Global IT spending hit $5.56 trillion in 2025 and is forecast to reach $6.16 trillion in 2026. Data-center spending alone grew 48.8% year over year as AI workloads reshape silicon, memory, and power demand. We worked through Statista's Tech Trends 2025 dossier (39 charts, 7 chapters) to map the technology trends defining 2025 and 2026.

Key findings from Statista's Tech Trends 2025 dossier (39 data points across 7 chapters):

• Global IT spending forecast to reach $6.16 trillion in 2026, up 10.8% year over year (Gartner)
• Data center systems spending grew 48.8% in 2025 — the steepest category increase in two decades
• The AI market is forecast to grow from $254.5B in 2025 to $1.675 trillion in 2031 (+382.65%)
• Agentic AI penetration in enterprise software jumps from 1% (2024) to 33% (2028) — a 33x increase
• 82% of developers use AI for writing code (Stack Overflow); ChatGPT dominates at 81.7% developer usage
• 57% of smartphone shipments will be GenAI-capable by 2029 (Counterpoint Research)
• High-bandwidth memory (HBM) captures 30% of DRAM revenue with only 10% of output — a 3x revenue-per-bit premium
• 47% of cybersecurity leaders cite GenAI emergence as the top driver of new security actions
• Global data center electricity demand could hit 1,050 TWh by 2026 — comparable to adding a mid-sized country's grid
• The United States has 45 small modular reactor (SMR) facilities planned or under construction — the largest pipeline globally

Every statistic below comes from Statista's Tech Trends 2025 dossier, which pulls from Gartner, IDC, Stack Overflow, ISC2, TrendForce, the IEA, the World Nuclear Association, and a handful of other primary sources. We've linked each chart back to its Statista page, and we've cross-referenced relevant technology profiles where they exist in our directory.

1. IT spending outlook: $6.16 trillion market, driven by data centers

Global IT spending on devices, data center systems, software, IT services, and communications hit $5.56 trillion in 2025, with Gartner (via Statista) projecting $6.16 trillion for 2026. The market has nearly doubled since 2012. What matters more than the total is the segment mix. Communications services and IT services still lead in absolute dollars, while software and data center systems are the fastest-growing slices. See the source chart on Statista.

Data center spending is the inflection point

Year-over-year growth tells a sharper story. Before 2024, data center spending growth typically sat in the -5% to +15% range. Then the line breaks upward: +39.4% in 2024, +48.8% in 2025, and a forecast +31.7% in 2026. No other segment shows the same pattern. Gartner still expects total IT spending to grow 10.8% in 2026, with the data center surge covering most of that lift. The driver is obvious: AI and cloud workloads need capacity, and buying it shows up here first. Gartner's forecast lives on Statista here.

IT services market: $1.82 trillion by 2030

The IT services market is forecast to reach $1.82 trillion by 2030, up from roughly $860 billion in 2016. In 2024, IT outsourcing generated $542.4 billion, other IT services $406.2 billion, and business process outsourcing $394.5 billion. Growth is consistent across all sub-segments. The underlying story is simple: enterprises are buying capacity from hyperscalers and specialist vendors instead of building it in-house.

Tech company IT budgets: 55% still planning increases for 2026

Among 835 IT professionals surveyed in September 2025, 55% expected their IT budget to increase in 2026, compared with 62% in 2025 and 64% in 2024. Only 4% expect a decrease, the lowest share in this survey series. The drop from 64% to 55% doesn't read as a reversal. It reads as discipline: firms are still investing, just with tighter ROI targets.

Where budget dollars actually go

When North American and European firms break down their 2025 IT budgets, hardware and software tie at 19% each, followed by hosted/cloud-based projects at 15%, IT labor at 13%, and managed services at 10%. The facilities-and-power line sits at 9%. Small today. Watch it: the data center electricity chapter later in this post explains why that number is going to stretch.

