technology and software-defined automation (where AI increasingly drives product and process design) are arguably the areas offering the biggest potential for both disruption and benefits.Physical AI is often equated with humanoid robots, but the market is larger than that: NVIDIA, for example, divides AI-capable robots into three categories: agentic AI (knowledge robots), generalist robots (including humanoid forms) and transportation (such as autonomous vehicles). We focus on generalist robots, specifically in factory and warehouse automation. Humanoid robots will be an important category but won’t displace many specialized robotic form factors, which can be faster or more powerful in specific applications.&nbsp;Even for data software, the industrial installed base and related domain experience matter; industrial software platforms benefit enormously from “equipment on the ground” — controllers, sensors, motors and other devices well positioned to collect data in order to optimize equipment and streamline processes. These connected products arguably formed the largest barriers to entry for newcomers to the industrial Internet of Things, and we think this will also be the case for Physical AI.<h2>Use cases</h2>Simulation is a key pillar of the “simulation, training, edge” framework to drive penetration of Physical AI, especially in product and process design. Product design already relies heavily on software to simulate everything from drag coefficients to load-bearing capabilities, and AI design software can iterate toward a design that’s optimized but within the parameters. Simulation already offers efficiency gains over real-world testing, with more such gains expected: For instance, teaching a robot using real-world data is painfully show, but using synthetic data can accelerate this.When discussing industrial robots, humanoid form factors have attracted a lot of attention, but our impression is that intelligent robotic arms with dedicated end-of-arm effectors are optimal in more than 90% of manufacturing cases, pointing to significant adoption potential. Over time humanoids could offer autonomous versatility, but in the near term we see intelligent single-arm robots integrated with dedicated process know-how as the preferred form factor. Integrating intelligence and AI will be the next key technology step, with ABB Group seeing pick-and-place applications becoming more autonomous and versatile first, with potential differentiation in software, high motion accuracy, high-speed performance and integration tools.Simulation, training and edge represent the “three computers” needed in industrial AI, and edge control will need to evolve alongside the assets. At its most basic, industrial automation is sensing (data gathering), intelligence (decision making) and control (instructions). Most industrial processes rely on embedded software in logic controllers or distributed control systems as the intelligence, which has been coded in advance. AI can evolve this to optimize decision-making in real time.<h2>Humanoid robot challenges</h2>Physical AI is simultaneously the key and the main obstacle to developing humanoid robots because integrated advanced AI with complex hardware creates significant hurdles to real-time performance, efficiency and safety. Unlike software-only AI, Physical AI must directly perceive and interact with the unpredictable real world, and without excessive latency.&nbsp;Building Physical AI requires repetitive training and data collections, which can be done through simulated methods, real-world ones or a combination of the two. We see more humanoid-robot companies relying on simulated methods for such training, as simulated methods are far more time-efficient than real-world ones.&nbsp;Many humanoid-robot companies are focusing on industrial applications, as factory jobs are mostly well-defined tasks such as moving, stacking or sorting objects. An issue that’s generated considerable debate is whether a humanoid robot needs to be equipped with two legs. This is considered the optimal form for a variety of workplace setups, but maintaining balance consumes significant battery power. This has led to more humanoid-robot companies in China introducing wheel-based versions of their products.&nbsp;Another issue is the design of dexterous hands. Chinese humanoid-robot makers equip their robots with suckers or three-finger hands, since these are able to handle existing tasks that humanoid robots are trained to carry out.&nbsp;<h2>Vision and sensing</h2>As the Physical AI landscape evolves, we see machine vision playing a critical role in further driving efficiency on factory floors and enhancing product quality through assessments made on the assembly line. We also think machine vision will be increasingly essential in collecting real-time factory data, which can be utilized to optimize overall operational productivity. Machine vision is becoming more mainstream, with such systems used for applications including visual inspection, defect detection, process control and sorting.&nbsp;AI has helped spur a shift from rule-based machine vision to AI-powered inspection, which has allowed machine vision to handle tasks that previously required human inspectors. For instance, cosmetic defects can be challenging for rule-based machine vision to detect, but AI-powered machine-vision systems learn autonomously by analyzing multiple examples of defects, interpreting data, learning from patterns and making decisions. Machine vision also allows companies to better comply with regulations and enhance worker safety on the factory floor.<h2>China, AGVs and AMRs</h2>We think the warehouse-automation market is at a critical inflection point, transitioning from rigid, fixed-path Automated Guided Vehicles (AGVs) to intelligent, flexible Autonomous Mobile Robots (AMRs). This shift has been propelled by the rapid growth of e-commerce, critical labor shortages, and the strategic need for supply-chain resilience.&nbsp;We think the total addressable market for warehouse-automation systems will increase by ~11% CAGR to some $112 billion in 2029, and one IPO prospectus leads us to expect about 20% of this total addressable market to fall into AGVs and AMRs.&nbsp;China’s national strategies have provided a powerful impetus for the development of intelligent logistics and robotics, creating a uniquely favorable environment for domestic companies. Policy support extends beyond production subsidies to the more ambitious goal of creating an entire industrial ecosystem. This state-led industrial strategy provides a significant long-term advantage over competitors in nations lacking a coordinated industrial policy, and its success is already evident: In 2024, Chinese AMR solution providers accounted for about half of the global market share. This has created a virtuous cycle, with state support ultimately driving technological maturity and global expansion.But significant barriers to adoption of AMRs exist. These include high upfront capital costs, complex integration with legacy systems, and a critical labor-skills gap. One major technical challenge is integrating AMR systems with existing Warehouse Management Systems (WMS) and Enterprise Resource Planning software. A critical issue is the speed mismatch: Robots operate at millisecond-level speeds while many legacy WMS platforms lack real-time processing capabilities. Automation also requires a fundamental shift in the workforce from manual labor to technical roles involving robotic systems’ operation and maintenance.We think the global AMR market could surge 33% to some $22 billion in 2029, with the market continuing to bifurcate. AGVs will remain relevant for simple, repetitive, point-to-point transport tasks in stable manufacturing environments, but the high-growth, high-margin opportunities will be concentrated in dynamic, complex environments such as e-commerce fulfillment, with such environments demanding the flexibility and intelligence offered by AMRs.We see the future of competition less about robotic hardware than about the software ecosystem orchestrating that hardware, and we think the long-term winners will be the companies that master the software, AI and integration complexities of modern logistics. AMR pure plays are best positioned to lead the industry into its next phase, thanks to software-native DNA and their proven success in the most demanding fulfillment environments. The key strategic question for incumbents is whether they can make a successful transition from manufacturing hardware to providing integrated software and solutions before the technology gap becomes insurmountable.

