Back in 2024, I spent a full week exhibiting at the UK Pavilion at MWC in Barcelona, having over a hundred conversations at my stand. It was a great deep dive, but it taught me that walking the floor at large trade shows is mostly noise unless you have pre-booked meetings with people you simply cannot reach otherwise.

If you look at MWC over the past few years, it has transformed from a deeply technical telecom exhibition into a massive stage for geopolitical debates and digital economy hype. Lately, it’s felt like a “wildebeest stampede” of buzzwords. In 2024, the talk was still grounded in 5G and network automation, with some early curiosity about AI. But by 2025, Gen AI completely overshadowed the show floor. Suddenly, everyone was an “AI company,” but if you scratched the surface, most of it was just basic LLM wrappers and prompt engineering dressed up as revolutionary products.

This year, for MWC 2026, the herd is just changing its banner.

“Gen AI” is out, and “Agentic AI” is the new magic phrase.

You can expect the floor to be flooded with repackaged automation workflows claiming to be “autonomous agents,” alongside buzzwords like “AI-Native Telcos” and premium “Sovereign AI” stacks. There will also be plenty of early 6G hype and digital twins lacking real production data.

If you are heading to Barcelona this year, your goal should be separating the real builders (the “lions”) from the hype-chasers (the “wildebeest”). Don’t get dazzled by flashy demos. Instead, ask exhibitors the hard questions:

1. How do you measure performance beyond the demo?

2. Who owns the data and what does the governance look like? And most importantly,

3. what happens when your AI agent makes the wrong decision?.

If you’re staying home like me, don’t worry about FOMO, you really aren’t missing much. Almost all the big product reveals, walkthroughs, and impressive demos are launched online weeks before the actual event. Plus, after the first thirty chats on the expo floor, the conversations just repeat themselves anyway. You can easily catch up on the actual substance by simply reading the post-event analyst reports.

So what should you do instead?

Just build. A week of focused product development and R&D compounds much faster than a week of random handshakes. Plus, skipping the event saves you anywhere from £5,000 to £15,000 in travel and exhibition costs money that easily covers a month of engineering runway. This advice is of course for the founders, specially those bootstrapping or early stage, for those getting paid to go, please, by all means, go, and get the experience.

Now that we have out of the window, Lets look in to whats happening this week and if there’s some interesting insights that we can get from the event and all the buzz around it so you are covered.

I found it interesting to look in to a bit of history as well, how this primarily a telco gathering has transformed since. plus MWC in a way sets the tone and budget priorities for the telecom and enterprise infrastructure sectors for the next 12–18 months, and which is why its an important event for the telco community. And my doing this deep dive is not exactly being a a founder complaining about trade show costs here, but to try and cut out the noise and build a forward-looking technology thesis for a deep-tech founder including me, and so to pin-point and make sense where the real value will be captured exactly.

Now If you listen to the keynotes at Mobile World Congress (MWC), you would think the telecom industry solves its hardest structural problems every February. The reality on the show floor is far more complex. MWC is the ultimate mirror for the tech industry. Its a place where long-term infrastructure realities collide head-on with short-term software hype.

As a deep-tech founder building at the intersection of edge computing and network AI, I look at MWC not as a product catalogue, but as a strategic map. If you want to understand where enterprise budgets are actually flowing and where the “tourist traps” lie? you have to look past the banners.

So here is a deep-dive analysis of

how MWC has evolved,

why the AI hype cycle is a dangerous trap for builders, and

where the real technological value is being created in 2026.

1. The Macro Evolution: From Technical Standards to Geopolitical Stage

To understand the current state of MWC, you have to understand its trajectory. The event didn’t start as a selfie wall for folding phones. It started as a closed-door room full of engineers arguing about radio frequencies. Over 35 years, it has morphed from an engineering summit into a high-stakes geopolitical arena.

The Stack of Global Influence

1990 (Rome): The Standards Era
The first real GSM World Congress was held in Rome. The primary focus was GSM coordination. European regulatory coordination created a shared technology platform that replaced fragmented, incompatible national analogue systems. This wasn’t marketing; this was the creation of the global mobile economy.

2006 (Barcelona): The Spectacle Phase
Coinciding with the explosion of the smartphone, MWC became a “device carnival”. The floor was dominated by handset launches. However, underneath the consumer marketing, the real work was operators scrambling to lay the 4G IP-based infrastructure that would make the mobile app ecosystem possible.​

Mid-2010s: Infrastructure & The Cloud Invasion
The narrative shifted to 5G, network virtualization (NFV), and the software-defined core. This was the era where hyperscalers (AWS, Azure, Google Cloud) realized telcos owned the ultimate edge real estate.

Telcos were forced to stop thinking of networks as pipes and start thinking of them as programmable edge infrastructure.

2020s–Present: The Geopolitical Forum
Today, MWC is a stage for debates heavily influenced by Washington, Brussels, and Beijing. The underlying theme of modern MWC is Sovereignty.

🛑 The Counter-Point: Geopolitics Is the Tech Stack

We often treat the “geopolitics of tech” as a bureaucratic annoyance that gets in the way of innovation. In telecom, it is the primary market constraint.

Take the European Huawei rip-and-replace mandates. European operators built their 4G and early 5G networks heavily reliant on cost-effective Huawei RAN (Radio Access Network) and core equipment.

As the EU and local governments moved to ban “high-risk vendors” from core networks by 2026/2027, operators have been forced into multibillion-euro replacement projects.

You can build the greatest AI network optimizer in the world, but if your hardware supply chain violates US export controls on advanced semiconductors, or if your data routing breaches European data sovereignty laws, your tech is commercially dead on arrival.

The geopolitical layer now dictates the hardware layer, which in turn dictates the software layer.

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2. The 3-Year AI Hype Cycle (2024–2026)

I use the term “Wildebeest Stampede” to describe the telecom industry’s tendency to charge toward new software buzzwords with high velocity but varying levels of direction. The banners change annually, but the underlying technical maturity takes years to catch up.

Let’s look at the last three years of MWC show floors:

2024: Network Automation / 5G
AI hype was building, but implementations remained tentative. A GSMA Intelligence survey during this period showed 81% of telcos were testing GenAI, but very few were deploying it in the core. The real money and engineering focus was still on 5G Standalone (SA) upgrades and basic network automation.​

2025: Gen AI / Wrappers
The stampede reached peak volume.

Every booth slapped “GenAI” on their collateral.

Yet, analysts quickly pointed out the reality: most of these services were gimmicky LLM wrappers.

Vendors were taking off-the-shelf OpenAI or Anthropic API keys, wrapping them in a telecom-themed UI, and selling prompt engineering as “network transformation.”​

2026: Agentic AI / AI-Native

The exact same wrappers have been crossed out and rebranded.