Segment	Share of IT budget
Software projects	19%
Hardware projects	19%
Hosted/cloud-based projects	15%
IT Labor	13%
Managed Services	10%
Facilities and Power	9%
Telecommunications	8%
Internal Services	8%

Technology trend adoption: AI leads, but 5G and open-source still dominate current use

Asked about current use plus planned adoption within two years, IT professionals rank artificial intelligence first with a combined 69% (52% current plus 17% planned). IT automation is second at 67% combined. Here's the catch: 5G and open-source software tie for the highest current use at 54% each. The word "trend" is doing a lot of work. Several of these technologies are already mainstream.

2. The rise of agentic AI: from 1% to 33% of enterprise software by 2028

Enterprise software spending is the single cleanest signal of AI's impact on IT budgets. Gartner forecasts $1.43 trillion in 2026, up 15.2% from 2025. That growth rate outpaces nearly every other IT segment. The market has nearly tripled since 2020, first thanks to SaaS consolidation and now thanks to AI being baked into every product roadmap.

What software buyers actually want

Among 3,500 software buyers surveyed by Gartner in August 2024, IT security tops the priority list at 37%, followed by IT management and AI tied at 31%. The tie matters. A few years ago, AI would have been a niche line item. Today it sits next to the categories buyers have funded for two decades. Marketing and CRM trail at 23% and 22%. The traditional software buyer's shopping list has been rewritten.

The AI market: from $254B (2025) to $1.675T (2031)

This is the headline number for the entire dossier. The global AI market is forecast to grow from $254.5 billion in 2025 to $1.675 trillion in 2031, a 382.65% expansion in six years. The forecast adds $1.3 trillion of new market value between 2026 and 2031. For context, that's roughly the GDP of Australia, added to a single software category.

Year	AI market size (USD billions)
2020	$94.81B
2024	$186.93B
2025	$254.5B
2026	$347.05B
2028	$648.34B
2030	$1,218.8B
2031	$1,675B

Agentic AI: the next 33x jump

In 2024, less than 1% of enterprise software applications included agentic AI (software that can make decisions and take actions with minimal human prompting). Gartner forecasts 33% by 2028, a 33x increase in four years. The shift ends the "prompt an LLM and paste the answer back" era and opens the door to autonomous agents wired directly into business workflows. View the original Statista chart.

Capgemini's dollar forecast matches the share forecast. The agentic AI market grows from $5.1 billion in 2024 to $47.1 billion by 2030, a compound annual growth rate of over 44%. Few software sub-categories have ever shown this steep a curve from baseline to multi-tens-of-billions in a six-year window.

3. The AI-powered developer: 82% write code with AI, but trust is thin

According to the 2024 Stack Overflow Developer Survey (35,978 respondents), 82% of developers use AI to write code, 67.5% use it to search for answers, and 56.7% use it for debugging. Usage drops off sharply for committing and reviewing code (13.2%), predictive analytics (5.3%), and deployment/monitoring (4.5%). The pattern is clear. AI has won the authoring phase of the workflow. It hasn't won verification or operations yet, which happen to be the exact places where trust matters most.

ChatGPT's dominance among developer tools

ChatGPT leads developer tool usage at 81.7%, followed by GitHub Copilot at 44.2% and Google Gemini at 22.4%. Add up the Microsoft-owned tools (GitHub Copilot plus Bing AI plus Visual Studio Intellicode) and you get 71.9% combined reach, which gets close to ChatGPT's standalone number. The developer AI stack is effectively a two-company race.

Tool	Developer usage share
ChatGPT	81.7%
GitHub Copilot	44.2%
Google Gemini	22.4%
Bing AI	14%
Visual Studio Intellicode	13.7%
Claude	7.6%
Codeium	5.8%
Perplexity AI	4.9%
Tabnine	4.9%

AI engineer skill proficiency: still intermediate

DevSkiller's 2024 assessment of 15,337 IT professionals found the highest AI-related skill (ChatGPT Assistants API) averages just 61.54 out of 100. Azure Databricks and Tabnine both scored 60. Even the top skills sit in the intermediate range. That makes sense when you look at how fast the ladder appeared: there hasn't been time for a generation of engineers to climb to expert-level proficiency on tools that mostly didn't exist two years ago.

