Reasons AI Development Is Advancing Quickly

October 2024 marked a critical inflection point in AI development. Hidden in the performance data, a subtle elbow emerged - a mathematical harbinger that would prove prophetic. What began as a minor statistical anomaly has since exploded into exponential growth. Since then AI performance has surged attaining a new trajectory, a new slope - no longer linear but geometric. Segmenting out the models by size & type reveals a striking shift in innovation’s source. While model size drove the initial wave of improvements, & smaller models showed promise in the early fall, neither factor fully explains the recent acceleration. The breakthrough appears to stem from fundamental architectural advances & training methodologies. Segmenting out the models by size and type, the source of the innovation is clear. No longer model size which drove the initial wave of improvements, nor the improvements in the smaller models of the early fall. It’s reasoning - ask a model to articulate its thought process, consider alternatives, & ultimately select one. With improved accuracy, fewer errors, & the ability to conduct deep research - work extending for fifteen minutes or more, the potential of the technology has never felt more tangible. Recently, Alberto Romero suggested that the differences between the performance of AI models is much less important than the difference between people’s ability to use them well. A sophisticated user of AI - like any skilled worker - can produce much more than a novice. As these models continue to improve, it may be less important for management teams to track relative benchmarks of AI performance & much more to train their teams & reimagine their workflows.

We’re no longer just scaling computing power. We’re using compute to scale intelligence itself. That’s what makes this moment historically significant. For sixty years, progress in computing followed Moore’s Law—transistor density doubling roughly every two years. But AI is advancing on a far steeper curve. Today, frontier model capabilities are improving on a cadence closer to every six months—an order of magnitude faster than classical hardware scaling. The underlying principle is both simple and radical: when you increase data, compute, and model complexity, intelligence emerges. Scaling laws show that larger models—given sufficient compute and high-quality data—become predictably more capable. In just over a decade, we’ve gone from neural nets that could identify cats to systems that can draft legal briefs, write production-grade code, generate scientific hypotheses, and outperform top human competitors in mathematics, strategy, and reasoning tasks. This is no longer “software” in the traditional sense. It is a new form of intelligence—synthetic, scalable, rapidly compounding, and increasingly able to take meaningful action in the real world. The geopolitical, economic, and societal implications of this shift are only beginning to unfold—and they will redefine global power in the decades ahead.

For me, three major advancements defined the AI landscape in 2025. First, the rise of agentic AI. We have moved well beyond chat interfaces to AI systems that can retrieve information, reason through options and take action across enterprise tools. Agent designers, orchestration layers and enterprise-grade frameworks mean organisations can now deploy AI that assists with real work, from sales preparation to financial analysis to HR case resolution. This shift has lowered the barrier to meaningful adoption and is pushing companies to rethink workflows, skills and operating models. Second, the emergence of world models. These models give AI a richer understanding of context, space, time and causality. They can simulate how the world works rather than just predict the next token. This unlocks more reliable planning, better judgment and far safer autonomy. It also lays the foundation for AI that can coordinate tasks, operate machinery and reason about complex multi-step processes. In many ways, world models are the missing link between today’s narrow AI systems and the more general capabilities we expect in the future. Third, the acceleration of physical AI, especially humanoid robots. We have seen huge progress in locomotion, manipulation and cost efficiency. Several prototypes are already being tested in factories, logistics centres and retail environments. What is changing is not just the hardware, but the intelligence that drives it. Combining robotics with advanced foundation models and world models brings us much closer to general-purpose robots that can adapt, learn and operate safely alongside humans. Taken together, these developments show how rapidly AI is moving from generating content to understanding the world, taking action and working in physical space. It feels like a genuine step toward AI that is more capable, more useful and more aligned with real-world needs. What has been the key AI advancement for you in 2025? #LinkedInNewsEurope