Repackaged RPA (Robotic Process Automation) and deterministic, scripted workflows are now being labelled as “autonomous agents.” Gartner predicts 40% of enterprise apps will embed agents by the end of 2026, creating massive industry FOMO.​

🛑 The Counter-Point: The Upgrade Cycle Illusion

The stampede isn’t actually driven by sudden technological breakthroughs; it’s driven by the enterprise software upgrade cycle.

Vendors are forcing “Agentic AI” into product updates to justify subscription price hikes.

For deep-tech founders, chasing these buzzwords is a fatal trap. If you build a product tailored strictly to the 2025 GenAI hype, by the time it achieves production-grade stability in late 2026, the marketing herd will have already moved on to the next banner.

Startups cannot afford to pivot their core architecture every 12 months just to match the MWC keynote schedule.

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3. The Technical Litmus Test: Separating Lions from Wildebeests

At MWC, there are two types of companies:

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Right now, the AI story you see online is all about “agents,” “copilots,” and “one‑person unicorns.” It looks like magic on a laptop screen. But behind every slick demo there’s something much more boring 😑 🥱 yet much more important going on and that is computers yelling at each other over wires.

When you train a big AI model, it doesn’t run on one fancy chip.

It runs on thousands of chips at once, spread across rows of racks in a warehouse. Those chips have to talk to each other constantly. If the “conversation” between them slows down, everything slows down.

That conversation happens over cables. Today, those cables are mostly copper. Over the next few years, they’ll increasingly be glass fibers carrying light. And that change, from electricity in metal to light in glass – will quietly decide who actually wins the AI race.

Put simply: It’s no longer about who has the cleverest AI. It’s about who can move data around the fastest and cheapest.

TL;DR

• AI isn’t just about smart software; it needs huge factories of computers working together.

• The real choke point now is how fast we can move data between those computers.

• Old copper wires are hitting physical limits: they get hot, waste power, and can’t go far at very high speeds.

• Light through fiber (photonics) lets data travel faster and farther with less energy.

• The winners in AI will be the ones who build better “roads and pipes” for data, not just better apps.

Table of Contents

1. Why the conversation between thousands of GPUs matters more than the demo

2. Tech giants are spending like they’re building a second power grid, not buying servers

3. At bleeding-edge speeds, copper cables collapse to rack distances—here’s the physics

4. How light replaces electricity to turn scattered racks into one giant machine 🔒

5. What MWC, RSA, and national AI summits reveal about infrastructure constraints e 🔒

6. Specific moves for founders, enterprise buyers, and investors 🔒

   1. For Founders 🔒
      Four white-space opportunities in AI infrastructure tooling

   2. For Enterprise Buyers 🔒
      Questions that separate real infra from vendor fluff

   3. For Investors 🔒
      How to spot actual moats when bandwidth dominates.

7. Winners build better machines, not just better models.

Everyone’s Selling Agents; The Bottleneck Is the Wires.

The $650B AI Grid Build-Out

Over just one year, the biggest tech companies ( Alphabet, Amazon, Meta, and Microsoft ) are planning to spend around $650 billion building AI infrastructure mostly data centers, power, and networks. That’s more than many countries spend on their entire power grid in the same period.

Bridgewater framed the same magnitude in Reuters coverage, emphasizing it as “AI investment” scale rather than routine IT refresh. Meaning?

This isn’t “buy a few more servers” money or a normal cloud upgrade cycle. This is “build a new utility” money. The spending tells you what cycle you’re actually in.

That number matters less as a headline and more as a diagnostic:

• This is utility build-out behavior, not a SaaS feature cycle.​

• The constraint is increasingly “can we build it?” » power, sites, cooling, networking gear and not “can we sell it?”

• The bottleneck is moving from software iteration to construction, supply chain, and physics.

A helpful way to think about it:

The first internet was built so people could send messages, watch video, and browse websites.

The new AI “internet under the internet” is being built so computers can talk to other computers at extreme speed, all day, every day.

A good comparison is “building a second internet under the first one.”

The public internet is optimized for many independent flows like north-south traffic between clients and servers. AI infrastructure is increasingly dominated by east-west traffic:

• accelerators synchronizing,

• exchanging activations,

• routing tokens (MoE),

• sharding weights,

• retrieving KV cache,

• checkpointing, and

• feeding multi-stage inference pipelines.

That east-west world needs different economics and different topology. It needs the kind of determinism and bandwidth you don’t get by accident.

So effectively we’ re laying a second, invisible network inside and between data centers that is only there so AI chips can keep each other up to date. That’s what this $650B wave is really paying for.

for a founder or CTO, the non-obvious takeaway is that the enduring advantage in this cycle is not a wrapper UI. It’s throughput, latency, and reliability per dollar spent, end-to-end from GPU package, to rack, to row, to campus.

Why Copper Is Running Out of Road

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I’ll try to explain this and next section simple language, as most of the readers may be non-deep tech. Most of today’s AI data centers still use copper cables for the short links between chips and racks, this is basically very expensive versions of the same metal inside your laptop charger. The long‑distance traffic across cities and under oceans already runs mostly over fiber, but inside the AI ‘factory’ a lot of those close‑range connections are still copper.

When you push data through a copper cable at extreme speeds, two things happen:

• The signal dies quickly – after maybe a meter or so at cutting‑edge speeds, it becomes mush without heavy boosting.​

• You burn a lot of power as heat, because you’re constantly charging and discharging a metal wire at GHz rates.

For AI, this is a killer. A modern AI server rack can use 3–5 times more power than a traditional rack. If too much of that power is wasted just pushing bits down hot copper, you hit a wall on both cooling and cost.

Inside the rack, that’s why copper is still everywhere: it’s cheap, familiar, and for a few meters it works great. But as AI clusters grow, the real bottleneck isn’t just “moving big files”, it’s keeping thousands of GPUs in sync so none of them are sitting idle waiting on the slowest link.

In modern systems, a surprisingly large chunk of time is spent just on GPUs talking to each other rather than doing math, and that share keeps rising as models and clusters scale.​

Bottomline. the physics is now biting.

copper isn’t “dead,” but it’s running out of road as the main way to glue big AI machines together. The faster we push the links, the shorter and hotter those copper runs become, and beyond roughly a rack’s distance the wires themselves start to dictate how you design the whole system.

Photonics and the New AI Backplane

If copper is like shouting across a room, photonics is like using lasers and fiber‑optic cables to talk. Instead of pushing electricity through metal, you send light through glass.

Why this matters:

• Light can carry much more data over longer distances without fading.

• It uses less energy per bit, especially as the distance increases.

• It doesn’t turn your cables into heaters in the same way.

In the old world, we had lots of separate servers connected by a network. In the AI world, the “computer” is the whole domain of accelerators that can swap tensors fast enough to feel like one big chip. Photonics simply makes that domain bigger.

