The pace and scale of artificial intelligence (AI) development is now outstripping every previous tech revolution, according to new landmark reports.
Faster Than Anything We’ve Seen Before
Some of the latest data confirms that AI really is moving faster than anything that’s come before it. That’s the key message from recent high-profile reports including Mary Meeker’s new Trends – Artificial Intelligence report and Stanford’s latest AI Index, both released in spring 2025. Together, the data they present highlights an industry surging ahead at a speed that’s catching even seasoned technologists off guard.
Meeker, the influential venture capitalist once dubbed “Queen of the Internet”, hasn’t published a trends report since 2019 but it seems that the extraordinary pace of AI progress has lured her back, and her new 340-page analysis uses the word “unprecedented” more than 50 times (with good reason).
“Adoption of artificial intelligence technology is unlike anything seen before in the history of computing,” Meeker writes. “The speed, scale, and competitive intensity are fundamentally reshaping the tech landscape.”
Stanford’s findings echo this. For example, its 2025 AI Index Report outlines how generative AI in particular has catalysed a rapid transformation, with advances in model size, performance, use cases, and user uptake occurring faster than academic and policy communities can track.
The Numbers That Prove the Surge
In terms of users, OpenAI’s ChatGPT generative AI chatbot hit 100 million users in two months and it’s now approaching 800 million monthly users just 17 months after launch. No platform in history has scaled that quickly – not Google, not Facebook, not TikTok.
Business adoption of AI is rising rapidly. For example, according to Stanford’s AI Index 2025, more than 70 per cent of surveyed global companies are now either actively deploying or exploring the use of generative AI. This represents a significant increase from fewer than 10 per cent just two years earlier. At the same time, worldwide investment in AI reached $189 billion in 2023, with technology firms allocating record levels of funding to infrastructure, research, and product development.
Cost of Accessing AI Falling
It seems that the cost of accessing AI services is also falling sharply. For example, Meeker’s Trends – Artificial Intelligence report notes that inference costs, i.e. the operational cost of running AI models, have declined by a massive 99.7 per cent over the past two years. Based on Stanford’s calculations, this means that businesses are now able to access advanced AI capabilities at a fraction of the price paid in 2022.
What’s Driving This Acceleration?
Several factors are converging at once to drive this acceleration. These are:
– Hardware efficiency leaps. Nvidia’s 2024 Blackwell GPU reportedly uses 105,000x less energy per token than its 2014 Kepler chip! At the same time, custom AI chips from Google (TPU), Amazon (Trainium), and Microsoft (Athena) are rapidly improving performance and slashing energy use.
– Cloud hyperscale investment. The world’s biggest tech firms are betting big on AI infrastructure. Microsoft, Amazon, and Google are all racing to expand their cloud platforms with AI-specific hardware and software. As Meeker puts it, “These aren’t side projects — they’re foundational bets.”
– Open-source momentum. Hugging Face, Mistral, Meta’s LLaMA, and a host of Chinese labs are releasing increasingly powerful open-source models. This is democratising access, increasing competition, and reducing costs — all of which accelerate adoption.
– Government and sovereign AI initiatives. National efforts, particularly in China and the EU, are helping to fund AI infrastructure and drive localisation. These projects are pushing innovation outside Silicon Valley at a rapid pace.
– Developer ecosystem growth. Millions of developers are now building on top of generative AI APIs. Google’s Gemini, OpenAI’s GPT, Anthropic’s Claude, and others have created platforms where innovation compounds rapidly. As Stanford notes, “Industry now outperforms academia on nearly every AI benchmark.”
AI Agents – From Chat to Task Execution
One major change in the past year has been the move beyond simple chatbot interfaces. For example, so-called “AI agents”, i.e. systems that can plan and carry out multi-step tasks, are emerging quickly. This includes tools that can search the web, book travel, summarise documents, or even write and run code autonomously.
Companies like OpenAI, Google DeepMind, and Adept are racing to build these agentic systems. The goal is to create AI that can do, not just respond. This could fundamentally change knowledge work, and is already being trialled in areas like customer service, legal research, and software testing.
The Message
For businesses, the message appears to be that there is a need to adapt quickly, or risk falling behind.
Meeker’s report emphasises that AI is already “redefining productivity”, with tools delivering step changes in output for tasks like drafting, data analysis, code generation, and document processing. Many enterprise users report 20–40 per cent efficiency gains when integrating AI into daily workflows.
However, it’s not just about performance. Falling costs and rising model capabilities mean that AI is becoming accessible to even small businesses, not just tech giants. Whether it’s automating customer support or generating marketing copy, SMEs now have access to tools that rival those of major players.
From a market perspective, however, things are less clear-cut. While revenue is rising – OpenAI is projected to hit $3.4 billion in 2025, up from around $1.6 billion last year – most AI firms are still burning through capital at unsustainable rates.
Also, training large models is very expensive. GPT-4, for example, reportedly cost $78 million just to train, and newer models will likely exceed that. As Meeker cautions: “Only time will tell which side of the money-making equation the current AI aspirants will land.”
Challenges, Criticism, and Growing Pains
Despite the enthusiasm, not everything is rosy. The pace of AI’s rise has sparked a host of issues, such as:
– Energy use and environmental impact. Training and running AI models consumes vast amounts of electricity. Even with hardware improvements, Stanford warns of “significant sustainability challenges” as model sizes increase.
– AI misuse and disinformation. The Stanford report logs a steep rise in reported AI misuse incidents, particularly involving deepfakes, scams, and electoral disinformation. Regulatory frameworks remain patchy and reactive.
– Labour market upheaval. Stanford data shows a clear impact on job structures, particularly in content-heavy and administrative roles. While AI augments some jobs, it also displaces others and workers, employers, and policymakers are struggling to keep up.
– Profitability concerns. While AI infrastructure is growing rapidly, it’s not yet clear which companies will convert hype into long-term revenue. Even the most well-funded players face stiff competition, regulatory scrutiny, and the risk of market saturation.
What Does This Mean For Your Business?
It seems that the combination of surging adoption, falling costs, and rising capability is placing AI at the centre of digital transformation efforts across nearly every sector. For global businesses, the incentives to engage with AI tools are growing rapidly, with productivity benefits now being demonstrated at scale. At the same time, the pace of change is creating new risks, particularly in terms of workforce disruption, misuse, and unsustainable infrastructure demands, that still lack clear long-term responses.
For UK businesses, the implications are becoming increasingly difficult to ignore. As global competitors embed AI into operations, decision-making, and service delivery, organisations that delay may struggle to keep pace. At the same time, the availability of open-source models and accessible APIs means that smaller firms and startups are also in a position to benefit, if they can navigate the complexity and choose the right tools. Key sectors such as financial services, legal, healthcare, and logistics are already seeing early AI-driven efficiencies, and pressure is mounting on others to follow suit.
Policy makers, regulators, and infrastructure providers also have critical roles to play. Whether it is through ensuring fair access to computing resources, investing in AI literacy and skills, or designing governance frameworks that can evolve with the technology, stakeholders across the economy will need to respond quickly and collaboratively. While the financial picture remains uncertain, what is now clear is that AI is no longer a frontier science, but is a core driver of technological change, and one that is advancing at a pace few expected.
