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. 𝗖𝗼𝗺𝗽𝗹��𝘅 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

My biggest takeaways from Fei-Fei Li: 1. Just nine years ago, calling yourself an AI company was considered bad for business. Nobody believed the technology would work back in 2016. By 2017, companies started embracing the term. Today, virtually every company calls itself an AI company. 2. The modern AI revolution started with a simple but overlooked insight from Fei Fei: AI models needed large amounts of labeled data. While researchers focused on sophisticated mathematical models and algorithms, she realized the missing ingredient was data. Her team spent three years working with tens of thousands of people across more than 100 countries to label 15 million images, creating ImageNet. This dataset became the foundation for today’s AI systems. 3. The human brain’s efficiency vastly exceeds current AI systems. Humans operate on about 20 watts of power—less than any lightbulb—yet accomplish tasks that require AI systems to use massive computing resources. Current AI still can’t do things elementary school children find easy. 4. Simply scaling current approaches won’t be enough. While adding more data, computing power, and bigger models will continue advancing AI, fundamental innovations are still needed. Throughout AI history, simpler approaches combined with enormous datasets consistently outperformed sophisticated algorithms with limited data. 5. Breakthrough technologies often start as toys or fun experiments before changing the world. ChatGPT was tweeted by Sam Altman as “Here’s a cool thing we’re playing with” and became the fastest-growing product in history. What seems like play today might transform civilization tomorrow. 6. Spatial intelligence is as crucial as language for real-world applications. In emergency situations like fires or natural disasters, first responders organize rescue efforts through spatial awareness, movement coordination, and understanding physical environments—not primarily through language. This is why world models that understand three-dimensional space represent the next frontier beyond text-based chatbots. 7. Physical robots face much harder challenges than self-driving cars, which took 20 years from prototype to street deployment and still aren’t finished. Self-driving cars are metal boxes moving on flat surfaces, trying not to touch anything. Robots are three-dimensional objects moving in three-dimensional spaces, specifically trying to touch and manipulate things. This makes robotics far harder than creating chatbots. 8. Everyone has a role in AI’s future, regardless of profession. Whether you’re an artist using AI tools to tell unique stories, a farmer participating in community decisions about AI deployment, or a nurse who could benefit from AI assistance in an overworked health-care system, you can and should engage with this technology. AI should augment human dignity and agency, not replace it—which means both using AI as a tool and having a voice in how it’s governed.

Recent research is advancing two critical areas in AI: autonomy and reasoning, building on their strengths to make them more autonomous and adaptable for real-world applications. Here is a summary of a few papers that I found interesting and rather transformative: • 𝐋𝐋𝐌-𝐁𝐫𝐚𝐢𝐧𝐞𝐝 𝐆𝐔𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 (𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭): These agents use LLMs to interact directly with graphical interfaces—screenshots, widget trees, and user inputs—bypassing the need for APIs or scripts. They can execute multi-step workflows through natural language, automating tasks across web, mobile, and desktop platforms. • 𝐀𝐅𝐋𝐎𝐖: By treating workflows as code-represented graphs, AFLOW dynamically optimizes processes using modular operators like “generate” and “review/revise.” This framework demonstrates how smaller, specialized models can rival larger, general-purpose systems, making automation more accessible and cost-efficient for businesses of all sizes. • 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 (𝐑𝐀𝐑𝐄): RARE integrates real-time knowledge retrieval with logical reasoning steps, enabling LLMs to adapt dynamically to fact-intensive tasks. This is critical in fields like healthcare and legal workflows, where accurate and up-to-date information is essential for decision-making. • 𝐇𝐢𝐀𝐑-𝐈𝐂𝐋:: Leveraging Monte Carlo Tree Search (MCTS), this framework teaches LLMs to navigate abstract decision trees, allowing them to reason flexibly beyond linear steps. It excels in solving multi-step, structured problems like mathematical reasoning, achieving state-of-the-art results on challenging benchmarks. By removing the reliance on APIs and scripts, systems like GUI agents and AFLOW make automation far more flexible and scalable. Businesses can now automate across fragmented ecosystems, reducing development cycles and empowering non-technical users to design and execute workflows. Simultaneously, reasoning frameworks like RARE and HiAR-ICL enable LLMs to adapt to new information and solve open-ended problems, particularly in high-stakes domains like healthcare and law. These studies highlight key emerging trends in AI: 1. APIs and Simplifying Integration: A major trend is the move away from API dependencies, with AI systems integrating directly into existing software environments through natural language and GUI interaction. This addresses one of the largest barriers to AI adoption in organizations. 2. Redefining User Interfaces: Traditional app interfaces with icons and menus are being reimagined. With conversational AI, users can simply ask for what they need, and the system executes it autonomously. 3. Tackling More Complex Tasks Autonomously: As reasoning capabilities improve, AI systems are expanding their range of activities and elevating their ability to plan and adapt. As these trends unfold, we’re witnessing the beginning of a new era in AI. Where do you see the next big research trends in AI heading?