Stanford’s 2025 AI Index Report makes the incredible speed of AI improvement crystal clear. Over the past year, benchmark performance has increased by up to 67 percentage points on new, more challenging tests. For example, coding benchmarks jumped from solving 4.4% of problems to over 70%. Anyone still thinking about advancements in linear terms will be sidelined. Here’s what this means for founders building in the AI space: → AI capabilities are evolving at breakneck speed. The tools we build on are improving so fast that yesterday’s benchmarks are already obsolete. If you’re not iterating quickly, you will fall behind. → Costs are plummeting. The inference cost for GPT-3.5-level performance dropped 280x in two years. This means that near-endless intelligence is no longer just for tech giants; it’s accessible for even the leanest start-ups. → Adoption is exploding. From healthcare devices to autonomous vehicles, AI is moving out of labs and into real-world applications at scale. This is your moment to embed AI into your product roadmap. But here’s the catch: rapid AI progress comes with new challenges. The report highlights ongoing issues with reasoning, safety, and equitable access. As founders, we can’t just chase growth; we must build responsibly, with human-centered values at the core

𝗜𝗳 𝘆𝗼𝘂 𝗳𝗼𝗹𝗹𝗼𝘄 𝘁𝗵𝗲 𝗻𝗲𝘄𝘀, 𝘆𝗼𝘂’𝘃𝗲 𝗽𝗿𝗼𝗯𝗮𝗯𝗹𝘆 𝘀𝗲𝗲𝗻 𝗶𝘁 𝗮𝗹𝗹: 𝗔𝗜 𝗶𝘀 𝗯𝗼𝗼𝗺𝗶𝗻𝗴. 𝗔𝗜 𝗶𝘀 𝗼𝘃𝗲𝗿𝗵𝘆𝗽𝗲𝗱. 𝗔𝗜 𝘄𝗶𝗹𝗹 𝘀𝗮𝘃𝗲 𝘂𝘀. 𝗔𝗜 𝘄𝗶𝗹𝗹 𝗱𝗲𝘀𝘁𝗿𝗼𝘆 𝗷𝗼𝗯𝘀. The Stanford University AI Index 2025 cuts through all of it. Produced by the Institute for Human-Centered Artificial Intelligence, it’s one of the most respected and data-driven reports on the state of AI today. Over 400+ pages of concrete insights — from technical benchmarks and real-world adoption to policy shifts, economic impact, education, and public sentiment. 𝗧𝗵𝗲 2025 𝗲𝗱𝗶𝘁𝗶𝗼𝗻 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝗹𝗮𝘀𝘁 𝘄𝗲𝗲𝗸. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 12 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: 1. 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀 𝗮𝗿𝗲 𝗯𝗲𝗶𝗻𝗴 𝗰𝗿𝘂𝘀𝗵𝗲𝗱. ➝ AI performance on complex reasoning and programming tasks surged by up to 67 percentage points in just one year. 2. 𝗔𝗜 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝘀𝘁𝘂𝗰𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗮𝗯. ➝ 223 FDA-approved AI medical devices. Over 150,000 autonomous rides weekly from Waymo. This is mainstream adoption. 3. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝘀 𝗴𝗼𝗶𝗻𝗴 𝗮𝗹𝗹-𝗶𝗻.  ➝ $109B in U.S. private AI investment. 78% of organizations using AI. Productivity gains are no longer theoretical. 4. 𝗧𝗵𝗲 𝗨.𝗦. 𝗹𝗲𝗮𝗱𝘀 𝗶𝗻 𝗾𝘂𝗮𝗻𝘁𝗶𝘁𝘆—𝗖𝗵𝗶𝗻𝗮’𝘀 𝗰𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝘂𝗽 𝗼𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆.  ➝ Chinese models now rival U.S. models on MMLU, HumanEval, and more. Global AI is becoming a multi-polar game. 5. 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗶𝘀 𝗹𝗮𝗴𝗴𝗶𝗻𝗴 𝗯𝗲𝗵𝗶𝗻𝗱 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻. ➝ Incidents are rising, but standardized RAI benchmarks and audits are still rare.   Governments are stepping in faster than vendors. 6. 𝗚𝗹𝗼𝗯𝗮𝗹 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗺 𝗶𝘀 𝗿𝗶𝘀𝗶𝗻𝗴—𝗯𝘂𝘁 𝗻𝗼𝘁 𝗲𝘃𝗲𝗻𝗹𝘆.   ➝ 83% of people in China are optimistic about AI. In the U.S., that number is just 39%. 7. 𝗔𝗜 𝗶𝘀 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗰𝗵𝗲𝗮𝗽𝗲𝗿, 𝘀𝗺𝗮𝗹𝗹𝗲𝗿, 𝗮𝗻𝗱 𝗳𝗮𝘀𝘁𝗲𝗿.  ➝ The cost of GPT-3.5-level inference dropped 280x in two years. Open-weight models are nearly matching closed ones. 8. 𝗚𝗼𝘃𝗲𝗿𝗻𝗺𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗶𝗻𝘃𝗲𝘀𝘁𝗶𝗻𝗴.  ➝ From Canada’s $2.4B to Saudi Arabia’s $100B push—states aren’t watching from the sidelines anymore. 9. 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗲𝘅𝗽𝗮𝗻𝗱𝗶𝗻𝗴—𝗯𝘂𝘁 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗹𝗮𝗴𝘀. ➝ Access is improving, but infrastructure gaps and lack of teacher training still limit global reach. 10. 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗶𝘀 𝗱𝗼𝗺𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁.   ➝ 90% of top AI models now come from companies—not academia. The gap between top players is shrinking fast. 11. 𝗔𝗜 𝗶𝘀 𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝘀𝗰𝗶𝗲𝗻𝗰𝗲.   ➝ AI-driven breakthroughs in physics, chemistry, and biology are earning Nobel Prizes and Turing Awards. 12. 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗿𝗲𝗺𝗮𝗶𝗻𝘀 𝘁𝗵𝗲 𝗰𝗲𝗶𝗹𝗶𝗻𝗴.   ➝ Despite all the progress, models still struggle with logic-heavy tasks. Precision is still a challenge. You can download the full report FREE here: https://lnkd.in/dzzuE5tN

