Is AI Slowing Down in 2026? (28 Data to Understand)

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AI development in 2026 paints a complex picture that defies simple narratives.

Investment is exploding, commercial adoption is skyrocketing, and model capabilities keep improving at the frontier.

Yet the methods driving progress are fundamentally shifting, costs are rising exponentially, and physical infrastructure bottlenecks are creating real constraints that money alone can't solve. Check out our 200-page report covering everything you need to know about AI Wrappers to understand how these market dynamics affect product strategy.

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Is AI Actually Slowing Down in 2026?

• 1. OpenAI's valuation doubled to $300B in just 6 months

  Explanation:

  OpenAI jumped from $157 billion in September 2024 to $300 billion by early 2025. That's nearly double in six months. They closed a $40 billion funding round, the biggest VC round ever. Other AI companies saw similar jumps: Anthropic went 3.3x higher (from $18.4B to $61.5B) in one year, and xAI doubled from $24B to $50B in six months.

  Interpretation:

  Investors doubled OpenAI's valuation in six months because they expect massive future growth. If AI was slowing down, valuations would drop instead of skyrocket. The fact that smart money keeps pouring in at higher prices shows they see a clear path to making this investment pay off.

  Source: Crunchbase

• 2. Big Tech AI spending hits $325B in 2025, up 46% year-over-year

  Explanation:

  Combined capital expenditure from Microsoft, Meta, Amazon, and Google reached $223 billion in 2024 and is projected to hit $325 billion in 2025. Amazon alone plans to spend approximately $105 billion, Microsoft around $80 billion, Google roughly $75 billion, and Meta between $60-65 billion. The vast majority targets AI infrastructure including data centers and GPUs.

  Interpretation:

  This is one of the biggest investment booms in modern history. The 46% jump in spending shows these companies believe AI will make them money. If they thought AI was hitting a wall, they'd cut spending instead of increasing it by nearly half. These are multi-year bets that signal confidence through at least 2027-2028.

  Source: Yahoo Finance

• 3. Enterprise AI adoption jumped from 55% to 78% in one year

  Explanation:

  Organizations using AI in at least one business function surged from approximately 55% in 2023 to 72-78% in 2024. Generative AI usage nearly doubled from 33% to 65% in the same period. Additionally, 50% of organizations now use AI in two or more business functions, up from under 33% in 2023. Manufacturing shows 77% adoption, while healthcare is experiencing the most dramatic year-over-year growth.

  Interpretation:

  After staying flat at 50% for seven years, adoption suddenly jumped to 78% in just one year. This means AI crossed a critical threshold where it actually works well enough for mainstream business use. The shift from testing to production (31% vs 15% previously) shows companies are using AI for real work, not just experiments. They wouldn't scale up if it wasn't delivering value.

  Source: McKinsey

• 4. ChatGPT doubled to 800M weekly users in 7 months

  Explanation:

  ChatGPT grew from 400 million weekly active users in February 2025 to 800 million by September 2025, a 100% increase in just seven months. It now processes over 1 billion queries daily with 122.6 million daily active users. The service has 15.5 million ChatGPT Plus subscribers at $20/month, 1.5 million enterprise customers, and is used in 92% of Fortune 500 companies.

  Interpretation:

  Doubling from 400 million to 800 million users in seven months is insane growth. This shows people aren't just trying ChatGPT once and leaving. They're coming back and using it regularly. The fact that 92% of Fortune 500 companies use it means AI tools are now must-haves, not nice-to-haves. If AI was hitting limits, user growth would slow down instead of speeding up. By the way, we analyze similar adoption patterns and what drives user retention in our report covering the AI Wrapper market.

  Source: NerdyNav


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• 5. OpenAI revenue projected at $12.7B for 2025, up 244% year-over-year

  Explanation:

  OpenAI's revenue reached $3.7 billion in 2024 and is projected to grow to $12.7 billion in 2025. The company hit a $10 billion annualized run rate by June 2025. Revenue breakdown for 2024 showed approximately 73% from ChatGPT subscriptions and 27% from API access. Anthropic showed even more dramatic growth, jumping from $1 billion annualized revenue in December 2024 to $4 billion by June 2025, currently targeting $9 billion by end of 2025.

  Interpretation:

  Revenue growing 244% in one year shows people are paying for AI in a big way. When leading AI companies grow revenue 9x in just two years, it means customers see real value. If AI was getting worse or staying the same, companies would spend less, not triple their spending. This kind of revenue explosion only happens when the product keeps getting better.