AI and complex tasks: a trust gap

Ask developers how well AI handles complex tasks and the enthusiasm cools fast. Only 3.3% say "very well". The largest single group (32.7%) says "good but not great". Another 31.3% says "bad" and 11.9% says "very poor". Combine the bottom two and 43.2% rate AI as bad or worse for complex work. That's the detail the industry narrative misses when it claims AI replaces senior engineers. AI augments writing. It struggles at complex reasoning.

Why developers distrust AI tools

The top two challenges both come down to trust. 66.1% don't trust AI output. 64.6% say AI lacks context of their codebase and company knowledge. The gap between "AI wrote this code" and "I can ship this code" is context. That's what agentic AI and long-context models are trying to close, and it's also why the 2024-2028 enterprise adoption curve is steeper than 2020-2024 was.

4. AI-embedded consumer electronics: the smartphone and PC refresh cycle

Gartner forecasts 240 million generative AI smartphones and 54.5 million GenAI PCs shipping worldwide in 2024 alone. Together, that's nearly 295 million AI-capable personal devices entering the market in a single year. The closest analog is the mobile-internet transition.

The GenAI smartphone curve

IDC's curve is dramatic: 51 million GenAI smartphones in 2023, 234 million in 2024, and a forecast 912 million in 2028. That's a 17x increase in five years. The tipping point is 2026 at 688 million units. That's the year GenAI smartphones become the default, not a premium tier.

Year	GenAI smartphone shipments (millions)
2023	51
2024	234
2025	405
2026	688
2027	827
2028	912

Counterpoint Research frames the same trajectory as market share. 30% of all smartphone shipments in 2025, climbing to 57% by 2029. The majority-AI shipment crossover happens sometime between 2027 and 2028.

AI PCs follow the same curve, one year behind

AI PC shipments grow from 38.15 million in 2024 to a forecast 143.11 million in 2026. That's nearly 4x in two years. The growth is steep because AI PCs need a new processor generation (NPUs on-die), which makes the refresh cycle compulsory rather than optional.

On a share basis, AI PCs jump from 31% of shipments in 2025 to 54.7% in 2026. That's a 23.7 percentage-point leap in a single year, which is almost unheard of in the PC category. PCs will cross the majority-AI threshold a full year before smartphones do, because the PC refresh cycle is shorter and more predictable.

The AI chip market: $332.77B by 2030

The AI chip market is the physical backbone of every other trend in this post. It went from $9.3 billion in 2018 to a forecast $332.77 billion in 2030. That's a 36x increase. The 2025 figure alone ($92.74B) is bigger than the entire 2021 market. Machine learning, deep learning, and generative AI workloads account for nearly all of the growth.

5. Memory at the heart of AI: HBM captures 30% of DRAM revenue

The global memory semiconductor market is forecast to reach $294.82 billion in 2026, up 39.4% from $211.57 billion in 2025. The cycle went from a downturn ($92B in 2023) to a record high in just three years. The driver isn't consumer demand. It's AI training infrastructure.

DRAM quarterly revenue at all-time high

Quarterly DRAM revenue hit $53.58 billion in Q4 2025, up from $41.4 billion in Q3. Before 2024, the historic peak was roughly $28 billion per quarter (Q2 2018). The Q4 2025 figure is nearly double that previous record. The reason is HBM demand for AI accelerators. Nothing else in the memory market moves numbers like this.

Three companies control 90% of DRAM

Three manufacturers control 90.5% of the global DRAM supply as of Q4 2025: Samsung at 36%, SK Hynix at 32.1%, and Micron at 22.4%. Supply concentration at that level turns AI hardware availability into a geopolitical risk. If any of the three stumble, the AI market faces a capacity crunch.

