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The Twin Engines of AI: How Computer Vision and LLMs Are Reshaping the World

Published

2 months ago

July 6, 2025

Introduction

Ever feel like technology is learning superpowers overnight? One day your phone is just taking photos; the next it’s unlocking itself by recognizing your face. Ask a simple question online, and instead of a list of links, you get a paragraph-long answer as if from a knowledgeable friend. These magic tricks are powered by the twin engines of modern AI: computer vision and large language models (LLMs). Computer vision (CV) gives machines the ability to see and interpret the visual world, while LLMs let them understand and generate human-like language. Individually, each is a marvel. Together, they’re like peanut butter and jelly – different flavors that complement each other to create something even more amazing. In an era of smart assistants and self-driving cars, these two technologies are reshaping how we live, work, and play, often in ways we don’t even realize.

AI is evolving with “eyes” (computer vision) and “voice” (language models), enabling devices to perceive and communicate with the world.

It’s 2025, and the rise of computer vision is all around us. In “Rise of Computer Vision,” I highlighted how machines interpreting images – from selfies to self-driving cars – went from academic experiments to everyday utilities. Meanwhile, the chatbot boom has made LLMs a household term. Language models like GPT-4 have read basically the entire internet and can chat with you, write stories, or answer complex questions as if they were human experts. In fact, we’re witnessing a trend where these two AI fields are the hottest developments of recent years. They’re the twin engines of AI, propelling innovations across industries at breakneck speed. Tech giants and startups alike are racing to harness both: one engine to see the world, the other to understand and explain it. This blog dives into why computer vision and LLMs are such a big deal, how they complement each other in real life (from factory floors and doctor’s offices to your living room), and what it all means for people like you and me.

Human-like Senses

By Husna Miskandar on Unsplash

Take a step back, and you’ll notice a pattern: the biggest AI breakthroughs lately have come from teaching machines human-like senses. Vision and language are two fundamental ways we humans navigate our world, so it’s no surprise that giving these abilities to machines has unleashed a wave of innovation. Over the past decade, computer vision and LLMs have each matured dramatically. Vision AI went from barely identifying blurry shapes to superhuman image recognition. (No joke – some algorithms now spot tumors or street signs better than people can.) Similarly, LLMs evolved from clunky text generators to eerily fluent conversationalists. If 2015 was the year of big data, and 2020 was all about cloud computing, then 2023-2025 is the era of CV and LLMs.

Why now? In short, better tech and bigger data. On the vision side, breakthroughs in deep learning (in particular, neural networks that mimic how our brain’s visual cortex works) turbocharged image processing. At the same time, cameras got insanely cheap and ubiquitous – there’s likely one on your doorbell, your laptop, and definitely in your pocket. On the language side, researchers figured out that feeding massive neural networks humongous amounts of text (think billions of webpages and books) produces a model that starts to grasp the nuances of language. The result: LLMs that can compose emails, summarize reports, or hold a conversation about almost any topic. Importantly, these aren’t isolated developments. A major trend in AI is convergence – combining different capabilities. We see voice assistants that can also use a camera, or search engines that answer with a generated paragraph instead of links. The cutting edge of AI is all about blending modalities, essentially creating “AI fusion” cuisine. As someone who mapped out the “Rise of AI Agents” in an earlier article, I can tell you that today’s AI agents often owe their smarts to both vision and language working in tandem. The trend is clear: the coolest applications of AI now tend to be the ones that see what’s happening and then talk about it or act on it. Let’s break down each of these twin engines and see how they rev up different parts of our lives.

Computer Vision – Teaching Machines to See

If you’ve ever marveled at how Facebook tags your friends automatically in photos or how your iPhone magically sorts pictures by location or person, that’s computer vision in action. Computer vision (CV) is the field of AI that enables computers to interpret images and videos – essentially giving them eyes. And those eyes are everywhere now. As I described in “The Rise of Computer Vision,” what started as niche research has exploded into a technology that touches daily life and business in myriad ways.