  Source: Epoch AI

• 6. AI VC funding surged 80% to $100B in 2024

  Explanation:

  Global AI startup funding totaled $100-103 billion in 2024, an 80-84% increase from $55.6 billion in 2023. By Q3 2025, AI startups had already raised $192.7 billion. AI captured approximately one-third of all global venture funding in 2024, rising to 43% of all VC funding by H1 2025. Q1 2025 alone saw $59.6 billion in AI funding, including OpenAI's massive $40 billion round.

  Interpretation:

  Funding is speeding up, not slowing down. AI's share of total venture capital jumped from 18% to 32% to 43% in just three years. Investors are betting bigger on AI because they expect it to keep improving and making money. This level of funding concentration shows sophisticated investors see AI as the main technology wave of the decade.

  Source: Crunchbase

• 7. Training costs double every 8-9 months as compute scales

  Explanation:

  Training costs for frontier AI models grow at 2.4x per year for amortized hardware plus energy, and 2.6x per year for cloud costs. This means costs double approximately every 8-9 months. GPT-3 cost $2-4 million to train in 2020, while GPT-4 cost $41-78 million in 2023, with Sam Altman claiming over $100 million. Projections suggest billion-dollar training runs by 2027 if trends continue.

  Interpretation:

  Costs doubling every 8-9 months while improvements get smaller is a problem. This is forcing AI labs to find smarter, cheaper ways to make progress. The question now is whether new techniques can keep delivering improvements without burning infinite money. The shift to test-time compute is one attempt to find a more sustainable path forward.

  Source: Epoch AI

• 8. AI M&A deal values exploded 288% in 2024

  Explanation:

  AI mergers and acquisitions increased 53% in deal volume and 288% in deal value in 2024 compared to 2023, totaling $49.8 billion in disclosed value. Major acquisitions include Google's $32 billion Wiz acquisition, OpenAI's $6.5 billion acquisition of Io, and Cisco's $28 billion Splunk acquisition. Deal volume grew from 430 deals in 2020 to 1,277 deals in 2024, a 197% increase.

  Interpretation:

  Companies are buying up AI capabilities aggressively because they think they're critical for staying competitive. High prices show buyers believe these acquisitions will make them money in the future. If they thought AI was maxing out, they'd wait for prices to drop instead of paying premium prices in a bidding war.

  Source: PYMNTS

• 9. OpenAI o3 achieved 87.5% on ARC-AGI benchmark, approaching human-level

  Explanation:

  OpenAI's o3 model scored 87.5% on the high-compute version and 75.7% on the low-compute version of the ARC-AGI benchmark in December 2024. This represents a 3x improvement over o1's 32% score and approaches human-level performance of around 85%. However, the high-compute version costs approximately $34,400 per task, while the low-compute version costs $200 per task.

  Interpretation:

  This shows AI can still make big leaps in reasoning ability. But here's the catch: it now costs $34,400 per task to achieve top performance. AI isn't slowing down in raw capability, but it's getting way more expensive to push the frontier forward. The old cheap method (pretraining) is maxing out, so labs are switching to expensive test-time compute instead.

  Source: ARC Prize


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• 10. Pretraining scaling showing diminishing returns, forcing strategy pivot

  Explanation:

  Multiple industry leaders reported in late 2024 that traditional pretraining scaling approaches are converging at similar capability ceilings. Models from different companies are reaching similar performance levels despite different training approaches. Labs are now pivoting to test-time compute as the new scaling direction, giving models more time to think during inference rather than just using more compute during training.

  Interpretation:

  This is a shift in method, not a slowdown in progress. The old playbook (throw more compute at training) stopped working as well, so labs are trying new approaches. Test-time compute lets models "think longer" during use instead of just training them harder upfront. It's like switching from making a bigger engine to improving aerodynamics. The real question is whether this new method can keep delivering rapid improvements, and we explore these technical transitions in our market clarity report covering AI Wrappers.

  Source: TechCrunch

• 11. Training compute grows 4-5x annually for frontier models

  Explanation:

  Training compute for frontier AI models grew at 4.1x per year from 2010-2024, with frontier models specifically showing 5.3x per year growth. GPT-3 used approximately 3×10²³ FLOP in 2020, while GPT-4 required around 2×10²⁵ FLOP in 2023, and Gemini Ultra reached approximately 5×10²⁵ FLOP in 2023.