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I started my career with Network automation at Cisco over 2 decades ago. my focus area was more script based automation for ( dev-test) routers and switches for MPLS Networks. from there I moved to production side in AT&T and Verizon and then to Keysight to selling testing and network monitoring solutions. when I founded my started bandarlog.dev in 2021, in UK, which was relatively a new tech landscape for considering I haven’t been to Europe much until, I wanted to understand how we plan to transform the legacy infrastructure to something that can handle next generation applications and services including metaverse who no one wants to speak about anymore except yes, there are groups working in peace on this, including me, with in a small bandwidth and scope.

the telco industry’s journey in past few years has been marked by intense activity. good or bad, some success here and there but a lot of technological disruption. in 2022-23 every other conference was focussed on Network automation, before that we were busy talking about how 5G is gonna change the world. then came Gen AI and all hell broke lose, literally. Disruption is good though. no seriously. but distraction? not really.

so now at the end of 2025 when we’re slightly more settled in the Gen AI space, and figuring out how Agentic AI and distributed architecture may be the future, I wanted to assess where we are in terms of our main goal and thats infrastructure.

TL;DR

1. Industry focus on Network automation in past 5 years.

2. Have we just been talking? How much automation has been achieved since.

3. has AI accelerated the efforts or been a distraction?

4. What’ is actually blocking progress, what are the biggest challenges.

5. What should be our roadmap

for Non-networking people » Automation comes in different flavors for example factory robots that build things, software “robots” (RPA) that click through computer screens like a person would, and workflow automation that handles business tasks like approving expense reports.​

Network automation at the basic is what manages your internet equipments, the hardware - it automatically configures and fixes routers, switches, and firewalls that keep networks running.​ This is not workflow automation which handles paperwork, RPA mimics human clicking, but network automation actually controls the invisible infrastructure that makes the internet work - it’s managing the Infrastructure, not just the tasks.

1. The boom in Network Automation Conferences

The last 5 years have witnessed a dramatic arc in network automation conference activity and industry focus, with a clear peak that tells us much about where the industry has been and where it’s heading now. the most number of conferences focussed on Network automation were in 2022-2023, with lot of industry mobilization around automation themes. In 2022, 18 major events focused heavily on network automation, with a focus intensity score reaching 85 out of 100. This number surged to 22 events in 2023 with peak focus intensity of 95, representing the high-water mark of pure automation-focused gatherings.

Mobile World Congress Barcelona 2022 epitomized this peak, dedicating substantial programming to 5G expansion and network transformation. in the same year, specialized events like Cisco’s “Automate & Innovate 2022” concentrated almost exclusively on integrated network automation and intent-based networking.​ The 2023 conference season maintained this momentum. MWC Barcelona 2023 pivoted toward cloud-native networks, 5G Standalone architecture, and network automation with a focus score of 92. NANOG 88 in June 2023 presented critical data from the “2023 State of Network Automation Survey Results,” while the GSMA Digital Transformation Leaders’ CxO Summit in November 2023 in Kuala Lumpur brought together industry executives to discuss intelligent digital transformation in the 5G era.​

Then comes the shift:

While the absolute number of events remained substantial (20 in 2024, 15 in 2025), the focus intensity dropped markedly to 75 and 60 respectively in past 2 years. AI took the center stage and focus dropped to almost 50. all the major events such as DTW Copenhagen 2024, Network X Paris 2024 and FutureNet World 2025 demonstrated the new reality with automation focus at 55 versus AI focus at 90.​

Several converging factors could explain why network automation may have first dominated the conference landscape during this 22-23 and then the drop afterwards.

5G Deployment Momentum: Global 5G connections surged from 1.05 billion in 2022 to 1.5 billion in 2023, a 43% increase in a single year. The number of commercial 5G networks expanded from 229 to 259, each requiring sophisticated automation for network slicing, dynamic resource allocation, and service orchestration.​

Market Validation: The network automation market experienced explosive growth from USD 2.99 billion in 2022 to USD 14.56 billion in 2023, an astonishing 387% year-over-year increase. This validated the business case and attracted intense industry attention and investment.​

Technology Maturity: By 2022-2023, SDN/NFV had matured sufficiently for widespread deployment. Over 140 telecom operators across 70 countries had implemented SDN or NFV by 2022, growing to over 70% of communication service providers adopting cloud-native network functions by 2023.​

Legacy Infrastructure Urgency: The PSTN shutdown timelines became critical, with the UK’s BT Openreach announcing a complete shutdown by January 2027 and nationwide stop-sell beginning September 2023. Operators faced mounting pressure as 60-80% of IT budgets were consumed maintaining legacy systems, creating existential urgency around modernization.​

Pre-GenAI Clarity: Importantly, 2022-2023 predated the GenAI revolution triggered by ChatGPT’s November 2022 release. The automation discussion remained focused on achievable, well-understood technologies rather than speculative AI capabilities, creating clearer messaging and stronger industry alignment.

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2. How Much Automation Have We Actually Achieved?

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IBM conducted a survey with 106 executives from major telecommunications operators across six countries, focusing on leaders responsible for AI, technology, operations, and business strategy within their organizations. The just released paper “Telecommunication in AI Era” focusses on the generative AI ( GEN AI) telco use cases that mark a shift from using AI predominantly for efficiency and cost savings to leveraging it for business innovation, new revenue streams, and competitive advantage and rapidly redefining the telecommunications industry. Most CSPs however are still facing challenges scaling and unlocking full value due to data silos, legacy systems, and the need for deeper enterprise-wide integration.

A recently published BCGs research ( TLDR here ) showed a section of Gen AI focussed applications delivering measurable business impact across sales operations. I wanted to include this here to emphasize we’re only focussing on “Gen AI” here and not Agentic AI, which remains largely experimental as of now. BCGs researched use cases focus on specific, well-defined tasks like content generation, call transcription, and automated battle cards, whereas agentic implementations attempt broader autonomous decision-making that current technology cannot reliably support, so there’s an overlap and the the research compliments the telco data.

AI use cases in telecom

As companies scale AI projects from pilots to enterprise-wide deployments, Here are few telco specific use cases listed in the report, where per the leadership, Gen AI implementation across the industry and the incremental impacts has been observed.

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While everyone’s getting comfortable talking about AI these days, the telecommunication world, and the stuff powering all our connected device still feels like a foreign language to most people, well unless until that connection is slow or goes off of course. So I understand that terms like “Private 5G” may still sound abstract or even intimidating to most if you’re not immersed in the industry, but here’s the thing private 5G is no longer a hype, It’s the behind-the-scenes engine that’s starting to change how big, complex operations actually work.

Even though its not yet noticeable to consumer in daily life, for organizations that rely on speed, safety, and reliability. such as hospitals or factories where devices talk to each other instantly, and ports run more smoothly than ever, it matters the most. For example, from smart factories in Germany to AI-powered farms in Japan, private 5G is quietly turning Wi-Fi headaches into yesterday’s problem. and for those leading the charge, private 5G has become less of a “nice-to-have” and more of a must-have for keeping the wheels turning.