In industry, CV has been a game-changer on the factory floor. Picture a manufacturing line where products whiz by under high-speed cameras. Ten years ago, a human inspector might catch one defective widget out of a thousand (and need coffee afterward). Today, an AI-powered camera system can examine each item in milliseconds, 24/7, never getting tired or distracted. It’s like having an army of tireless inspectors with perfect eyesight. In fact, in “Computer Vision’s Next Leap: From Factory Floors to Living Rooms,” I noted how these AI “eyes” have become standard in manufacturing and logistics. Robots in Amazon warehouses use vision to navigate and pick items, scurrying around like diligent little ants that recognize boxes and barcodes on the fly. Quality control, inventory tracking, assembly line safety – CV is supercharging all of it. And as usually happens with tech, what started in big industries is now trickling down to consumers.

How is CV impacting you at home? Chances are you’ve already used it today. Did you unlock your phone with your face? That’s your phone briefly playing security guard, matching the live image of you to the stored model of your face. Applied to consumer tech, CV ranges from the playful to the profound. Those fun filters on Instagram or Snapchat that put dog ears on your selfie or swap your face with a celebrity’s – they rely on computer vision to track your facial features in real time. It’s serious tech doing silly things, but hey, it brings joy! On a more practical note, think of augmented reality (AR) apps: you point your phone at an empty corner of your room, and IKEA’s app shows a digital couch fitting right in – that’s CV understanding your space and AR overlaying info. Or consider healthcare apps: there are apps now where you can take a photo of a mole on your skin and an AI will assess if it looks potentially concerning. In “Industrial Eyes” and the healthcare section of my CV articles, I wrote about AI that can catch medical details doctors might miss, like subtle patterns in an X-ray or MRI. That same tech is now available in your pocket as a dermatology app or a fitness app that counts your exercise reps via the camera. We’re basically giving everyone a mini doctor or personal trainer in their phone, powered by CV. From retail to security to education, machine vision is quietly making devices smarter. Home security cams can distinguish between a stray cat and a person at your door (so you don’t get 50 motion alerts for raccoons). Shopping apps can visually search – snap a picture of those cool sneakers you saw on the street, and an app finds you similar ones online. It’s not sci-fi; it’s here and now. The bottom line: computer vision has matured to the point that machines can reliably see and make sense of the visual world, and it’s changing how we live and work in ways big and small.

Large Language Models – Giving Machines a Voice (and a Brain)

Now let’s talk about the other half of our dynamic duo: large language models, the masters of words. If computer vision is about eyes, LLMs are the “brain” and “voice” of AI, processing text and speech to communicate with us. An LLM is essentially a computer program that has read a ridiculous amount of text and learned to predict what comes next in a sentence. The result? It can generate coherent paragraphs, answer questions, and even crack jokes (occasionally good ones!). In “Stop Patching, Start Building: Tech’s Future Runs on LLMs,” I argued that these models are so transformative that companies need to rethink their approach to software – not just bolting on a chatbot here or there, but rebuilding systems with AI at the core. Why? Because LLMs aren’t just fancy autocomplete; they’re a whole new way for software to interact with humans and handle knowledge.

Think about how we traditionally used computers: you click menus, type exact queries, or follow rigid procedures. With LLMs, suddenly you can just ask or tell a computer what you need in plain English (or Spanish, Chinese – they’re multilingual too!). That’s a sea change in usability. No wonder everyone from Google to your local app developer is racing to integrate ChatGPT-like features. We now have email writers, customer service bots, coding assistants, and even therapy chatbots all powered by LLMs. Have you used an AI to draft an email reply or come up with a meal recipe? That’s an LLM at work, acting like a knowledgeable assistant. People love this because it feels like talking to an expert or a friend rather than using a tool. In fact, it’s become so popular that in 2024 an estimated 13 million Americans preferred asking an AI for information over using a search engine – a trend I explored in “LLMs Are Replacing Search: SEO vs GEO.” Ask ChatGPT for the best backpack for commuting, and it will give you a handy summary of top brands in seconds, saving you a half-hour of Googling and comparing. It’s like the difference between getting a GPS voice telling you exactly where to turn versus unfolding a paper map yourself.