  Interpretation:

  The industry keeps pouring 4-5x more compute into AI models each year, which shows they haven't hit a wall yet. But this growth is getting really expensive and probably can't last forever. The big question is whether this 4-5x growth rate continues through 2026-2027 or starts slowing down. Physical limits around power and chips might force a slowdown before technical limits do.

  Source: Epoch AI

• 12. Llama 4 trained on 30 trillion tokens, 2x increase over Llama 3

  Explanation:

  Meta's Llama 4 was trained on 30 trillion tokens, doubling Llama 3.1's 15 trillion tokens. The Maverick variant achieved 80.5% on MMLU Pro and 69.8% on GPQA Diamond. The Scout variant features a 10 million token context window, the industry's longest.

  Interpretation:

  Meta doubled the training data because they think more data still helps. But the real test is whether doubling the data actually makes the model twice as good. If it does, data scaling still works. If the improvement is small, it confirms that just adding more data is hitting its limit.

  Source: Llama

• 13. NVIDIA shipped 1.5-2M H100 GPUs in 2024, prices dropped 60%

  Explanation:

  NVIDIA shipped 1.5-2 million H100 GPUs in 2024, with projections of 2-3.75 million units in subsequent years. Cloud rental prices dropped from $8/hour in early 2024 to $1.90-3.50/hour currently. Major buyers like Microsoft and Meta each received approximately 150,000 units in 2023. The upcoming B200 Blackwell chip claims approximately 15x improvement over H100 and is expected to account for over 80% of high-end GPU shipments in 2025.

  Interpretation:

  Prices dropping 60% shows supply is catching up with demand. If GPU shortages were seriously holding back AI progress, prices would stay high instead of falling. The massive production ramp-up shows the hardware side of AI is in good shape. But remember, these GPUs still need massive amounts of power and data centers to run in.

  Source: Tom's Hardware

• 14. Hardware energy efficiency improves 40% annually

  Explanation:

  ML hardware energy efficiency (FLOP/s per Watt) doubles approximately every 2 years, representing 1.4x per year or 40% annual improvement. The H100 achieves 1.4×10¹² FLOP/s per watt, while Meta's MTIA reaches 2.1×10¹² FLOP/s per watt. This represents a 33.8x efficiency improvement from NVIDIA's P100 in 2016 to B100 in 2024.

  Interpretation:

  Getting 40% more efficient each year helps offset rising costs. This means every 2.5 years, you can train the same model for half the energy cost. Efficiency gains alone can keep AI progress going even if raw compute scaling slows down. It's a relief valve that prevents the economics from completely breaking down.

  Source: Epoch AI

• 15. OpenAI spent $5-7B on compute in 2024, growing to $9B in 2025

  Explanation:

  OpenAI spent approximately $5 billion on R&D compute (training plus research) and $1.8 billion on inference in 2024, totaling around $6.8 billion in cloud compute expenses. This is projected to grow to $9 billion in R&D compute for 2025. The majority went to experiments and research rather than final training runs of released models, representing 32% year-over-year growth in compute spending.

  Interpretation:

  OpenAI keeps spending more on compute each year because they think it's still worth it. If all this spending doesn't lead to better AI models, then they've hit a real limit. The fact that they're planning to spend even more in 2025 suggests their internal data shows compute is still delivering results.

  Source: Epoch AI


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• 16. arXiv AI paper submissions increased 86% year-over-year

  Explanation:

  The cs.AI category on arXiv grew from 1,742 papers in 2023 to 3,242 papers in 2024, representing an 86% increase. October 2024 alone saw 24,226 new submissions across all categories, a monthly record. Top AI sub-categories accounted for over 6,000 submissions in October 2024 alone.

  Interpretation:

  The field is super active with tons of research happening. But more papers doesn't automatically mean more breakthroughs. The real question is whether we're seeing the same rate of game-changing discoveries or just lots of small tweaks. Exponential paper growth with stable acceptance rates might mean it's getting harder to achieve truly new results.

  Source: arXiv

• 17. NeurIPS conference submissions grew 37.7% to 21,575

  Explanation:

  NeurIPS 2025 received 21,575 submissions, up from 15,671 in 2024, a 37.7% increase. ICLR 2025 submissions increased 59.8% year-over-year to 11,672 submissions. Acceptance rates remained stable at 24.5-31.75%. NeurIPS 2025 will host parallel conferences in Mexico City and Copenhagen due to venue capacity constraints.