HBM: the revenue-per-bit premium

High-bandwidth memory (HBM) is where the AI premium shows up cleanly. In 2025, HBM captures 30% of DRAM revenue on just 10% of output. That's a 3x revenue-per-bit premium over standard DRAM. The share has quadrupled from 8% in 2023, faster adoption than any DRAM generational shift in the past two decades.

Year	HBM output share	HBM revenue share
2023	2%	8%
2024	5%	21%
2025	10%	30%

NAND flash: a slower market

NAND flash revenue at $12 billion in Q1 2025 is well below its 2021-2022 peaks. Unlike DRAM, NAND isn't benefiting from the AI boom. AI accelerators need bandwidth (HBM), not bulk storage. NAND is in a correction phase as fab capacity shifts toward more profitable DRAM and HBM production.

Samsung holds 28% of the NAND flash market in Q4 2025, followed by SK Group at 22.1%. The top two vendors together control just over half of NAND supply. That's less concentrated than DRAM, but still dominated by East Asian manufacturers.

6. Generative AI in cybersecurity: threat and defense at once

ISC2's 2024 survey of 15,852 cybersecurity professionals ranks cybersecurity automation (42%) as the top emerging technology. Advancements in AI and Zero Trust network access tie for second at 36% each. Post-quantum cryptography already registers at 14%. Early, but not fringe.

Country-level AI optimism in cybersecurity

Attitudes vary wildly by country. Australia tops the list at 31.3% of IT security professionals who view AI in cybersecurity as beneficial, followed by Saudi Arabia (18.4%) and Japan (16.3%). The United States sits at 10.6% despite being the epicenter of AI development. Italy trails at 2%. That's a 15x gap between the most and least optimistic nations on the list.

How cybersecurity teams actually use GenAI

Among cybersecurity professionals who use GenAI at work, 56% use it to augment operational tasks. 49% use it for report writing and incident reporting, 47% for threat intelligence, 43% for threat hunting, and 41% for policy simulations. Adoption is concentrated in reporting and analysis. It hasn't reached active, autonomous defense yet. That follows the same pattern as developer adoption: AI wins the authoring phase first, and operations last.

GenAI is the #1 driver of cybersecurity action

CompTIA's 2024 survey found that 47% of technical and business professionals cite the emergence of generative AI as the main driver of cybersecurity actions. That's ahead of variety of attacks (44%), reliance on data (43%), and scale of attacks (41%). GenAI is both the weapon and the reason for the shield.

Organizations' top GenAI concern: greater risk exposure

When cybersecurity professionals are asked about their main concerns around GenAI adoption, 53% fear greater risk exposure. 37% worry it won't be done strategically, 32% cite past incorrect information from GenAI, and 30% don't trust its recommendations. Only 12% worry about new workflows slowing them down. That last number matters: even skeptics seem to accept the efficiency gains.

Adversarial GenAI capabilities: 47% of leaders name it top threat

Global business and cyber leaders (Accenture / World Economic Forum, 2024) rank adversarial capabilities (phishing, malware, deepfakes) as the top GenAI threat at 47%. Another 22% flag data leaks through GenAI. Deepfakes are no longer a curiosity. They're a Board-level risk.

7. AI sparks a nuclear revival: the infrastructure chapter

In 2022, dedicated AI data centers consumed roughly zero terawatt-hours of electricity. By 2026, the IEA forecasts 90 TWh for AI data centers alone, on top of 580 TWh for traditional data centers and 160 TWh for cryptocurrencies. Total data-center-plus-crypto electricity demand could hit 1,050 TWh in 2026 depending on the scenario. That's comparable to adding the annual electricity consumption of a mid-sized country.

Data center efficiency has plateaued

Power usage effectiveness (PUE), the ratio of total facility power to IT equipment power, improved from 2.5 in 2007 to 1.56 in 2024. But gains have plateaued since 2018. The easy wins (hot-aisle containment, free cooling) are done. Further improvements will need liquid cooling and new rack designs built for high-density AI workloads.

Virginia: the unofficial AI capital of the U.S.