Our new report, Embodied Intelligence: The Rise of Physical AI, also discusses stock implications of these trends. It’s available in full to existing Citi Research clients <a href="https://www.citivelocity.com/t/eppublic/4C2ev" target="_blank">here</a>.

We expect Physical AI in industrial markets to accelerate in the coming years, with opportunities in the cloud and at the edge. As data are AI's fuel, we see large data-gathering physical installed bases and domain-specific know-how as key enablers (and moats) for industrial companies.

Embodied Intelligence: The Rise of Physical AI

Transcript 

In this episode of Citi Institute podcast, host Alex Miller is joined by Logan Kilpatrick, Product Lead, Google DeepMind and Shobhit Varshney, Head of AI, Citi. Together, they explore the rapid growth of AI models, how enterprises can position themselves to leverage the exponential progress, and the importance of embedding robust safeguards and governance.Logan Kilpatrick discusses how emerging AI capability innovations will continue to unlock a wide range of new use cases, keeping everyone on their toes.Shobhit Varshney emphasizes the need for a disciplined approach to driving, efficiency, and delivering exceptional experiences for clients and colleagues.Together, they reflect on the future of AI and its ability to empower individuals and organizations to augment their capabilities while maintaining appropriate safeguards and controls. &nbsp;Citigroup (“Citi”) and Google are not affiliated and are independent of each other. Google maintains a client relationship with Citi through Citi Cloud Technology Services. The speakers’ views are their own and may not necessarily reflect the views of Citi or any of its affiliates. All opinions are subject to change without notice. Neither the information provided nor any opinion expressed constitutes a solicitation for the purchase or sale of any security. The expressions of opinion are not intended to be a forecast of future events or a guarantee of future results.

<ul><li>The Transformative Power of Gemini 3 and "Infinitely Remixable" AI involves the ability to take various forms of input—images, emails, videos—and combine or extend that information in novel ways. </li><li>Comprehensive Platforms and Responsible AI involves a disciplined approach to driving growth, efficiency, and exceptional experiences for clients and colleagues, while embedding responsible AI principles, risk guardrails, and controls from ideation to scale. </li><li>AI development is moving away from specialized, piecemeal solutions towards integrated, multimodal models like Google's Gemini 3.&nbsp;</li></ul>

Headshot of Logan Kilpatrick leads the product team for Google AI Studio and the Gemini API at Google DeepMind. 