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If AI dominates the conversation, Private 5G is the quiet partner making sure everything runs as promised. Private 5G is finally moving beyond talk and starting to actually deliver real-world results for a lot of different industries. As a Founder in this space, I wanted to dig in to the 2600 pages - SNS Telecom & IT report released in August, that brings together a mountain of data, case studies, and practical insights you’d struggle to find anywhere else. It surely 10x further to the Mary Meeker’s AI report that was released in June, and rightly so. For anyone trying to understand why all this matters or just what’s actually happening out there in the trenches with real deployment, this report should present both the big-picture view and those rare, on-the-ground details to understand where digital transformation is headed, and why Private 5G is becoming a serious tool for organizations that can’t afford “business as usual” anymore.

While the full report isn’t available publicly, and I had to make do with the snippets, but from what I gather It answers some really important questions for example just how big this 5G business is going to get, what’s driving adoption, and where the major growth hotspots are, both by sector and by region. The Questions such as what makes private 5G different, when advanced features like URLLC and network slicing will matter, and how new spectrum rules are making it possible for more players to set up their own networks. The report explores which industries and countries are leading the charge, what practical benefits organizations are already seeing (from cost savings to worker safety), and how private 5G is sparking real digital change in everything from agriculture to heavy industry. Apart from how the tech is unpacking itself, and competition within technologies and market segmentation, the deployment trends and strategies are also significantly important to explore, along with connecting the dots between technology, regulatory roadmap, and really practical business outcomes, which this report helps with.

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Here are the key insights:

• Private 5G networks are emerging as a mainstream technology, moving beyond niche LTE deployments to support demanding Industry 4.0 applications across manufacturing, logistics, healthcare, mining, ports, aviation, defense, and other sectors.

• Key advantages over Wi-Fi and LTE include ultra-reliable low-latency communication (URLLC), massive IoT support, scalability, strong security, wide coverage, and high throughput—enabling use cases like autonomous robots, connected production lines, wireless quality control, remote operation, and AR/VR in complex environments.

• China is the dominant leader in private 5G deployments, fueled by state support, followed by growing rollouts and interest in the US, Germany, UK, France, Japan, South Korea, and others. Adoption is increasingly global, with hundreds to thousands of dedicated network nodes in some industrial facilities.

• Case studies show quantifiable benefits:

  • efficiency gains of 20-90%, up to 55% labor cost savings, substantial productivity increases, and significant reductions in downtime, operating costs, accidents, and emissions.

  • Cost savings, safety, and sustainability improvements are reported, especially where Wi-Fi or legacy connectivity failed. Common cases include predictive maintenance, AGVs, wireless quality control, and process automation.

  • Public studies from Nokia and GlobalData show that 87% of surveyed industrial adopters report ROI within 12 months of deploying private 5G and industrial edge computing.

  • Common cases include predictive maintenance, AGVs, wireless quality control, and process automation.

• High-profile adopters include Tesla, Jaguar Land Rover, Lufthansa, John Deere, NEC, Newmont, Walmart, and major airports, utility companies, and military bases.

• Major vendors and system integrators (Ericsson, Nokia, Huawei, ZTE, Samsung, NEC, Celona, HPE, Cisco, etc.) plus a growing set of new players and alternative suppliers, are competing in the evolving ecosystem.

  • new solutions such as network-in-a-box, 5G as a Service (5GaaS), and national efforts to open more spectrum to private enterprise.

• Market Drivers: Push for digital transformation, local spectrum liberalization enabling enterprise-run networks, and the need to replace unreliable Wi-Fi or legacy wired/cellular infrastructure.

• Market forecast: Private 5G investment will grow at a CAGR of ~41% from 2025–2028, surpassing $5B by 2028, led by manufacturing, process industries, and critical communications (public safety, utilities, railways), with hundreds of real-world deployments and pilots seen globally. Asia-Pacific is leading, especially China, but North America and Europe are rapidly scaling up too.

• Regulatory, Spectrum, and Comparisons: growing trend of integrating private 5G with existing Wi-Fi or IT/OT systems for optimized, resilient connectivity in industrial settings.

• Future roadmap: Accelerated by shared/local spectrum regimes, 5G-Advanced features, edge computing, new business/operational models, and integration with Wi-Fi/IoT—paving the way for 6G in 2030s.

Private 5G Is the Tech You Don’t See - But It’s Already Redefining the Way the World Works. Private 5G networks are poised for rapid expansion as organizations seek robust, scalable wireless connectivity for mission-critical applications, with measurable impacts on productivity, cost, and safety across many industries. That’s why the new SNS Telecom & IT report on the Private 5G Market is so timely and important.

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In today’s world, experience is everything—visibility is everything. Consumers flock to services and products that make life easier, more delightful, and more “seamless.” We cheer for digital platforms, apps, and tools that are visible, narrate their magic, and make friction vanish in everyday moments. The curated, and frictionless premium digital experiences are so valuable for us that, we’ll happily pay $2000 a year for a browser—just for the privilege of ordering Chipotle more smoothly. Take the recent LinkedIn post from Perplexity’s CEO: a user points out you can use the Comet browser to seamlessly order Chipotle, dodging aggregator app fees and clunky websites. It’s positioned as a breakthrough: easier, cheaper, neater. And thousands “like” it.

• In entertainment, Netflix gets the love, not the global network of fiber and content delivery behind it.

• For commerce, Amazon is adored for one-click orders—not for the logistics pipes and networks that guarantee delivery.

• In software, flashy AI app builders like lovable win headlines, while cloud compute and storage quietly enable them in the background. unless its Nvidia of course but thats a different story.

• Even in fintech, the visible front-end wallet or payment app is prized, not the core payment rails that never get a moment in the spotlight.

The hunger for visible convenience means that industries working behind the scenes, no matter how transformative, risk fading from public affection. In the digital era, brand love, value, and pricing power concentrate where the experience is visible, narratable, and emotional—not where the hardest technical work or investment happens. Narrative and visibility aren’t just marketing—they’re survival strategies. Whoever frames the daily magic wins hearts, growth, and premium status.

Now imagine a small team of engineers, working through the night on a remote hilltop, brings mobile connectivity to a village—quietly empowering children’s learning, fairer markets, and family reunions, one signal at a time. Telecom, for instance, powers every message, call, payment, and viral moment, yet gets little credit, loyalty, or mindshare, simply because its greatest value is invisible when it’s working well. the network industry that has quietly made all this online convenience possible—collapsing the cost of mobile data, multiplying speeds, blanketing cities in coverage—still can’t sell even basic upgrades without resistance. Telcos remain the uncelebrated infrastructure, unable to command premium pricing or inspire affection, while new digital brands market the joys of order-tracking and instant checkout as major innovations.