For businesses, LLMs are equally revolutionary. They can read and write at a scale and speed humans simply can’t. Imagine an AI intern that can instantly summarize a 100-page report, draft dozens of personalized customer emails, translate documents, and brainstorm marketing slogans, all before lunch. Companies are deploying LLMs to assist with writing code, to parse legal contracts, and to handle customer chats at midnight. And thanks to these models’ ability to learn from examples, they can even be fine-tuned on a company’s own data to become an expert in, say, insurance policies or medical research. One important point, though: slapping an LLM onto a legacy process can be like putting a jet engine on a biplane – it might add some speed, but you’re not really redesigning the experience to harness the power. That’s why we’re seeing a new crop of AI-native apps and startups. As I noted in “Stop Patching, Start Building: Tech’s Future Runs on LLMs”, the real breakthroughs come when we stop treating LLMs as plug-ins and start building tools around them. A great example is the emergence of AI agents (covered in “Rise of AI Agents” and “The Agentic Revolution: How AI Tools Are Empowering Everyday People”). Instead of just answering questions, an AI agent powered by an LLM can take actions – schedule meetings, send emails, do research – all on its own, because it can interpret commands and carry out multi-step tasks. It’s the difference between a librarian that tells you where the book is and a proactive assistant that goes, checks the book out, reads it, and gives you the summary. In one fell swoop, LLMs have given software a voice to talk to us and a kind of reasoning ability to make decisions with language. They’re not perfect (they can still mess up or “hallucinate” false info), but they are improving quickly. And crucially, they excel when combined with other AI skills – which brings us to the real magic that happens when vision and language meet.

Better Together – AI’s Eyes and Voice Join Forces

On their own, computer vision and LLMs are impressive. But what happens when you put them together? That’s when AI really starts to feel like science fiction come to life. Combining vision and language allows machines to understand context and interact with the world in a more human-like way. After all, we humans rely on multiple senses working together: you don’t only listen to your friend’s words, you also look at their facial expressions; you don’t only see the stove is on, you also read the cooking instructions. In the same way, AI that can both see and converse can tackle far more complex tasks.

Consider the humble smart assistant. Today, devices like Alexa or Google Assistant can hear you and speak, but they’re basically blind. Now imagine a smart assistant with a camera: you could hold up a product and ask “Hey, is this milk still good?” and it could inspect the label or even the milk itself and answer. In fact, such multimodal AIs are already emerging. OpenAI introduced a version of GPT-4 that can analyze images – users showed it a fridge’s contents and asked “What can I make for dinner?” and it figured out a recipe. That’s vision (identifying ingredients) + language (providing a recipe in steps). Google’s latest iterations of search and assistants are heading this way too: you can snap a photo of a plant and ask the AI what it is and how to care for it, all in one go. It’s like having a botanist friend with you who can both see the plant and chat about it. In the “The Agentic Revolution: How AI Tools Are Empowering Everyday People” piece, I talked about AI tools empowering people – a big part of that is this kind of contextual understanding that comes from mixing visual and linguistic intelligence.

In enterprise settings, the combo of CV and LLMs opens up powerful use cases. Think healthcare: An AI system could scan medical images (X-rays, MRIs) using computer vision to detect anomalies, and then summarize its findings in a report or explain them to a doctor in plain language. There are already early signs of this – AI can annotate an X-ray with suspected issues, and LLM tech is being used to draft medical notes. Or consider retail and inventory management: cameras in a stockroom might visually track product levels and detect when something is running low; an LLM-based system could then automatically generate an email to suppliers, in perfect business prose, to reorder those items. The result is an almost autonomous operation, where visual data triggers language-based actions seamlessly. Even in something like finance, envision a scenario where an AI monitors video feeds for fraud or suspicious activity (say, at ATMs or offices via CV) and then dispatches alerts or writes up incident reports using an LLM. Essentially, tasks that used to require hand-offs between separate systems (one to see, one to write) can now be done by a single cohesive AI agent.