Logan Kilpatrick works on Google AI Studio and the Gemini API at Google DeepMind, where he leads the team dedicated to enabling developers worldwide to build with Gemini, Veo, Imagen and more.

Logan Kilpatrick, Product Lead, Google DeepMind 

Shobhit Varshney is Head of AI at Citi where he leads the firm’s global AI strategy, execution, adoption and value creation at scale. Reporting to the Chief Operating Officer, he partners closely with Citi’s Executive Management Team, the technology organization and leaders across the firm to deliver measurable business outcomes – enhancing client and colleague experiences, strengthening controls, and boosting productivity through responsible AI usage.&nbsp; Prior to Citi, Shobhit served as Head of Data &amp; AI for IBM Consulting across the U.S., Canada, and Latin America. Over his 13 years at IBM, he spearheaded large-scale data and AI transformations, helping enterprises unlock the potential of AI by grounding it in data and automation. Collaborating with a broad network of technology partners, he focused on translating AI innovation into trusted, real-world business impact. A patented AI innovator, keynote speaker, and recognized industry thought leader, Shobhit has received multiple accolades, including Technology Leader of the Year. Shobhit holds a Master of Science (M.S.) in Computer Science (Computational Bio-Medicine) and a Master of Business Administration (M.B.A.) in Strategy and Finance from Cornell University. He earned his Bachelor of Science (B.S.) in Computer Engineering with a Math minor from the University of Arkansas at Fayetteville. Shobhit lives in Dallas with his wife and two young children. Beyond work, he is passionate about photography and brewing craft beer.&nbsp;

Shobhit Varshney, Head of AI at Citi 

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Alex Miller is joined by Logan Kilpatrick, Product Lead, Google DeepMind and Shobhit Varshney, Head of AI, Citi. Together, they explore the rapid growth of AI models, how enterprises can position themselves to leverage the exponential progress, and the importance of embedding robust safeguards and governance.

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Evolution of AI at Google and Citi

<ul><li>Smart glasses look set to become the next fast-growing AI edge device, disrupting the traditional eyewear industry as its leading players cooperate with tech companies</li><li>We see shipments of smart glasses growing by a 105% compound annual growth rate through 2030, with shipments reaching 112 million, as market revenue grows at a 112% CAGR to $40 billion</li><li>Smart-glasses development faces a number of challenges, ranging from semiconductor and battery capabilities to the need to create advanced displays compatible with prescription lenses &nbsp;</li></ul>