It’s a paradox: Consumers celebrate paying more for a browser to get a better burrito, but balk at the prospect of paying telcos for exponentially better, cheaper, and more reliable access to everything online. The narrative belongs to whoever frames the experience—not necessarily the true enabler behind the curtain, and for every founder or industry: this is sort of a lesson—don’t just build the value, own the experience and make sure the world sees it. storytelling and relevance, not just reliability, win hearts (and wallets) in the digital age. Until telcos rewrite their narrative—from utility to experience-maker—the credit, the love, and the margin will keep going elsewhere.

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But was this always the story? Where did the things go wrong?

Lets take a deeper look unpack the paradox, quantify the progress, explore the perception gap, and outline a playbook for change.

From “Miracle Talking Wires” to the Lost Romance of the Platform Age : A Short History of Telco Love

Before smartphones and 5G, the telephone itself was magic. Over 150 years, public affection for the companies that supplied that magic has risen, plateaued, and then faded. The timeline below tracks pivotal “love moments” and shows how sentiment crested in the late-1990s mobile boom before sliding into today’s indifference.

1. The Awe-and-Wonder Era (1876 – 1920)

Wins

• Pure enchantment. Newspaper columns hailed Alexander Graham Bell’s device as a “new nerve of civilization,” promising to shrink distances and advance democracy.

• Celebrity status. Early subscribers—often doctors, journalists, and merchants—were local influencers; switchboard operators became cultural icons of calm competence.

Losses

• Sticker shock. Annual fees of £15–£20 in Britain exceeded a clerk’s wage, sparking editorials that only the elite could afford “the costly toy”.

• Usability pain. Crank handles, party lines, and manual exchanges produced mis-dials and privacy fears, sowing early frustration.

Mind-set

Optimism tempered by Victorian morals: critics worried the phone would encourage gossip and erode letter-writing decorum, yet overall sentiment netted strongly positive (+4 on our index).

2. The Monopoly Paternalism Era (1920 – 1984)

Wins

• Universal-service mythos. AT&T’s 1916 manifesto, The Kingdom of the Subscriber, cast the Bell System as benevolent steward of national progress.

• Reliability worship. Engineers delivered five-nines uptime; the phrase “We are the phone company” signified authority—and a certain comfort.

Losses

• Price resentment. Controlled tariffs and equipment rental stifled choice; waiting lists for new lines stretched months.

• Antitrust backlash. By the 1970s the U.S. Justice Department framed Ma Bell as a “trust to be busted,” culminating in the 1984 breakup that fractured public confidence.

Mind-set

A blend of grudging respect and silent irritation. Telcos were necessary, even admired for engineering prowess, but rarely loved.

3. Deregulation & Brand Romance (1984 – 2000)

Wins

• Freedom to choose. Competition slashed long-distance rates and introduced colourful newcomers—Sprint’s pin-drop ads, MCI’s “Friends & Family.”

• Lifestyle rebrand. In 1994 Orange launched with the human promise “The future’s bright” and 45% brand awareness within two weeks—an emotional shockwave in a market of grey utilities.

• Global mobile boom. By 1998 Nokia owned 40% of handset share; the mantra “Connecting People” resonated across cultures and ages, embedding genuine affection for a telco-rooted brand.

Losses

• Network glitches. Early GSM drop-outs dented credibility.

• Rising churn. Pre-pay SIMs made loyalty fickle even as advertising spend soared.

Mind-set

Hopeful, hip, and increasingly personal. For a brief moment telcos were consumer technology.

4. The Early-Smartphone Peak (2000 – 2007)

Wins

• Pop-culture cachet. Motorola’s “Hello Moto” blitz tied phones to fashion runways and club scenes, helping the brand rebound against Nokia.

• SMS mania. Texting volumes exploded, and carriers basked in the glow of New Year’s Eve messaging records.

Losses

• Billing rage. Roaming shocks and opaque WAP charges triggered tabloid scandals.

• Apple’s entrance. The 2007 iPhone shifted the centre of consumer excitement from network to device, relegating operators to billing rows on the screen.

Mind-set

Excitement mixed with a nascent fear of irrelevance as hardware and internet brands stole the spotlight.

5. The Platform & OTT Eclipse (2008 – 2020)

Wins

• Infrastructure heroism. 4G roll-outs doubled median mobile speeds and enabled app stores, streaming, and social video—quietly.

• Trust bump (brief). Edelman’s 2014 barometer showed telecom gaining 15 points in Australia, second only to energy, on reliability during crises.

Losses

• Commodity trap. Unlimited bundles reframed data as a race to zero price.

• Customer-service drag. ACSI ranked internet providers near the bottom of 40 industries for call-centre satisfaction throughout the 2010s.

• Narrative hijack. Consumers thanked Netflix and Instagram for experiences delivered over telco pipes.

Mind-set

Functional gratitude but emotional neutrality. Operators became invisible backdrop to Big Tech.

6. The 5G-AI Paradox (2021 – Present)

Wins

• Speed miracles. 5G median mobile speeds top 250 Mbps in leading markets, and networks carry 200 × more traffic than in 2010—largely without outages.

• Pandemic resilience. Lockdown video calls highlighted telco indispensability.

Losses

• Erosion of trust. In Edelman 2025 telco trust slipped to 64/100, trailing technology by 12 points and sliding into the lower half of industries.

• Love gap. Consumers rate mobile brands’ emotional engagement 30% below that of digital platforms in Omdia’s 2024 engagement survey, blaming “transactional tone” and call-centre pain.

Mind-set

Respect for utility, disappointment in experience, and indifference to brand—a stark contrast with the late-1990s high-affection peak.

Telecom operators were once national marvels, then paternal monopolists, then pop-culture darlings—before drifting into the background of Big Tech’s stage. Each era’s love hinged less on raw technology and more on perceived human benefit and brand voice.

The progress however looks as impressive as it could be:

• Mobile data now costs under $0.10/GB in Italy but over $7 in Switzerland, proving affordability gains are real yet uneven.

• 4G coverage expanded from 44 percent of the global population in 2015 to 90 percent in 2023, and 5G already reaches 51 percent.

• Despite a cumulative $1.5 trillion mobile CAPEX bill expected for 2023-30, telcos’ capital intensity is falling as big 5G and fibre build-outs taper off.

• Customer trust lags: in 2025 Edelman ranks telecom 16 points below technology and 28 points behind food & beverage.

• Root causes cluster in three zones: invisibility (networks are hygiene factors), legacy service pain points (billing, call centres), and narrative failure (operators talk bits while digital brands sell meaning).

• Winning back love requires shifting from “pipes” to “possibility” storytelling, closing experience gaps with AI-driven personalization, monetizing relevance (network-exposed APIs, B2B2X), and reframing CAPEX as purpose-led infrastructure.

Despite driving one of history’s most profound digital transformations—tripling connection speeds, pioneering global 4G/5G reach, and slashing data costs in many regions—telcos remain oddly unloved, scoring low on public trust and customer satisfaction. This paradox highlights a key future insight: as networks evolve to power AI, quantum security, and the smart grid, true differentiation will come from humanizing the telco brand and making value visible. Recognition will belong to operators who move beyond invisible pipes and position themselves as intelligent enablers of tomorrow’s connected societies. The future telco must win not just on performance, but by earning back public affection.