VCs love to say “AI is moving fast.” Sarah Guo and Mike Vernal think that’s the wrong frame. At a two-voices Icons dinner I hosted, Sarah (founding partner at Conviction, ex-Greylock) and Mike (investor at Conviction, ex-partner at Sequoia, ex-Facebook VP of Product) argued something more radical: AI isn’t just moving fast. It’s moving faster than any market, regulator, or organization can absorb. They’re not watching from the sidelines. They’ve backed the founders building at the frontier—Rippling, Notion, Clay, Harvey, Cursor. Here are 5 takeaways that stuck with me: 1. The recipe to AGI already exists. Labs like OpenAI and Anthropic don’t need $100B breakthroughs—capabilities will keep compounding. The real challenge is turning reasoning into usable workflows. 2. “Bad markets” can flip overnight. Defense tech was unfundable a decade ago. Today, it’s one of the hottest categories in venture. Timing, not the market, was broken. 3. Customer obsession beats positioning debates. The winners aren’t arguing “wrapper vs. foundation.” They’re mapping every pain point inside a Fortune 500 client’s org chart. 4. Feedback loops compound. Rippling ships on monthly cycles. Some CEOs demand answers by 1pm, not next week. Startups are turn-based games—shorter turns mean faster compounding. 5. There’s a decade of alien products waiting. Sarah put it simply: “There’s a decade worth of products to be built with this alien technology. The only question is—who’s fast and obsessed enough to build them?” The lesson for founders and scientists: Don’t wait for perfect markets or perfect infra. Shrink your feedback loops, live inside your customer’s head, and build. Because the question isn’t whether AI is moving fast. The question is whether we can keep up.

𝗦𝗽𝗲𝗲𝗱, 𝗔𝗜, 𝗔𝗻𝗱 𝗧𝗵𝗲 𝗡𝗲𝘄 𝗚𝗹𝗼𝗯𝗮𝗹 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 Scale once defined power. Now speed, intelligence, and execution define who wins. Artificial intelligence has changed the geometry of competition. We are no longer living in an era where the largest organizations automatically dominate simply because they are large. We are living in an era where small, focused companies can move faster than entire nations once could. AI has collapsed time, compressed expertise, and removed many of the structural advantages that historically protected incumbents. Globally, the race in advanced AI and embodied systems has intensified. Large economies are investing heavily, publishing aggressively, and scaling manufacturing at historic rates. Performance gaps between leading AI models are narrowing, and open approaches are allowing rapid iteration even under constraint. At the same time, private innovation ecosystems continue to produce the most influential breakthroughs, pushing reasoning, perception, and autonomy forward at an accelerating pace. Here is where small companies quietly change the game. They do not need multi year planning cycles to adopt new models. They do not require institutional consensus to integrate AI into physical systems, workflows, or products. They can test, discard, rebuild, and redeploy continuously. While large organizations optimize for stability and scale, small teams optimize for learning velocity. In robotics, autonomous systems, and applied AI, speed matters more than perfection. The ability to move from idea to prototype to deployment faster than competitors determines who sets standards and who follows them. Small companies can integrate multimodal AI, experiment with embodiment, and iterate hardware and software together without waiting for organizational alignment. That agility compounds. Large entities will always matter. They scale infrastructure, manufacturing, and distribution. But history shows that transformational shifts rarely originate inside rigid systems. They begin with small groups that see clearly, move decisively, and exploit new tools before anyone else can react. AI has made intelligence abundant. Speed has become scarce. The organizations that understand this reality and design themselves around rapid learning, not bureaucracy, will define the next decade.

OpenAI CEO Sam Altman thinks we’ll see a 1-person billion-dollar company. A single-employee unicorn sounds ridiculous ... But it’s the logical conclusion of a trend we’re already witnessing: → WhatsApp: • Acquired $19 billion in 2014 • With just 55 employees • $345 million per employee. → Instagram: • Acquired for $1 billion in2012 • With only 13 employees • Around $77 million per employee. Technology’s enabled small teams to create enormous value over the past decade 5 reasons AI will take this trend to its extreme conclusion. 1) AI as a Force Multiplier: GPT-4 can already code, write, and analyze as well as seasoned pros - but at massive scale. Future AI will likely manage complex operations autonomously. 2) Exponential Growth in Computing Power: The spirit of Moore's Law - computing power doubling every two years -remains valid. Quantum computing promises to solve in seconds, problems that would take traditional computers 100s of years. 3) Democratization of AI: Automated Machine Learning and open-source models are making advanced AI accessible to non-experts. This levels the playing field between ‘solopreneurs’ and tech giants. 4) Network Effects in the AI Era: AI-powered platforms like TikTok's recommendation algorithm can rapidly scale to billions of users with minimal human intervention. 5) Convergence with Other Tech: The synergy between AI and technologies like CRISPR or brain-computer interfaces could create trillion-dollar markets practically overnight. This convergence creates a plausible scenario where a single visionary with the right AI tools could innovate, operate, and scale a company with unimaginable efficiency. The primary constraint may no longer be team size or capital. But the scope of an entrepreneur's vision and her ability to orchestrate AI systems. We're entering an era where solo founders will be able to: • Launch complex software products in days, not months • Manage global operations 24/7 without employees • Conduct market research and optimize strategies in real-time • Provide personalized services to millions of customers simultaneously • Entire industries will be restructured and value creation will be redefined. We may soon see a unicorn with a single rider. ____________________________ Hi, I’m Richard Stroupe, a 3x Entrepreneur, and Venture Capital Investor I help early stage tech founders turn their startups into VC magnets. Send me a DM to see if you qualify for hands-on guidance to nail your niche and wow investors.