In a new Super-Sector Analysis led by Kyna Wong and a team of analysts, Citi Research explores the technology behind AI glasses, the challenges that must be overcome their development, and the potential they offer.Despite challenges, AI glasses look set to become the next fast-growing AI edge device, disrupting the traditional eyewear industry as technology companies bring innovative products, distribution channels and marketing resources to the mix. Part of this disruption should include transformation of traditional eyewear companies or channels such as EssilorLuxottica, Warby Parker and the like, as traditional eyewear companies with the resources cooperate with tech players.We forecast shipments of smart glasses will grow by a 105% compound annual growth rate (CAGR) from 2024 to 2030, with shipments reaching 112 million, while market revenue could grow at a 112% CAGR to $40 billion. Major players include EssilorLuxxottica-Meta, Google and Apple. With smart glasses designed for AI, providers of Large Language Models are likely to play an important role.Generally speaking, there are three types of AI glasses: ones with a microphone and/or camera, such as the Amazon Echo Frames Gen 3; ones with multimodal AI, such as Meta Ray-Ban; and ones with in-lens displays, such as the Meta Ray-Ban Display. Based on hardware capabilities, we see AI smart-glasses migration as starting either with audio or camera glasses, followed by AI glasses with in-lens displays. Eventually, smart glasses are likely to be all-in-one devices equipped with more sensors, such as built-in eye tracking and gesture-recognition features, and the display may be upgraded to high-definition resolution. We see incremental hardware design depending on the device’s AI capability and the complexity of interactions. We estimate optical components could account for 40% to 70% of the overall bill of materials for a pair of full-feature smart glasses, compared with likely less than 20% for camera AI glasses.&nbsp;<h2>Six challenges</h2>We identify six challenges in smart-glasses development.Semiconductors are a bottleneck, as smart glasses have key requirements for weight, power efficiency, form factor and the like. Currently there are few optimal chipsets for smart glasses, with many smart-glasses companies using smartphone chipsets, which aren’t the best fit. New chips will likely emerge for smart glasses, with some integrated-circuit designers already jumping in.Battery life is another issue. Smart glasses’ audio functions can typically last all day on a single charge, but camera and video functions such as video encoding and wireless streaming demand quite a lot of battery power.Design is important for appeal and social acceptance, and we think earlier attempts to create popular smart glasses likely failed primarily for these reasons — glasses are essentially fashion accessories. In this context, we see a crucial collaboration linking traditional tech companies and companies with known fashion brands and expertise in eyewear design: EssilorLuxottica’s tie-up with Meta is a prime example, as is Google’s announced partnership with Kering Eyewear and Gentle Monster.Standard operating systems are needed for smart glasses, as was the case with smart watches — witness WatchOS, Android Wear OS and the like.Display questions abound. Optical engine, waveguide and virtual image distance (VID) lens processing could be challenging. We see compatibility with prescription lenses as critical for widespread adoption: We think one of the drivers of uptake for Ray-Ban Meta is its ability to facilitate all-day wear, which wasn’t necessarily the case with previous models on the market. Technology for light engines — miniature projectors that create images — is still evolving, with challenges including field of view, cost, power consumption, form factor and the reliability of the light source.Eyewear channels are a challenge. We expect smart eyewear to be sold across multiple platforms — including, crucially, optical shops to facilitate the inclusion of prescription lenses. We see this channel as an important point of differentiation vs. smart watches, for which customization options are more limited. We see optical shops as the best positioned to promote mass adoption: Prescription lenses and medical eyecare are their bread and butter, and wearers of prescription glasses tend to prefer service and fitting provided by eyecare professionals. Smart-glasses developers who lack partners in the eyecare space will likely need to build relationships with the medical-distribution channel and train medical staff to sell tech products. &nbsp;

Our new report, AI Glasses: The Next Fast-Growing Edge Device, also explores stock implications for the various players in this emerging tech ecosystem. It’s available in full to existing Citi Research clients <a href="https://www.citivelocity.com/t/eppublic/4DqY0" target="_blank">here</a>.

Smart glasses look set to become the next fast-growing AI edge device. Citi Research forecasts smart-glass shipments of ~112 million by 2030, a 105% compound annual growth rate from 2024, and market revenues of $40 billion, a 112% CAGR. We explore this technology and its potential in a new Super-Sector Analysis.

AI Glasses: The Next Fast-Growing Edge Device

<ul><li>New AI tools could potentially disrupt the music industry by letting consumers stop being merely listeners and start being active content creators</li><li>We see digital service providers (DSPs) as more likely to embrace this opportunity, while record labels are more likely to play defense</li><li>On the M&amp;A front, we think DSPs are better candidates to acquire AI music start-ups than labels</li></ul>