Robotics is another domain where vision+language is making waves. A robot that can see is useful; a robot that can also understand spoken instructions or read text is a lot more useful. We’re starting to see service robots and drones that do just this. Imagine a home assistant robot: you point and say, “Please pick up that red book on the table and read me the first paragraph.” For a long time, that was firmly in sci-fi territory. But now the pieces exist: CV to recognize the red book and navigate to it, and an LLM (paired with text-to-speech) to read out the paragraph inside. In tech demos, researchers have shown robots that take commands like “open the top drawer on the left and bring me the stapler” – the robot uses vision to identify the drawer and stapler, and language understanding to parse the request into actions. It’s a bit like a buddy-cop duo where one partner is really observant (CV) and the other is super articulate (LLM); together, they can solve the case that neither could crack alone.

For consumers, one of the coolest emerging examples of this synergy is in augmented reality glasses. Companies are working on AR glasses that will have outward-facing cameras (eyes) and an AI assistant (voice/brain). Picture walking down the street wearing smart glasses: The CV system identifies landmarks, signs, even people you know, and the LLM whispers contextual information in your ear – “The store on your right has a sale on those running shoes you looked at online,” or “Here comes John, you met him at the conference last week.” It sounds wild, but prototypes are in the works. Apple’s Vision Pro headset hints at this future too, blending an advanced CV system (to track your environment and hands) with presumably some language-understanding AI for Siri and interactions. Soon, our devices won’t just respond to our inputs; they’ll proactively assist by seeing what we see and chatting with us about it.

In short, when machines can both see and talk, they become exponentially more capable. This complementary strength is why I dub CV and LLMs the twin engines – one engine gives perception, the other gives comprehension and communication. Together, they enable truly agentic AI: systems that can not only perceive complex situations but also make decisions and take actions in a way we understand. And while this is exciting, it’s also a bit chaotic (in a good way): industries from automotive to education are being reinvented as we find new creative ways to pair vision with language. The interfaces of technology are changing; rather than clicking and typing, we’ll increasingly show and tell our machines what we want. How’s that for a dynamic duo?

Empowering People – AI for the Little Guys (and Gals)

One of the most inspiring aspects of these AI advancements is how they’re empowering everyday people. Not too long ago, cutting-edge AI felt like the exclusive domain of big tech companies or PhD researchers. But with widespread computer vision and language AI, we’re seeing a democratization of tech superpowers. I called this “The Agentic Revolution: How AI Tools Are Empowering Everyday People” – the idea that AI tools are now like sidekicks for normal folks, enabling us to do things that used to require teams of experts. Whether you’re a small business owner, a hobbyist developer, or just someone with a smartphone, the twin engines of AI are leveling the playing field in remarkable ways.

Take small businesses and creators. In the past, if a shop wanted an AI-based inventory system or a smart customer support agent, it was basically impossible without big budgets. Now, even a tiny online store can use off-the-shelf vision APIs to track products (just a few security cams and some cloud AI service) and deploy an LLM-based chatbot to handle customer questions. Solo entrepreneurs are using AI to punch far above their weight. In “The Builder Economy: How Solo Founders Build Fast & Smart,” I shared examples of scrappy developers launching products in a weekend thanks to AI helpers. It’s not hyperbole: a solo founder can plug an LLM (like OpenAI’s API) into their app to handle all the text understanding and generation, and use pre-trained CV models to add features like image recognition, without needing a dozen data scientists. This means faster innovation and more voices bringing ideas to life. The builder economy is indeed transforming how software is made – as explored in “The Builder Economy’s AI-Powered UI Revolution” and “The Builder Economy is Transforming UI Development,” modern tools let you describe what you want, and the AI helps build it. For instance, you might say “I need an app that helps classify plant images and gives care tips,” and much of the heavy lifting (from UI creation to the CV model for identifying plants to the LLM for generating care advice) can be assembled with surprisingly little code. It’s almost like having a junior engineer and designer on your team, courtesy of AI.

This empowerment extends to everyday consumers as well. Consider how accessibility has improved: visually impaired individuals can use apps that see for them and narrate the world. These apps use CV to identify objects or read text out loud, and LLM-like capabilities to describe scenes in a natural way. “You are in the kitchen. There is a red apple on the counter next to a blue mug.” – that level of rich description is life-changing for someone who can’t see, and it’s powered by the combo of vision and language AI. Language translation is another empowering trick: point your camera at a sign in a foreign country, and CV+LLM technology can not only translate the text on the sign but also speak it to you in your native language. Suddenly, travel becomes easier and more fun, like having a personal translator with you.