Telcos face tepid revenue CAGRs (~3%) and CAPEX pressure, but AI-driven efficiency and platform plays could lift margins. By reframing themselves as custodians of digital civilisation rather than SIM-card vendors, operators can reclaim narrative ground – and public affection. Re-winning hearts means reviving that voice: celebrating networks as enablers of everyday magic, fixing lingering service pain, and reclaiming narrative credit for the connective tissue of modern life.

The industry’s brand deficit is not a technology failure.

It is a storytelling and experience gap. Telcos own the arteries of the digital age yet speak like meter readers. By humanising infrastructure, fixing service pain points with AI, and evangelizing their indispensable role in everything from remote surgery to climate tech, operators can turn silent success into celebrated relevance – and finally earn the ovations their networks already deserve.

I hope you found this article interesting, informative, and useful. Do support by liking, subscribe, share it with you colleagues and friends and on social media — X, LinkedIn, or the platform of your choice. You can follow me on linkedin here.

PS I think this article needs a Part 2 : “Telco 2030 Outlook: How AI, Quantum & Semiconductors Will Redraw the Network Map” and More. coming up next month.

Share @poonamparihar

Further reading :

• Wiring the Future: AI, Chips, and the Race to Reinvent the Internet

• AI’s Gold Rush 2.0: Unpacking the Psychology, Power, and Paradoxes Shaping Billion-Dollar Bets

• Why Tech Journalism Is Now the Most Powerful Beat in the Newsroom

• Legends from the Land of Logic…in the making, mostly.

• Project Trillion: UK's 10-Year Race to Reshape Global Technology

• Between Promise and Practice: The Real Story of AI Adoption

• The Anatomy of the Shake-Out Phase

Leave a comment

NotebookLM podcast review of McKinsey’s Tech trends outlook 2025 report.

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We’re in Q3 of 2025, and as a founder at the intersection of deep technology and future global networks, I wake every day to an industry landscape profoundly more complex—and thrilling—than even the boldest of the forecasts I could imagine, and there’s been many this year.

The exponential surge in AI capabilities in past couple of months isn’t simply about software anymore; it’s a tidal force, demanding co-innovation from silicon, network protocols, and cloud fabric, right down to new market structures. The lines have blurred: we are now co-designing semiconductors unique to our workloads, partnering on sovereign data clusters, and architecting security for cross-border agent ecosystems—all while racing to escape the gravitational pull of legacy infrastructure. and as a builder- founder who’s lived the journey from prototype to hyperscale, I recognize that value creation no longer happens at the product alone, but in assembling the right convergence of talent, governance, and ecosystem. The world’s digital nervous system is being rewired—and we are the hands pulling the cables and penning the standards.

when Veo3 got integrated in Gemini Chat recently, I did a short trailer video to quickly test it out. Asked perplexity to pull out some of my journey and make it a mission impossible sort of an adventure. Amusing as it may sound, none of it fiction anymore. My life as I imagine turning in to the classic story of a lone founder in a dimly lit server room, surrounded by blinking lights and whiteboard equations. —the network is humming with AI agents negotiating network traffic, quantum prototypes quietly failing (sometimes spectacularly), and the pizza delivery drone drops off lunch on the fire escape. Welcome to the modern telco startup, where building the internet’s future feels oddly like assembling a spaceship while flying it to Mars.

The battleground has shifted from cobbling together open source routers to orchestrating multi-cloud clusters, securing supply chains for custom semiconductors, and deciphering AI ethics frameworks before breakfast. Yesterday’s deep tech founder needed to out-engineer competitors; today’s must out-think regulations, out-hustle venture capital, and out-collaborate entire ecosystems. Every day is “demo day,” not just to impress investors, but to prove your network fabric, trust models, or agentic AI “virtual coworkers” can survive the next quantum leap. If this feels relentless, that’s because it is. But it’s also exhilarating, and every hotshot AI, quantum prototype, or 6G testbed is a new brushstroke on the digital canvas of civilization.

This is no longer the future. It’s the present. And I am not alone—leaders from every sector are realizing that value creation, risk management, and industry leadership now hinge on understanding, and acting on, what McKinsey calls “frontier technology trends.” In this post I want to dig a bit deeper just released “McKinsey Technology Trends Outlook 2025” report, from founder’s perspective and find out what of these are simply tech fads or distant possibilities or if they will be the building blocks of tomorrow’s winners and losers.

This "Tech trends outlook 2025" report outlines 13 frontier technology trends, with Artificial Intelligence as a foundational amplifier, that are transforming global business. It details their breakthroughs, use cases, and potential impact on companies across sectors, helping leaders navigate increasing complexity and global competition. The report aims to guide executives in deciding which technologies are most relevant for their strategic future.

Top Priority: The AI Revolution

Artificial Intelligence (AI) stands out as the most critical and urgent trend. It is not merely a powerful technology wave on its own, but also a foundational amplifier that accelerates progress and unlocks new possibilities across all other trends. The evolution of AI solutions increasingly combines aspects of applied AI and generative AI, now examined together.

AI Revolution: Current State and Future Potential

The widespread adoption of AI contrasts sharply with organizational maturity levels, revealing significant opportunities for growth and development.

Unprecedented Investment Momentum

The scale of AI investment has reached historic proportions, demonstrating unprecedented market confidence and capital allocation toward artificial intelligence technologies. The investment trajectory shows remarkable acceleration, with first-quarter 2025 funding approaching half of the entire 2024 annual total, signaling intensifying market interest and the transformative potential investors see in AI technologies.

Critical Barriers to AI Implementation

Skills and training gaps represent the most significant obstacles to AI adoption, creating bottlenecks that organizations must address to realize AI's full potential. This breakdown below reveals that nearly half of organizational leaders identify skill gaps as primary barriers, while a significant portion of employees lack adequate training, highlighting the urgent need for comprehensive upskilling initiatives.

Key Insights / Lessons from AI

• Pervasive Adoption and Future Potential: Seventy-eight percent of organizations surveyed are using AI in at least one business function, and 92 percent of executives are planning to invest more over the next three years. Despite this, it's an early trend, with only 1 percent of leaders saying their companies are fully mature in AI deployments, indicating significant headroom for transformation.

• Cost Reduction and Accessibility: The cost of deploying powerful AI models is dropping sharply, and a new generation of smaller, domain-specific models is giving more organizations and devices access to AI than ever before. These smaller models deliver high-quality outputs while requiring much less computing power, dramatically reducing costs and energy consumption.

• Multimodality and Reasoning: Multimodal AI, capable of processing and generating text, images, video, and audio, has opened new creative and scientific frontiers. AI is also getting better at complex, multistep reasoning, allowing foundation models to strategically plan and adapt to variability.