A new Citi Research report from analysts led by Jason Bazinet explores how artificial intelligence (AI) could impact the music industry, including the possibility that AI tools could let consumers migrate from the role of music listeners to active content creators.To assess the implications, we’ve divided the music industry into three groups: AI-centric music start-ups such as Suno, Udio and KLAY Vision; major record labels Warner Music Group, Universal Music Group and Sony; and digital service providers (DSPs) such as Spotify, Apple and Amazon.While we see AI as offering both risks and opportunities for the music industry, we think if left unchecked, the risk-reward profile skews negatively for labels and positively for DSPs. We see labels as more exposed to downside from headwinds such as the erosion of intellectual-property (IP) value and monetization challenges, while we think AI brings a myriad of opportunities for DSPs, including content creation, enhanced features and cost efficiencies.We see the bulk of AI music start-ups as focused on content creation, with some helping to create brand new content and others looking to enhance existing content. For AI start-ups, licensing arrangements with record labels remains a key focus, as major labels have proved assertive in pursuing copyright-infringement litigation. &nbsp;As AI start-ups expand, record labels face potential risks from lost market share, though they could benefit from licensing arrangements and music-creation efficiencies. But if left unchecked, we see the challenges for the labels outweighing the potential opportunities.&nbsp;So far, Warner Music Group stands alone in signing licensing deals with the three biggest AI music start-ups, possibly signaling a more collaborative approach. While Universal Music Group appears to have more AI agreements than any other labels, we suspect it’s been more restrictive in its deal-making, seeking stronger safeguards to protect its IP and guard against low-quality AI content. Sony, meanwhile, has taken the most conservative approach, with minimal licensing or partnership activity.<h2>Changing industry flows</h2>Before the rise of AI, the value flows for the music industry were simple: 1) artists created music; 2) labels promoted it; 3) DSPs distributed it; and 4) consumers listened to it.&nbsp;But now AI tools can be used by the artist, the DSP or the consumer. (We can’t think of many use cases for the labels.) Of these three constituencies, we find consumer use of AI perhaps the most interesting as well as potentially disruptive. That’s because AI allows consumers to migrate from passive listeners to music to active creators of it. In effect, AI makes all consumers potential content creators.Unlike the labels, we see DSPs as apt to see greater benefits than risks from AI. Despite concerns about AI-generated content flooding platforms, we think DSPs have a significant first-mover advantage in distribution, and we suspect their share loss to emerging AI-focused DSPs will likely be minimal.&nbsp;On the opportunity side, DSPs are actively leveraging AI to improve R&amp;D efficiency and enhance the user experience. We think AI-driven feature enhancements will let platforms improve engagement and retention, and suspect AI could deepen engagement with DSP platforms and/or unlock new revenue streams as consumers become content creators.Looking ahead, we see DSPs as better candidates to acquire AI music start-ups (especially ones focused on content creation) than labels. We see DSPs as having strong incentives to integrate AI to help differentiate their platforms, while labels are more likely to prioritize IP control and licensing.&nbsp;

Our new report, AI May Turn Consumers From Passive Listeners to Content Creators, is available in full to existing Citi Research clients <a href="https://www.citivelocity.com/t/eppublic/4GCnU">here</a>.

Disruption for the music industry could come from AI tools that let consumers migrate from mere listeners to active content creators. A new Citi Research report looks at the possibilities; we see digital service providers as apt to lean into this opportunity, while record labels play defense.

AI May Turn Consumers From Passive Listeners to Content Creators

<ul><li>Citi Research identifies AI as a primary megatrend driving substantial growth for industrial companies in power thermal and electrical management sectors positioning them as key AI enablers</li><li>Despite potential capacity constraints AI capital expenditure continues to fuel strong double-digit revenue growth and operating margin expansion for industrials with peak investment still years out</li><li>Beyond direct AI investments, a broader industrial rebound is emerging supported by deglobalization "build in America" initiatives and fiscal policies broadening market opportunities</li></ul>

Anne Malone, North America Head of Research, sits down with Andrew Kaplowitz, Head of U.S. Industrials Research, to discuss themes shaping Industrials in 2026. The lead theme is AI, with capex growth permeating everything in the sector. But topics from the broadening thesis to M&amp;A and U.S. federal policy are also attracting investor attention.This podcast contains thematic content and is not intended to be investment research, nor does it constitute financial, economic, legal, tax, or accounting advice. This podcast is provided for information purposes only and does not constitute an offer or solicitation to purchase or sell any financial instruments. The contents of this podcast are not based on your individual circumstances and should not be relied upon as an assessment of suitability for you of a particular product, security, or transaction. The information in this podcast is based on generally available information, and although obtained from sources believed by Citi to be reliable, its accuracy and completeness are not guaranteed. Past performance is not a guarantee or indication of future results. This podcast may not be copied or distributed, in whole or in part, without the express written consent of Citi. Copyright 2026, Citigroup Global Markets, Inc. Member SIPC. All rights reserved. Citi and Citi and Arc Design are trademarks and service marks of Citigroup, Inc. or its affiliates and are used and registered throughout the world.

Anne Malone was named Head of North America and Latin America Equity Research 2022; promoted from Deputy Director, a role she held since 2018. Anne started at the firm in 1996 as an Analyst covering Medical Technology stocks. She has held several positions since then, in both Equity Research and Citi Asset Management, taking on roles that utilized her analytical, product, and people skills. Prior to joining Citi she was a Medical Device Analyst at Bear Stearns. Anne graduated from the St Leo College with a degree in Finance and a minor in Economics. She is a CFA Charter holder. &nbsp;