The Ascent of AI Now that we know what AI is all about, the question to ask is why there is this sudden buzz around AI. The talk of AI has been going around since the 1950s, but it is only in the last four years that AI has suddenly matured. Over the last few years, the price-performance ratio of computers has improved phenomenally. Because of this, the availability of computing power has gone up. The amount of data available on the internet in the form of words, images, and sounds has also increased exponentially. Finally, the algorithms that instruct the computer on how to reason have also improved. As a result of these three factors, the ability of computers to understand the world around them has increased dramatically. This has fueled the recent AI boom. While there is still some way to go for artificial intelligence to match human intelligence, there are areas where AI already has significant advantages over the human brain. The primary advantage of AI is its ability to refer to a huge number of facts. Currently, AI models have ingested billions of documents, images, and sounds. These are far more than what human memory can ever retain. The second advantage is the speed at which it can access all those documents, process them, build patterns and inferences, and make them accessible for any query. This ability is also much greater than that of the human brain. Thirdly, whereas all the information in a human brain cannot be transferred from one brain to another, all the information contained in one computer can be easily transferred to other computers, thereby significantly increasing the speed of learning. The extent of learning of artificial intelligence depends on three things: one, how much information it has accumulated from which it learns; two, the quality of that information; and three, the software algorithm it uses to generate connections between all the data it has gathered. The quality of results that AI produces has increased significantly over time because of the larger and larger amounts of data it has been ingesting. It is because of this ability that when you throw a query to AI asking for information about specific subjects, and if your query is properly framed, the AI can spit out the answer because of its ability to link every next word from its previous word based on how often the second word has followed the first in all the data it has so far collected. This is the technology used by ChatGPT for coming out with those extraordinary answers. This is also the same technology used for image recognition – the technology that will someday become very prevalent in the area of autonomous driving. The newly developed strengths of AI present an extraordinary opportunity for mankind. And to that, we will turn in the next post.