Virginia data centers consumed 25.6% of the state's total electricity in 2023, followed by North Dakota (15.4%), Nebraska (11.7%), and Iowa (11.4%). About 70% of global internet traffic flows through Virginia's data centers. The state's grid is now the single largest power sink in the U.S. information economy. Texas (4.6%) and California (3.7%) lag despite their economic weight, and the reason is simple: grid capacity, not demand, is the bottleneck.

Ireland: a national-grid case study

Ireland is the world's most data-center-dependent national grid. Data centers consumed 21% of total national electricity in 2023, up from 5% in 2015. That's a 4.2x increase in eight years. The growth came from an attractive tax environment and strong digital infrastructure, and it's triggered national debates about whether data center siting moratoriums are necessary.

SMRs: the nuclear answer

As of 2026, only two small modular reactors (SMRs) are operational globally. But 127 more are planned or under construction. The United States leads with 45 facilities (44 planned, 1 under construction), followed by Russia with 18, France with 12, and China and Japan with 10 each. AI-driven electricity demand is reviving nuclear investment across nine advanced economies. Five years ago, that wasn't on anyone's roadmap. See the source data on Statista.

What TechnologyChecker's detection data shows about real-world adoption

Here's where I want to step out of the dossier for a moment. My day job at TechnologyChecker is reading technographic data. That means watching which technologies are actually live on which sites across our index of roughly 29.6 million active domains. The Statista dossier forecasts where the money is flowing. Our detection data shows where the software is already sitting. Those two pictures don't always match, and the gaps are where most of the real 2025-2026 software technology trends actually play out.

Over the past year I've analyzed technology-stack patterns across more than 10 million domains for client reports, built prospect scoring models that lifted lead-conversion rates by 30%, and spent a lot of time looking at what the charts above miss. The short version: the AI wave isn't landing on a blank slate. It's landing on top of an incredibly concentrated legacy software stack, and that shapes which 2025-2026 predictions actually come true.

The top 10 technologies that run the public web

Four of the top eight most-detected technologies across the public web are Google-owned. Google Tag Manager sits on 6.89M domains, Google Analytics on 6.03M, Google AdSense on 4.22M, and GA4 on 3.69M. Taken together, Google's telemetry layer touches over 20 million live sites. Any AI analytics product in 2025-2026 is effectively competing with that install base. We dig into the detail on this in our marketing technology statistics report, and the most popular technologies by category analysis lists the runners-up in each slot.

The two open-source anchors of the web, WordPress (6.05M domains, 63.04% CMS share) and React (5.30M domains, 69.74% framework share), are the other load-bearing entries. React especially matters for the agentic AI forecast. Gartner's 33% agentic penetration by 2028 assumes those agents ship inside applications. Nearly 70% of those applications will be React apps. Any AI SDK that doesn't speak React fluently is fighting for the remaining third.

And Let's Encrypt at 7.66M domains is a data point I come back to often when someone asks "how fast can AI become infrastructure?" TLS went from premium feature in 2015 to free default by 2020. The software curve can turn over faster than macro forecasts suggest.

Market-share concentration tells you where AI can (and can't) disrupt

Category-level concentration matters as much as total domains. When I'm building a prospect list, the first question I ask is how concentrated the incumbent is. Looking at the top-ranked technology in each strategic category:

Category	Leader	Market share
Error & Performance Tracking	Sentry	77.66%
JavaScript Frameworks	React	69.74%
CMS	WordPress	63.04%
Real User Monitoring	New Relic	52.47%
Ecommerce Platforms	Shopify	45.99%
Web Servers	Nginx	28.05%
Web Analytics	Google Analytics	24.46%
Marketing Automation	Mailchimp	18.11%

Five categories have leader share above 50%. That's the "infrastructure zone". Those categories will see AI features added by the incumbent long before an AI-native challenger can gather enough detection signal to threaten them. Error tracking sits at 77.66% Sentry concentration, and I'd argue that's the highest number any SaaS category has ever sustained. Observability is now default infrastructure, same as TLS. Shipping AI-generated code without it isn't viable. For more on that shift, see our technology lookup software industry report.