Anne Malone, Head of North America and Latin America Equity Research

Andy Kaplowitz is a Managing Director and U.S. Industrial Sector Head covering the Multi-Industry / Conglomerates and Engineering &amp; Construction sectors. Prior to joining Citi Research in 2015, Andy had several roles in equity research for Barclays/Lehman Brothers from 2001 through 2015. Andy most recently was a Managing Director covering Machinery and Engineering Construction at Barclays since 2011. Prior to that, Andy was a Senior Analyst covering Engineering and Construction stocks since 2006 and Mining Equipment since 2008. Andy has been a Top 3-ranked analyst in Engineering and Construction from 2009 through 2016 in Institutional Investor’s annual All America Research Poll and also held a runner-up position in Machinery from 2012 through 2014. &nbsp;Andy holds a Bachelor of Arts degree from Rutgers University and an MBA degree from the Kellogg School of Management. &nbsp;

Andrew Kaplowitz, Head of U.S. Industrials Research

Anne Malone, North America Head of Research, sits down with Andrew Kaplowitz, Head of U.S. Industrials Research, to discuss themes shaping Industrials in 2026. The lead theme is AI, with capex growth permeating everything in the sector. But topics from the broadening thesis to M&amp;A and U.S. federal policy are also attracting investor attention.

/rcs/citigpa/storage/public/Episode-65-TRANSCRIPT.pdf

E65: You Can’t Spell Industrials Without “AI"

<ul><li>India could become a leader in applied AI by increasing investment and focus on vertical applications such as healthcare and public services and driving national-level adoption</li><li>The country’s ambition goes beyond adoption and usage to include developing models and investing in data center infrastructure</li><li>There is increasing top-down interest in adopting AI but most organizations remain at an early stage</li></ul>

<h2>What were your key takeaways from the summit?&nbsp;</h2>That India aims to be a leading global player in building and scaling agentic AI ecosystems, with a focus on infrastructure, innovation and impact at national scale. My panel explored how tokenization could help accelerate the adoption of agentic AI systems in payments. What is agentic AI? It is generative AI plus execution. That means not just producing insights or content, but undertaking various tasks on behalf of the user, such as connecting to databases and updating them, drafting and sending emails on your behalf, integrating with downstream systems, and moving beyond isolated tasks to complete end-to-end processing.

The focus now is on capital and infrastructure. Another important development is international collaboration. India is looking to deepen its partnership with France, particularly given France’s strength in foundation models such as Mistral AI."

<h2>India wants to be a leading force in AI. What does this ambition mean in practice?</h2>Prime Minister Narendra Modi attended the summit in person and showcased India’s domestic AI capabilities. There was real energy on the ground, with plenty of entrepreneurs discussing their agentic AI frameworks and applications. The talent base is clearly there. The focus now is on capital and infrastructure. Another important development is international collaboration. India is looking to deepen its partnership with France, particularly given France’s strength in foundation models such as Mistral AI.More broadly, there is a dual-track approach. First, there is a clear drive at the national level to establish partnerships between Indian organizations, such as consultancies and conglomerates, and global foundation model providers to expand their offerings. Second, sustained investment in AI and digital infrastructure is needed to ensure India becomes a major AI ecosystem hub in the coming years. Whether current investment levels are sufficient remains an open question, especially given the gap with the U.S. and China. Some stakeholders think that India must move faster. By increasing investment and focus on vertical applications such as healthcare and public services, and driving national-level adoption, the country could become a leader in applied AI.<h2>You have met a wide range of corporates across Delhi, Mumbai and Bangalore to discuss AI. How are they adopting AI and adapting their business models?</h2>We met over 30 corporates this week, and in total more than 80 individuals across treasury, finance and technology teams in Delhi, Mumbai and Bangalore. The mix was broad, spanning technology, consulting, corporate and commercial banking clients. In terms of use cases, there is a common pattern. Almost everyone wants to use AI, and adoption levels are broadly similar. Most are focused on uses that I would summarize as TEAG-CC (Translate, Extract, Analyze, Generate, Compare and Code). Teams want access to the tools and want to roll them out more widely across their organizations, especially within treasury and finance.There is increasing top-down interest in adopting AI. One treasurer noted that discussion meetings with their CFO on automation and AI have shifted from a quarterly to a weekly basis. That said, most organizations remain at an early stage. Functions such as treasury and finance are still working out how to move beyond basic prompting. &nbsp;What is not happening yet is important. Only one client mentioned that they have actively experimented and moved beyond TEAG-CC, into fully embedded, revenue-generating AI applications. Revenue-linked uses are still limited, and most firms are relying on third-party foundation models, often developed outside India.<h2>Can you name one key takeaway from your week in India?</h2>India wants to be a leading force in AI. There was a strong statement of intent to rank among the top three players in the AI ecosystem by 2047, to coincide with the country’s 100th year of independence. This ambition goes beyond adoption and usage. It includes developing models and investing in data center infrastructure. As the name of the summit suggests, the emphasis is firmly on impact. Given India’s scale, the focus is on deploying agentic AI in a way that benefits the broader population, ensuring inclusion and avoiding digital exclusion. Today, models, data and many applications are largely dominated by the West.At the same time, what struck me equally was the bottom-up momentum. There is no shortage of talent. At an individual level, there are many capable engineers and entrepreneurs who want to build and deploy these models. The real challenge is building or refining the full stack, from hardware inputs and data center capacity to GPU access, data availability and, ultimately, scalable use cases. The enthusiasm is real, the intent is serious and the opportunity may be significant, but it requires coordination across the stack.