  Interpretation:

  Huge growth in submissions shows the research community is still very active with lots of new ideas. But acceptance rates staying flat while submissions explode might mean the bar for getting published is rising. This could suggest it's getting harder to do truly novel work. The fact that they need two venue locations just shows how big the field has gotten.

  Source: arXiv

• 18. Industry produces 3.4x more notable AI models than academia

  Explanation:

  In 2023, industry produced 51 notable AI models, academia produced 15 models, and industry-academia collaborations produced 21 models. This represents a 3.4x ratio of industry to academia production. Training costs have increased over 78,000x in seven years, from under $1,000 for the original Transformer in 2017 to $78-191 million for GPT-4 and Gemini Ultra.

  Interpretation:

  Big tech companies now dominate cutting-edge AI research because they have the money. Training costs jumped 78,000x in seven years, which priced universities out of the game. This could slow innovation if companies only focus on profitable products instead of fundamental breakthroughs. But the good news is we're seeing lots of industry-academia collaborations (21 models), which combines academic creativity with corporate resources.

  Source: Stanford HAI

• 19. Data center GPU market growing 27-36% annually through 2030

  Explanation:

  Global data center GPU market is growing from $14-25 billion in 2024 to $81-230 billion by 2030-2034, representing a compound annual growth rate of 21-36%. North America hosts over 7,500 GPU-enabled data centers in 2024. Data center power capacity is growing 15-32% annually, with tracked capacity increasing approximately 20% annually from 2019-2023.

  Interpretation:

  Massive market growth shows companies are confident AI will keep scaling and making money. These are multi-year infrastructure bets that suggest the industry expects strong AI demand through at least 2027-2030. If they thought AI was about to plateau, they'd stop building. However, these predictions could change if AI hits capability limits or physical constraints prove too binding.

  Source: Grand View Research


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• 20. Epoch AI projects 10,000x compute scale-up feasible by 2030

  Explanation:

  Epoch AI says training runs of 2×10²⁹ FLOP are technically possible by 2030. That's 10,000x more compute than GPT-4. To get there, we'd need 1-5 GW data centers, over 100 million H100-equivalent GPUs, and continued hardware improvements. It's technically doable, but requires massive infrastructure buildout.

  Interpretation:

  AI has a clear technical path to keep scaling through 2030 if companies build the infrastructure. But we're talking about $100B+ data centers and massive chip production. The question isn't "can we do it technically?" but "can we afford it and is it worth it?" There's a growing gap between what's technically possible and what makes economic sense.

  Source: Epoch AI

• 21. Data center power demand projected to reach 945 TWh by 2030

  Explanation:

  Global data center electricity consumption is projected to increase from 415 TWh in 2024 to 945 TWh by 2030, a 128% increase. US consumption specifically will grow from approximately 185 TWh to 425 TWh, a 130% increase. The IEA identifies AI as the most important driver of this growth. US power demand for data centers will grow from 35 GW in 2024 to 78 GW by 2035.

  Interpretation:

  This is a huge infrastructure problem. The US alone needs 240 TWh more power, which equals all of Japan's electricity use. If the power grid can't grow fast enough, AI deployment has to slow down no matter how good the technology gets or how much money companies have. This isn't a future problem. It's happening right now.

  Source: IEA

• 22. Data center construction timelines extended 24-72 months due to power

  Explanation:

  Data center construction timelines have been extended by 24-72 months (2-6 years) specifically due to power supply constraints according to CBRE. The full development cycle now takes approximately 7 years in the US, with nearly 5 years in pre-construction phases. Grid interconnection wait times now average 5 years, with up to 7 years in Virginia.

  Interpretation:

  Building data centers now takes 2-6 years longer just because of power issues. Even with infinite money, you can't build faster than the power grid can handle. This is slowing AI deployment right now in 2024-2026. Money can't solve physics or speed up regulatory approvals.

  Source: CBRE

• 23. US would need 90% of global chip supply through 2030

  Explanation:

  London Economics analysis concluded that projected US data center growth would require over 90% of global new chip supply from 2025-2030, when the US currently accounts for less than 50% of semiconductor demand. Historical chip manufacturing capacity has grown at 6.1% annually, but supporting projected data center growth would require 10.7% annual growth.