• Responsible AI Imperative: As generative AI adoption accelerates, concerns persist over plagiarism, accountability, data poisoning, bias, and fairness. Companies face growing pressure to demonstrate transparency and accountability, with ethics becoming a strategic lever that can accelerate or stall scaling and investment.

• Global Investment Surge: Equity investment in AI reached $124.3 billion in 2024. In the first quarter of 2025 alone, AI companies raised $52 billion, including the SoftBank-led $40 billion investment in OpenAI, marking the largest venture capital funding deal ever recorded. Tech giants are projected to spend hundreds of billions on AI-related capital expenditures in 2025.

• Talent Gap: Forty-six percent of leaders cite skill gaps as a major barrier to AI adoption, and over 20 percent of employees report minimal training. Addressing upskilling needs and adapting to new human–machine collaboration models are crucial.

• Action for Leaders: "The true differentiator is not just technical capability; it is the ability to rewire operating models, talent, and governance, embedding AI deeply into workflows to deliver measurable business impact".

Big Questions for the Future of AI:

• What primary use cases should companies prioritize to drive innovation, adapt leading solutions, or build proprietary capabilities?

• How can organizations evolve their operating models, talent, and governance fast enough to scale adoption and capture value?

• How will the economics of smaller, specialized, and open-source AI models shift the balance of power?

• What is the appropriate corporate response to risks, including data privacy, security, equity, fairness, compliance, and intellectual property?

Agentic AI: Explosive Growth Trajectory

The emergence of Agentic AI represents one of the fastest-growing segments within the broader AI landscape, characterized by remarkable job market expansion. This new trend combines the flexibility of AI foundation models with the ability to act autonomously, creating "virtual coworkers" that can plan and execute multistep workflows.

Key Insights / Lessons from Agentic AI:

• Rapid Growth: Despite relatively low equity investment levels compared to more established trends, agentic AI is among the fastest-growing trends highlighted in the report, signaling its potentially revolutionary possibilities. Its job postings saw a +985% difference from 2023 to 2024, with equity investment reaching $1.1 billion in 2024.

• Unique Capabilities: Agentic AI agents can:

  • Handle a "long tail" of unpredictable tasks that are not easily codified into preset rules, thanks to large language models (LLMs).

  • Utilize digital tools, such as web browsers, just like a person would, enabling them to "read" websites and fill out forms.

  • Receive instructions and coaching in natural language, similar to interacting with a human coworker.

  • Generate work plans that are understandable and modifiable by humans, and can even communicate among themselves.

• Latest Developments: Progress includes AI-powered, general-purpose agent platforms, increasingly long chains of effective multistep reasoning through multiagent workflows, a new focus on specific business solutions (e.g., software development, sales optimization, customer support), growing momentum behind deep-research knowledge agents, and agents that can "talk" to one another (e.g., Anthropic's Model Context Protocol, Google's Agent2Agent protocol).

• Urgent Governance Needs: The rapid progress of agentic AI underscores a critical need for robust governance frameworks to address trust, liability, and ethical concerns.

• Thought from Leaders: "Agentic AI moves AI from a passive tool to an active collaborator with enterprise workflows". "Organizations that learn to build teams that bring people and agent coworkers together will unlock new levels of speed, scale, and innovation".

Big Questions for the Future of Agentic AI:

• What are the workforce implications of agentic AI at scale?

• What trust and safety tooling and techniques will be needed to mitigate risks?

• To what extent should agentic AI be allowed to operate independently, and how can a balance be struck between AI autonomy and human oversight?

Critical Enablers: Compute and Connectivity Frontiers

The immense demands placed by AI necessitate significant advancements in computing and connectivity, making these trends equally high-priority as enablers.

Application-Specific Semiconductors

• Purpose and Importance: These are purpose-built chips optimized to perform specialized tasks, offering superior speed, energy efficiency, and performance compared to general-purpose architectures. They are primary catalysts, driven by the exponentially higher demands for computing capacity, memory, and networking for AI training and inference.

• Innovation Surge: Innovations in this sector have spiked, reflected in quantitative metrics like patent numbers, with the semiconductor industry leading in patent activity across all technology trends.

• Market Dynamics: New AI hardware vendors are challenging established leaders, with tech companies like Amazon, Google, Meta, and Microsoft investing heavily in custom ASICs and proprietary semiconductor technologies.

• Demand and Supply Challenges: Global demand for data center capacity, especially for AI workloads, is projected to rise significantly (average of 33 percent a year between 2023 and 2030). This necessitates adequate semiconductor supplies to scale effectively. The industry faces "cracks" in global infrastructure due to power constraints and rising compute demands.

• Geopolitical Impact: Geopolitical tensions are affecting global supply chains, with countries pursuing self-sufficiency and securing leadership in strategic technologies.

• Lesson from Leaders: "The rise of gen AI isn’t just a software revolution; it’s a tectonic shift in hardware demand". "Innovation bloomed across all technology trends from 2023 to 2024, relecting in a broad increase in patent activity, and the semiconductor industry leads the way with the highest number filed overall".

Big Questions for the Future of Semiconductors:

• How may the increase in demand for AI-specific architectures reshape the semiconductor market?

• How can the industry address AI’s growing demand for high-bandwidth memory (HBM) amid supply chain challenges?

• What strategies will semiconductor companies employ to overcome talent shortages and skill gaps?

Cloud and Edge Computing

• AI's Reshaping Influence: The rapid evolution of AI is reshaping the entire cloud infrastructure landscape. The surging demand for AI computing has led to deeper collaboration among chip developers, cloud providers, and infrastructure manufacturers.

• Capacity Demands: Global demand for data center capacity could rise 19 to 22 percent annually from 2023 to 2030, more than tripling current demand.

• Overcoming Constraints: Companies are relocating or expanding to areas with more robust energy infrastructure, developing more efficient computation solutions (e.g., liquid cooling, AI-enabled thermal management), and distributing workloads across multiple clusters and edge environments to optimize performance and reduce latency.

• Evolving Landscape: Advances in hardware accessibility and investments have enabled smaller players to enter the cloud market, offering specialized AI-optimized platforms. There's also increasing demand for locally hosted data and compute, driven by concerns about data security, privacy, and geopolitical risks (sovereign clouds).

• Lesson from Leaders: "Previous investments in cloud are paving the way for AI. The learnings of deploying cloud at scale are lessons to be applied to AI".

Big Questions for the Future of Cloud/Edge

• How can cloud providers effectively reduce their energy consumption and carbon footprint?

• How can companies ensure data sovereignty and compliance with evolving regulations in multi-cloud environments?

• What will define competitive advantage in the cloud over the next five years: scale, architecture, or ecosystem control?

Advanced Connectivity

• Growing Importance with AI: As AI disrupts industries globally, connectivity is becoming even more important.