We’re also seeing individuals leverage these AI tools to learn skills or execute projects that would’ve been daunting before. In the past, editing a video or analyzing a large dataset might require special skills. Now AI can guide you: you can ask an LLM how to do a task step by step, or use a CV tool to automatically tag and sort through thousands of images for your project. There’s a story of a teen who built a home security system that texts her when the mail arrives – she used a Raspberry Pi camera and a vision model to detect the mail truck, then an LLM-based script to send a friendly formatted message. This kind of thing would have been unthinkable for someone without an engineering background just a few years back. But with AI building blocks readily available, creativity is the only real limit.

I’ve written about “What Is Generative UI and Why Does It Matter?”, explaining how AI can even design user interfaces on the fly. This means that not only can individuals use AI, they can have AI customize tools for them. A non-technical founder can literally describe the app interface they want, and a generative UI system (powered by an LLM “designer” and a bit of vision for layout analysis) can produce a working prototype. We’re heading towards a world where anyone can build and customize technology by simply interacting with AI in natural ways. That’s profoundly empowering. It reminds me of giving a person a super-toolkit: suddenly a one-person outfit can reach an audience or solve a problem as if they had a whole IT department or creative team behind them.

Of course, with great power comes great responsibility (and some challenges). As AI gets more accessible, we’ll need to ensure people learn how to use it wisely – verifying AI outputs, avoiding biases, etc. But overall, I see the rise of computer vision and LLMs as putting more power in human hands, not less. It’s enabling us to automate the boring stuff and amplify the creative and personal. The twin engines of AI are not just driving corporate innovation; they’re driving a renaissance for makers, creators, and problem-solvers at every level. Whether it’s a student using an AI tutor that can see their worksheet and guide them, or a farmer using a drone that surveys crops and then advises in plain language about irrigation (yup, those exist), the theme is the same: AI is here to help, and it’s for everyone. That, to me, is the real revolution.

Wrapping It Up

It’s remarkable to think how far we’ve come in just a few years. We now live in a world where machines can see the world around them and talk to us in fluent language. These twin engines of AI – computer vision and LLMs – have transformed what technology can do, and in turn, what we can do. They’ve turned devices into partners: your phone isn’t just a phone, it’s a photographer, a translator, a personal assistant. Your business software isn’t just a database, it’s becoming a smart coworker that can draft reports and spot trends in a dashboard image. We’re still in the early chapters of this story, but it’s clear that these two technologies are driving the plot.

Importantly, computer vision and language models aren’t replacing humans; they’re augmenting us. They take over tasks that are tedious or superhuman in scale (like scanning a million security camera frames or reading every research paper on cancer treatment) and free us up for what we do best – creativity, strategy, empathy. The synergy of AI’s eyes and voice means technology is becoming more intuitive and more integrated into our lives rather than being some separate technical realm. It’s becoming human-friendly. We ask, it answers. We show, it understands.

As we move forward, expect this duo to become even more inseparable. Future AI breakthroughs will likely involve even tighter integration of multiple skills – think AI that can watch a process, learn from it, and then explain or improve it. We might one day have personal AI that knows us deeply: it can see when we look tired and tell us to take a break, or watch our golf swing and literally talk us through adjustments. The possibilities are endless and admittedly a bit dizzying. But one thing’s for sure: the twin engines of AI are on, humming loudly, and they’re not slowing down.

In the end, what excites me most is not the technology itself but what it enables for people. We’ve got tools that would seem magical to past generations, and we’re using them to solve real problems and enhance everyday experiences. From helping doctors save lives to giving grandma a smart speaker that can describe family photos out loud, computer vision and LLMs are making the world a bit more like a whimsical sci-fi novel – except it’s real, and it’s here, and we get to shape where it goes next. So here’s to the twin engines of AI, and here’s to us humans in the pilot seat, exploring this new sky together.

Meta Description: Computer vision and large language models – the “eyes” and “voice” of AI – are propelling a revolution in tech. Discover how these two breakthroughs complement each other in smart assistants, retail, healthcare, robotics, and more, transforming everyday life in a very human way.