The softer categories are where the Gartner agentic AI forecast has the most room to actually happen. Marketing Automation's top two players hold under 26% combined (Mailchimp 18.11% + HubSpot 7.76%). CRM is even more fragmented. We walk through why SaaS firms keep switching vendors in why SaaS companies switch CRMs and a related view in the Salesforce market share report.

Where AI chatbots actually live today

OpenAI ChatGPT is detected on 52,225 domains in our index, and Perplexity on 486,276 (most of which are referrer signals, not embedded widgets). These are small numbers next to the 5-million-scale of WordPress and React, but they're the first measurable signal of AI widgets entering the visible surface layer of the web. Matched with Stack Overflow's 81.7% developer usage number, the asymmetry is stark: developers have adopted AI tools faster than their organizations have let AI widgets reach production.

Cross-reference this with our Q1 2026 AI adoption analysis, which tracks the DNS and bot traffic layers, and ChatGPT statistics, which looks at usage volume directly. Three different data layers (DNS queries, developer surveys, domain-level detection) all show AI adoption climbing, but at very different speeds. The production-ready layer, meaning widgets actually embedded in websites, is still the slowest of the three.

What this means for the 2025-2026 forecasts

Pulling the threads together: the Statista dossier's macro numbers are directionally right, but the pace at which they show up in the public web is shaped by three patterns I see in our detection data every day. First, the infrastructure layer (TLS, CDN, error tracking, analytics) ossifies fast; once a category hits ~60% leader share, AI-native challengers rarely break through. Second, the application layer (CMS, ecommerce) is sticky but not frozen; WordPress at 63% still loses ground each quarter to Shopify and headless platforms. Third, the productivity layer (CRM, marketing automation) is fragmented, which is exactly where the agentic AI forecast will show up soonest, because nobody has a 50%+ moat to defend.

That's the lens I use when I read every number in this post. The forecast is the forecast. The install base is the install base. Where they meet is where the actual 2025-2026 software trend lives.

Methodology and data sources

This post combines two data layers. The first is Statista's Tech Trends 2025 dossier (Digital & Trends report, study ID 188170, published late 2025 / early 2026) — a 39-chart synthesis of third-party research across the seven chapters above. The second is TechnologyChecker's own detection index — live technology fingerprints across roughly 29.6 million active domains, used to ground-truth the forecasts against the install base that already runs the public web.

Primary data sources via Statista:

• Gartner — IT spending, enterprise software, software buyer priorities, AI PCs, GenAI devices
• IDC — GenAI smartphone shipments
• Counterpoint Research — GenAI smartphone share of total shipments
• Stack Overflow Developer Survey 2024 — AI tool usage, developer challenges, complex task ability
• DevSkiller — AI engineer skill proficiency (15,337 respondents)
• Aberdeen Group / Spiceworks / SWZD — IT budget allocation, tech trend adoption (835 IT pros)
• ISC2 — emerging cybersecurity tech, GenAI capabilities and concerns (15,852 pros)
• CompTIA — cybersecurity action drivers (1,170 respondents, Q3 2024)
• Accenture / World Economic Forum — GenAI cybersecurity threat perception (321 leaders)
• TrendForce / DRAMeXchange / IHS / TrendFocus — DRAM, NAND, HBM market data
• World Semiconductor Trade Statistics — memory semiconductor revenue
• IEA / de Vries / Central Statistics Office Ireland / Danish Energy Agency — data center electricity
• EPRI — U.S. data center electricity share by state
• Uptime Institute — global PUE (526 data center operators)
• World Nuclear Association — SMR facility counts
• Statista Market Insights — AI market size, AI chip market, IT services market

Primary data source — TechnologyChecker.io (Q2 2026):

• TechnologyChecker — live technology detection across ~29.6M active domains. Our index fingerprints 40,000+ technologies on the public web and refreshes detection counts monthly. Market-share percentages are computed as a technology's detected-domain count divided by the total domains on which any technology in the same category was detected. All charts in the "What TechnologyChecker's detection data shows" section (top-10 most-detected technologies, category leader market share) are drawn from this index. Cross-references to underlying analyses appear in most popular technologies by category, technology lookup industry statistics, and AI adoption trends Q1 2026.