  Interpretation:

  Chips are a hard limit. AI can't scale faster than chip factories can produce them. The US would need 90% of all new chips globally to hit projected growth, but chip production historically only grows 6% per year. Either the demand projections are too optimistic, or chip shortages will slow AI down through 2026-2030. This is another physical bottleneck money can't instantly fix.

  Source: Utility Dive

• 24. AI job postings increased 38-56% year-over-year

  Explanation:

  AI-related job postings grew 38% from 2020-2024 on LinkedIn and 56.1% through April 2025 in US data. AI job postings increased 257% from 2015-2023, compared to 52% growth in overall job listings. Specific AI roles show explosive growth: AI Engineer up 143.2%, Prompt Engineer up 135.8%, AI Content Creator up 134.5%.

  Interpretation:

  Job postings growing way faster than the general market shows companies want more AI talent badly. If AI was plateauing, they'd slow down hiring. The 28% salary premium ($18,000 extra per year) shows there aren't enough skilled people to meet demand. Talent might be the real bottleneck holding back AI deployment, and we dive deeper into these workforce dynamics in our report to build a profitable AI Wrapper.

  Source: Autodesk

• 25. LinkedIn members adding AI skills increased 142x

  Explanation:

  As of late 2024, there's been a 142-fold increase in LinkedIn members globally adding AI aptitude skills like ChatGPT and Copilot to their profiles. Additionally, 160% more non-technical professionals are using LinkedIn Learning courses to build AI aptitude. Seven out of every 1,000 LinkedIn members globally (0.7%) are AI engineering talent, representing a 130% increase since 2016.

  Interpretation:

  Tons of people are learning AI skills, which shows AI is being used commercially across all kinds of jobs. But only 0.7% have real engineering-level skills, while job postings exploded 38-56%. This gap means there aren't enough skilled people. If we can't train talent fast enough, it'll slow down how quickly companies can actually deploy AI.

  Source: Microsoft


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• 26. AI engineer median salary reaches $136K, top tier earns $239K-$2M

  Explanation:

  US Bureau of Labor Statistics reports median AI engineer salary of $136,620. Entry-level at top companies like LinkedIn earn $239,000 total compensation. Senior AI researchers at Big Tech earn $500,000-$2,000,000 annually including stock and bonuses. Specific company ranges include Google ($200,000-$350,000), Microsoft ($250,000-$450,000), OpenAI ($300,000-$500,000), and NVIDIA ($340,000-$390,000).

  Interpretation:

  Salaries going through the roof shows there's a massive shortage of AI talent. Companies paying $500K-$2M for senior people are desperate to hire. If finding talent is harder than building better AI, then the real slowdown might come from not having enough people, not from the technology hitting limits.

  Source: Levels.fyi

• 27. 70% of top US AI researchers are foreign-born or foreign-educated

  Explanation:

  Among top AI researchers in the United States, 70% were foreign-born or foreign-educated. Most common origins are China (50 researchers), India (14), UK (10), and Taiwan (9). China's share of global top AI talent jumped from 11% in 2019 to 28% in 2022, approaching US levels. Immigration difficulties are cited by 60% of non-US citizen AI PhDs in the US versus only 12% in other countries.

  Interpretation:

  The US relies heavily on foreign talent for AI (70% of top researchers). This makes US AI vulnerable to immigration policy changes. China went from 11% to 28% of global AI talent in three years, showing they're keeping their talent at home. Immigration bottlenecks (50-year green card waits for some, visa approval rates dropping from 46% to 15%) could push talent away from the US and slow progress.

  Source: CSET Georgetown

• 28. EU AI Act imposes fines up to €35M or 7% of revenue

  Explanation:

  The EU AI Act, which entered force August 1, 2024 with full applicability by August 2, 2026, imposes maximum fines of €35 million or 7% of global annual revenue (whichever is higher) for non-compliance. Annual compliance costs are estimated at approximately €52,000 per high-risk AI model, though actual costs are likely higher.

  Interpretation:

  The big regulations are in place but won't fully kick in until 2026-2027. Right now, they're not really slowing AI down because companies are still figuring out compliance. But high fines (€35M or 7% of revenue) plus €52K per model costs could hurt smaller companies more than big ones, which might consolidate the market.

  Source: European Commission

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