The annual AI Impact Summit, which began in 2023 in the UK, was recently held in New Delhi, India. Attended by leading technology firms, banks, policy makers and political leaders (including India’s Prime Minister Narendra Modi and French President Emmanuel Macron), it was the first time the summit took place in the Global South. Citi Institute’s Dr. Prag Sharma was a panel contributor, speaking on agentic AI and its implications for 2026. Ronit Ghose, the Head of Citi Institute’s Future of Finance team, caught up with Prag.&nbsp;

India Doubles Down on AI Push

<ul><li>Understanding the impact on the global labor workforce, where AI agents take on more tasks and agency, fundamentally changing the structure of the economy and GDP over the next 5-10 years</li><li>The importance of understanding the shift in "moats" or competitive advantages, as software alone is no longer sufficient and the physical infrastructure and manufacturing capabilities are becoming more critical</li><li>The potential breakthroughs in quantum computing, which could significantly expand the capabilities of AI systems by enabling simulations and explorations of the physical world that are not possible with classical computing</li><li>To navigate this rapid change, it is important to develop a "deconstructionist" mindset, breaking down the key components and building frameworks to analyze the data and connections between them</li></ul>

In this episode of Citi Institute podcast, host Alex Miller is joined by Tony Kim, Global Technology Team Head, BlackRock and Alex Watkins, Global Head of Technology Capital Markets, Investment Banking, Citi. Together, they explore how AI and the ‘Intelligence Revolution’ are the single biggest drivers of growth, and how the shift in value is moving from the top layer of software and labor services to the underlying computing infrastructure and AI models.Tony Kim discusses breakthrough novelty and original invention as capabilities AI currently struggles to replicate.Alex Watkins emphasizes that in certain areas of tech, being the lowest cost marginal producer will be key to winning, not just innovation and market position.Together, they reflect on exciting opportunities and potential breakthroughs in space, quantum computing, and agentification. &nbsp;Citigroup (“Citi”) and BlackRock are not affiliated and are independent of each other. BlackRock maintains a client relationship with Citi through Citi Investment Banking. The speakers’ views are their own and may not necessarily reflect the views of Citi or any of its affiliates. All opinions are subject to change without notice. Neither the information provided nor any opinion expressed constitutes a solicitation for the purchase or sale of any security. The expressions of opinion are not intended to be a forecast of future events or a guarantee of future results.

Tony Kim, Managing Director, is Head of the Global Technology Team within the Fundamental Equities division of BlackRock’s Portfolio Management Group. He is lead portfolio manager of the BlackRock Technology Opportunities Fund and related global strategies.Mr. Kim joined BlackRock in 2013. His 30 years of experience in technology investments also includes key roles at Artisan Partners, Credit Suisse Asset Management, Neuberger Berman, Merrill Lynch and SG Warburg.Mr. Kim is a member of the Advisory Board for Columbia Business School’s Digital Future Initiative, which brings together Columbia faculty members and leaders from across industries to help organizations, governments and communities optimize and accelerate the technological advances of the future.Mr. Kim earned a BS degree in Industrial Engineering from the University of Illinois and an MBA from Columbia University.