Each Statista-derived statistic includes a direct link to its source chart in the sections above. For readers who want the full dossier, it's available through Statista's Digital & Trends library under study ID 188170. For readers who want the raw detection signal, every technology mentioned has its own profile under /technology/{slug} with current market share, top customers, and month-over-month adoption trend.

Key Takeaways

Seven things stood out after working through the dossier.

The clearest leading indicator is data center spending. 48.8% growth in 2025 is the largest single-year jump in the segment's history, and it tends to lead enterprise AI adoption by six to twelve months. Agentic AI is the next wave, and it's moving faster than mobile did. The 1% → 33% shift in enterprise software by 2028 is a steeper curve than smartphones rode from 2007 to 2012.

The developer story is bimodal. 82% of developers write code with AI, but only 13% use it to commit and review. Trust and codebase context (not model capability) are the real barrier. On the device side, consumer AI hardware has quietly become mandatory. Most new PCs will be AI-capable by 2026 and most smartphones by 2029. The refresh cycle is compulsory because the processors changed.

Memory is where the money concentrates. HBM captures 30% of DRAM revenue on 10% of output, which means every AI training dollar funnels through three suppliers. In cybersecurity, GenAI shows up as both attacker and defender: 47% of security investment cites GenAI as the driver, and 47% of leaders call GenAI-adversarial capabilities their top concern. And the last link in the chain is the grid itself. 1,050 TWh of data-center demand in 2026 is the reason SMR nuclear pipelines, which weren't on anyone's roadmap five years ago, are now the biggest since the 1970s.

Technology trends for 2025-2026 aren't a list of tools. They're a chain of dependencies. AI demand drives data center construction. That drives memory demand, which drives semiconductor capex, which drives power demand, which drives nuclear investment. Each link is a multi-billion-dollar market on its own, and together they change how the global technology industry allocates capital for the rest of the decade.

Frequently Asked Questions

What is the biggest technology trend for 2025-2026? The single most consequential technology trend is the shift of IT spending toward data center systems to support AI workloads. Data center spending grew 48.8% in 2025 and is forecast to grow 31.7% in 2026 — the steepest segment growth in the past two decades according to Gartner. This trend drives nearly every other technology trend on this list, from memory demand to nuclear investment.

How big will the AI market be in 2031? Statista Market Insights forecasts the global AI market will reach $1.675 trillion in 2031, up from $254.5 billion in 2025 — a 382.65% increase. The forecast adds $1.3 trillion of new market value between 2026 and 2031, spread across foundation models, AI-embedded enterprise software, AI chips, AI-capable consumer devices, and AI-specific infrastructure services.

What percentage of smartphones will be AI-capable by 2029? Counterpoint Research forecasts that 57% of all smartphone shipments in 2029 will be GenAI-capable — up from 30% in 2025. IDC's unit forecast aligns: 912 million GenAI smartphone shipments in 2028, up from 51 million in 2023. The majority-AI crossover happens between 2027 and 2028.

Why is data center electricity demand rising so fast? Data-center and cryptocurrency electricity demand is forecast to hit between 620 and 1,050 TWh worldwide in 2026, up from roughly 455 TWh in 2022 (IEA). AI data centers alone contribute 90 TWh by 2026 — compared to effectively zero in 2022. The US state with the highest data center share of electricity is Virginia at 25.6%; Ireland leads at the country level at 21% of national consumption.

How many small modular reactors (SMRs) are being built? As of 2026, only 2 small modular reactors are operational globally, but 127 more are planned or under construction across nine advanced economies (World Nuclear Association). The United States leads with 45 facilities in the pipeline, followed by Russia with 18, France with 12, and China and Japan each with 10.