FAQ

What is computer vision in simple terms?

Computer vision is a field of AI that trains computers to interpret and understand visual information from the world, like images or videos. In plain language, it lets machines “see” – meaning they can identify faces in a photo, read text from an image, or recognize objects and patterns (for example, telling a cat apart from a dog in a picture). It’s the technology behind things like face unlock on phones, self-driving car cameras, and even those fun filters on social media.

What is a large language model (LLM)?

A large language model is an AI system that has been trained on an enormous amount of text so that it can understand language and generate human-like responses. If you’ve used ChatGPT or asked Siri a complex question, you’ve interacted with an LLM. These models predict likely word sequences, which means they can continue a sentence, answer questions, write essays, or have a conversation. Essentially, an LLM is like a very well-read chatbot that knows a little (or a lot) about everything and can put words together in a surprisingly coherent way.

How do computer vision and LLMs work together?

When combined, vision and language abilities enable much smarter applications. For example, an AI can look at a photo (using computer vision) and then describe it to you in words (using an LLM). This is useful for things like accessible technology for the blind, where an app can “see” the user’s surroundings and talk about them. In robotics, a robot might use vision to navigate and detect objects, and an LLM to understand human instructions like “pick up the blue ball and place it on the shelf.” Together, CV and LLMs let AI systems both perceive the world and communicate or make decisions about it, which is a powerful combo. We see this in action with things like interactive shopping apps (point your camera at a product and ask questions about it) or AI assistants that can analyze charts/graphs you show them and discuss the data.

Where are these AI technologies used in everyday life?

A lot of places! Computer vision is used in everyday life through features like facial recognition (for unlocking devices or in photo apps that sort your pictures by who’s in them), object detection (your car’s backup camera spotting a pedestrian, or a smart fridge identifying what groceries you have), and augmented reality (think Pokemon Go or furniture preview apps). LLMs are in things like chatbots on customer service websites, voice assistants (when they generate a helpful answer rather than a canned phrase), email autocorrect and smart replies, and even in tools that help write code or articles. If you dictate a message and your phone transcribes it, that’s a form of language model at work. Many modern apps have some AI “smarts” under the hood now, whether it’s an AI tutor in a learning app or a feature that summarizes long articles for you. We interact with CV and LLMs often without realizing it – every time Netflix shows you a thumbnail it thinks you’ll like (yes, they use vision AI to pick images), or when an online form corrects your grammar, that’s these technologies quietly doing their job.

What’s next for AI in vision and language?

We can expect AI to become even more multi-talented. One big focus is making multimodal AI that seamlessly mixes images, text, audio, and maybe even other inputs. Future AI assistants might be able to watch a video and give you a summary, or hear a noise and describe what’s happening (coupling sound recognition with language). For computer vision, we’ll see continued improvements in things like real-time video analysis – imagine AR glasses that can label everything you look at in an instant. For LLMs, we’ll likely get models that are more factual and reliable, and specialized models that act as experts in medicine, law, etc. Also, efficiency is a big deal: these systems might run locally on your devices (some phones are already starting to run lightweight versions) so that they work faster and protect privacy. And as these twin engines improve, we’ll probably see new applications we haven’t even thought of – much like how nobody predicted AI-generated art would become a thing so soon. In summary, expect a future where interacting with technology feels even more natural: you’ll be able to show your AI assistant anything or tell it anything, and it will understand and help you as if it truly “gets” the world the way you do. The line between the digital and physical world will blur further, hopefully in ways that make our lives easier, safer, and more enjoyable.