• Infrastructure Enhancement: Technologies like 5G, emerging 6G, Wi-Fi 6 and 7, and low-Earth-orbit (LEO) satellites are enhancing connectivity infrastructure.

• Fiber Optics' Role: Fiber optics, in particular, enable AI with high-speed, reliable, and scalable infrastructure, especially due to the explosion of AI data centers.

• New Capabilities: The 5G rollout has created a foundation for 6G, which aims to enable sensing for data generation. The network-slicing market is growing, providing specific performance characteristics across network segments. Private-wireless-network market is also picking up steam. AI-RAN (AI-radio access network) is an innovative approach to mobile network infrastructure that allows for supporting both RAN and AI workloads.

• Lesson from Leaders: One of the most pronounced connectivity trends during the last year was a significant growth in data center connectivity demand driven by AI". "6G is coming from a standardization point of view and will introduce new capabilities like sensing. This will offer telcos a new monetization possibility, instead of just transporting data. With sensing, they can become data producers".

Big Questions for the Future of Connectivity

• What opportunities does advanced connectivity, combined with other trends, offer to improve telecom industry growth and profitability?

• How will the rise of LEO constellations and direct-to-device satellite connectivity reshape global communications infrastructure?

• How will the telecom industry develop advanced encryption methods and authentication protocols for 6G networks?

Innovation vs Interest Scores for Technology Trends

The relationship between innovation and market interest reveals distinct patterns across technology trends. AI leads both dimensions, reflecting its maturity and market penetration, while emerging technologies like quantum computing and agentic AI show lower interest scores despite significant innovation potential.

Adoption Patterns

1. Level 4 (Scaling in progress): 7 trends including AI, semiconductors, cloud computing, and digital trust

2. Level 3 (Piloting): 2 trends including mobility and energy/sustainability technologies

3. Level 2 (Experimentation): 3 trends including robotics, immersive reality, and space technologies

4. Level 1 (Frontier innovation): 1 trend (quantum technologies)

Talent and Labor Market Dynamics

The technology talent landscape reflects the rapid evolution of industry priorities. While most trends experienced fluctuations in job postings, the standout performer was agentic AI with its 985% growth, followed by artificial intelligence at 35% and semiconductors at 22%.

Key talent market insights include:

1. Skill Gaps as Barriers: 46% of leaders cite skill gaps as a major barrier to AI adoption

2. Training Deficits: Over 20% of employees report minimal AI training

3. Evolving Role Definitions: The rise of agentic AI is shifting responsibilities from deterministic coding tasks to higher-order activities like task planning and contextual decision-making

4. Investment Patterns and Market Dynamics

Notable Investment Trends

The investment landscape reveals clear priorities, with energy and sustainability technologies leading at $223.2 billion, followed by mobility at $131.6 billion and artificial intelligence at $124.3 billion. However, the relationship between investment levels and growth potential varies significantly across trends.

Energy and Sustainability Dominance: Despite challenges in scaling production and reducing costs, clean energy technologies continue to attract the highest investment levels

Mobility Sector Stabilization: Electric vehicle markets show uneven global growth, with strong performance in China and Europe but slower adoption in the United States

Emerging Technology Focus: While receiving lower absolute investment, trends like agentic AI and quantum technologies show rapid growth in interest and innovation metrics

Cross-Cutting Themes and Challenges

Several themes emerge across all technology trends:

Scaling Challenges

The surging demand for compute-intensive workloads has exposed infrastructure limitations, requiring solutions that address not only technical architecture but also talent, policy, and execution challenges.

Responsible Innovation Imperatives

As technologies become more powerful and personal, trust increasingly serves as the gatekeeper to adoption. Companies face growing pressure to demonstrate transparency, fairness, and accountability.

Regional and National Competition

Global competition over critical technologies has intensified, with countries and corporations pursuing self-sufficiency and securing leadership in strategic technologies.

Human-Machine Collaboration Evolution

Technology is becoming more responsive to human intent and behavior, shifting the narrative from replacement to augmentation and enabling more natural, productive collaboration.

Strategic Implications for Business Leaders

The 2025 outlook underscores several critical imperatives for executives:

1. AI Integration Beyond Deployment: Success requires rewiring operating models, talent, and governance to embed AI deeply into workflows for measurable business impact

2. Infrastructure Investment: The explosive growth in computing demands necessitates strategic investments in semiconductors, cloud capabilities, and connectivity infrastructure

3. Talent Development: Addressing skill gaps through comprehensive upskilling programs and adapting to new human-machine collaboration models is crucial

4. Trust and Ethics: Building robust governance frameworks and demonstrating responsible innovation practices are essential for long-term success

5. Ecosystem Thinking: The interconnected nature of these technologies requires collaborative approaches and strategic partnerships

Looking Ahead: Key Questions for 2025 and Beyond

As these technologies continue to mature and converge, several critical questions will shape their future impact:

1. How will organizations balance AI autonomy with human oversight as agentic systems become more sophisticated?

2. What regulatory frameworks will emerge to govern the responsible development and deployment of powerful AI systems?

3. How will geopolitical tensions affect global technology supply chains and innovation ecosystems?

4. What new business models will emerge as these technologies reach full commercial maturity?

McKinsey's Technology Trends Outlook 2025 reveals a technology landscape in rapid transformation, driven primarily by AI's emergence as both a standalone powerhouse and an amplifier of other innovations. While significant opportunities exist across all 13 trends, success will depend on organizations' ability to navigate complexity, invest strategically in enabling infrastructure, and build the trust necessary for widespread adoption.

The organizations that will thrive in this AI-powered future are those that move decisively from experimentation to scaled adoption while building robust frameworks for responsible innovation. As the lines between digital and physical continue to blur, the companies that act with focus and agility will not only unlock new value but also shape the future of their industries.

For executives, success will hinge on identifying high-impact domains, investing in necessary talent and infrastructure, and addressing external factors like regulatory shifts and ecosystem readiness. For the deep tech founders like myself is both exhilarating and sobering. No single leap in cloud, connectivity, or AI will determine tomorrow’s winners; rather, it’s the integration and orchestration of these advances in real time, under the accelerating pressure of regulation, geopolitics, and evolving markets, that will decide who shapes the future.

I hope you found this article interesting, informative, and useful. Do support by liking, subscribe, share it with you colleagues and friends and on social media — X, LinkedIn, or the platform of your choice. You can follow me on linkedin here.

Share @poonamparihar

Further reading :

• AI’s Gold Rush 2.0: Unpacking the Psychology, Power, and Paradoxes Shaping Billion-Dollar Bets

• Why Tech Journalism Is Now the Most Powerful Beat in the Newsroom

• Legends from the Land of Logic…in the making, mostly.

• Project Trillion: UK's 10-Year Race to Reshape Global Technology

• Between Promise and Practice: The Real Story of AI Adoption

• The Anatomy of the Shake-Out Phase

Leave a comment