Tony Kim. Head of Fundamental Equities’ Global Technology Team, Portfolio Manager, BlackRock

Alex Watkins joined Citi in October 2025 as Global Head of TechnologyCapital Markets–leading Citi’s efforts across debt and equity.Prior to Citi, Alex was the Co-Head, International Equity Capital Markets at J.P. Morgan, where he worked for the last 11 years. This group represented all of J.P. Morgan’s international ECM businesses –Europe, Middle East and Asia Pacific.Prior to that Alex worked at Goldman Sachs. He has over 20 years of investment banking experience, having had leadership roles in both ECM and corporate finance.Alex has worked on over 200+ capital market transactions. Since joining Citi, recent deals include the IPOs of Wealthfrontand Navan, primary / second follow on for Symbotic, convertible bonds for IREN, Checkpoint and Coreweave.

Alex Watkins, Global Head of Technology Capital Markets, Investment Banking, Citi 

How Do You Win When Software Is No Longer the Moat?

In this episode of Citi Institute podcast, host Alex Miller is joined by Tony Kim, Global Technology Team Head, BlackRock and Alex Watkins, Global Head of Technology Capital Markets, Investment Banking, Citi. Together, they explore how AI and the ‘Intelligence Revolution’ are the single biggest drivers of growth, and how the shift in value is moving from the top layer of software and labor services to the underlying computing infrastructure and AI models.

Where the Value Moves

Download Report 

Supercharged - AI and the New Age of Disruption

Technology has always had the power to reshape societies. But the current wave of disruption – driven by advances in artificial intelligence – is unfolding at dizzying pace and on an unprecedented scale. Unlike previous technological revolutions, which generally took decades, AI is firing change across industries, institutions and societies in real time. It’s a challenge just to keep up.This Citi Institute report, which accompanies Citi’s DisrupTech conference being hosted in London in March, focuses on a few areas of disruptive technology, taking in innovations themselves, but also the shifts they will drive.&nbsp;

<ul style="list-style-type:disc;"><li>Autonomous driving and humanoids: Two of the biggest technological shifts gaining momentum today are autonomous mobility and the rise of humanoid and physical AI-enabled robots. They represent a structural innovation cycle with far-reaching implications for productivity, demographics, capital allocation, labor markets and competition.</li><li>NextGen AI companies: The new wave of AI software companies are likely to focus on integrated and agentic AI platforms and huge investments in computing infrastructures, networking and data pipelines. The challenge will be to scale responsibly.</li><li>The rise of quantum computing: Three themes dominate long-term strategic positioning: the physical limits of classical computing and the need for new architectures, the infrastructural intensity of AI scale-up and the disruptive trajectory of quantum capability. &nbsp;</li></ul>

Report Highlights 

AI and the New Age of Disruption

Technology has always had the power to reshape societies. But the current wave of disruption – driven by advances in artificial intelligence – is unfolding at dizzying pace and on an unprecedented scale. Unlike previous technological revolutions, which generally took decades, AI is firing change across industries, institutions and societies in real time. &nbsp;&nbsp;

Supercharged

Related Content:<ul><li><a href="https://www.citi.com/ventures/perspectives/opinion/agentic-pricing-framework-value-roi.html">Pricing agentic AI: Working toward a framework for value and ROI</a></li><li><a href="https://www.citi.com/ventures/perspectives/opinion/agentic-pricing-models-enterprise-startups.html">Q&amp;A: Agentic pricing - Startups weigh in on the top question on everyone’s mind</a></li><li><a href="https://www.citi.com/ventures/perspectives/opinion/agents-as-a-service-evolution.html">The dawn of Agents as a Service: Where the market stands as we near the midpoint of 2026</a></li><li><a href="https://www.citi.com/ventures/perspectives/opinion/ltms-large-tabular-models-startups-enterprise-2026.html">Large tabular models: A new approach to AI with significant promise</a></li><li><a href="https://www.citi.com/ventures/perspectives/opinion/agentic-company-startups-role-enterprise-success.html">The&nbsp;agentic company: How startups are leading the way</a></li><li><a href="https://www.citi.com/ventures/perspectives/opinion/gen-ai-in-2026-reasoning-capabilities.html">Generative AI in 2026: Moving beyond infrastructure into the world of reasoning capabilities</a></li><li><a href="https://www.citi.com/ventures/perspectives/opinion/agentic-commerce-why-startups-are-key.html">Agentic commerce&nbsp;at&nbsp;scale: Why startups are key in building robust AI-powered shopping experiences</a></li></ul>&nbsp;&nbsp;

This Summit is designed for the most influential companies, thought leaders, and investors at the forefront of artificial intelligence, and offers a unique platform to engage with an elite network of peers and investors, explore strategic opportunities, and engage in the pivotal conversations defining AI's next era.

2026 Citi AI Summit

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