References:

Bandyopadhyay, Abir. *”Rise of Computer Vision.”* Firestorm Consulting, 14 June 2025. Vocal Media. https://vocal.media/futurism/the-rise-of-computer-vision

Bandyopadhyay, Abir. *”Computer Vision’s Next Leap: From Factory Floors to Living Rooms.”* Firestorm Consulting, 1 July 2025. Vocal Media. https://vocal.media/futurism/computer-vision-s-next-leap-from-factory-floors-to-living-rooms

Bandyopadhyay, Abir. *”Rise of AI Agents.”* Firestorm Consulting, 14 June 2025. Vocal Media. https://vocal.media/futurism/rise-of-ai-agents

Bandyopadhyay, Abir. *”The Agentic Revolution: How AI Tools Are Empowering Everyday People.”* Firestorm Consulting, 26 June 2025. Vocal Media. https://vocal.media/futurism/the-agentic-revolution-how-ai-tools-are-empowering-everyday-people

Bandyopadhyay, Abir. *”Stop Patching, Start Building: Tech’s Future Runs on LLMs.”* Firestorm Consulting, 14 June 2025. Vocal Media. https://vocal.media/futurism/stop-patching-start-building-tech-s-future-runs-on-ll-ms

Bandyopadhyay, Abir. *”LLMs Are Replacing Search: SEO vs GEO.”* Firestorm Consulting, 27 June 2025. Vocal Media. https://vocal.media/futurism/ll-ms-are-replacing-search-seo-vs-geo

Bandyopadhyay, Abir. *”The Builder Economy Is Reshaping the Future of Business.”* Firestorm Consulting, 29 June 2025. Vocal Media. https://vocal.media/futurism/the-builder-economy-is-reshaping-the-future-of-business

Bandyopadhyay, Abir. *”The Builder Economy: How Solo Founders Build Fast & Smart.”* Firestorm Consulting, 2 July 2025. Vocal Media. https://vocal.media/futurism/the-builder-economy-how-solo-founders-build-fast-and-smart

Bandyopadhyay, Abir. *”The Builder Economy’s AI-Powered UI Revolution.”* Firestorm Consulting, 18 June 2025. Vocal Media. https://vocal.media/futurism/the-builder-economy-s-ai-powered-ui-revolution

Bandyopadhyay, Abir. *”The Builder Economy Is Transforming UI Development.”* Firestorm Consulting, 18 June 2025. Vocal Media. https://vocal.media/futurism/the-builder-economy-is-transforming-ui-development

Bandyopadhyay, Abir. *”What Is Generative UI and Why Does It Matter?”* Firestorm Consulting, 20 June 2025. Vocal Media. https://vocal.media/futurism/what-is-generative-ui-and-why-does-it-matter

Bandyopadhyay, Abir. *”Move Over, Wall Street: Injective Is Building the Future of Finance.”* Firestorm Consulting, 15 June 2025. Vocal Media. https://vocal.media/trader/move-over-wall-street-injective-is-building-the-future-of-finance

Bandyopadhyay, Abir. *”Build Your Own Bank: How Injective’s iBuild is Revolutionizing Money.”* Firestorm Consulting, 5 July 2025. Vocal Media. https://vocal.media/theChain/build-your-own-bank-how-injective-s-i-build-is-revolutionizing-money

McKinsey & Company. *”The Economic Potential of Generative AI.”* McKinsey Global Institute, 2023.

Gartner. *”AI Chatbots Will Reduce Search Engine Use by 25% by 2026.”* Gartner Research, 2024.

Forbes. *”AI Agents Are Already Changing Everything.”* Forbes Technology Council, 2025.

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Imagining the future of banking with agentic AI

Published

1 hour ago

September 4, 2025

MIT Technology Review Insights

Adapting to new and emerging technologies like agentic AI is essential for an organization’s survival, says Murli Buluswar, head of US personal banking analytics at Citi. “A company’s ability to adopt new technical capabilities and rearchitect how their firm operates is going to make the difference between the firms that succeed and those that get left behind,” says Buluswar. “Your people and your firm must recognize that how they go about their work is going to be meaningfully different.”

The emerging landscape

Agentic AI is already being rapidly adopted in the banking sector. A 2025 survey of 250 banking exec-utives by MIT Technology Review Insights found that 70% of leaders say their firm uses agentic AI to some degree, either through existing deployments (16%) or pilot projects (52%). And it is already proving effective in a range of different functions. More than half of executives say agentic AI systems are highly capable of improving fraud detection (56%) and security (51%). Other strong use cases include reducing cost and increasing efficiency (41%) and improving the customer experience (41%).

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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