Navigating AI Competition

𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆, 𝘆𝗼𝘂 𝗳𝗶𝗿𝘀𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝗼𝗹𝗶𝗱 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗲𝗻𝗳𝗼𝗿𝗰𝗲 𝘀𝘁𝗿𝗶𝗰𝘁 𝗱𝗮𝘁𝗮 𝗵𝘆𝗴𝗶𝗲𝗻𝗲. Getting your house in order is the foundation for delivering on any AI ambition. The MIT Technology Review — based on insights from 205 C-level executives and data leaders — lays it out clearly: 𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗱𝗼 𝗻𝗼𝘁 𝗳𝗮𝗰𝗲 𝗮𝗻 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘆 𝗳𝗮𝗰𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗿𝗶𝘀𝗸 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Therefore, many firms are still stuck in pilots, not production. Changing that requires strong data foundations, scalable architectures, trusted partners, and a shift in how companies think about creating real value with AI. Because pilots are easy, BUT scaling AI across the enterprise is hard. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: ⬇️ 1. 95% 𝗼𝗳 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 — 𝗯𝘂𝘁 76% 𝗮𝗿𝗲 𝘀𝘁𝘂𝗰𝗸 𝗮𝘁 𝗷𝘂𝘀𝘁 1–3 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀:   ➜ The gap between ambition and execution is huge. Scaling AI across the full business will define competitive advantage over the next 24 months. 2. 𝗗𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗹𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀: ➜ Without curated, accessible, and trusted data, no AI strategy can succeed — no matter how powerful the models are. 3. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗮𝗿𝗲 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 — 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗴𝗼𝗼𝗱 𝘁𝗵𝗶𝗻𝗴:   ➜ 98% of executives say they would rather be safe than first. Trust, not speed, will win in the next AI wave. 4. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱, 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝘄𝗶𝗹𝗹 𝗱𝗿𝗶𝘃𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗲:  ➜ Generic generative AI (chatbots, text generation) is table stakes. True differentiation will come from custom, domain-specific applications. 5. 𝗟𝗲𝗴𝗮𝗰𝘆 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗱𝗿𝗮𝗴 𝗼𝗻 𝗔𝗜 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻𝘀:  ➜ Firms sitting on fragmented, outdated infrastructure are finding that retrofitting AI into legacy systems is often more costly than building new foundations. 6. 𝗖𝗼𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗿𝗲 𝗵𝗶𝘁𝘁𝗶𝗻𝗴 𝗵𝗮𝗿𝗱: ➜ From GPUs to energy bills, AI is not cheap — and mid-sized companies face the biggest barriers. Smart firms are building realistic ROI models that go beyond hype. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗳𝘂𝘁𝘂𝗿𝗲-𝗿𝗲𝗮𝗱𝘆 𝗔𝗜 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗵𝗮𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗺𝗼𝗱𝗲𝗹 𝗿𝗲𝗹𝗲𝗮𝘀𝗲.   𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 — 𝗱𝗮𝘁𝗮, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗥𝗢𝗜 — 𝘁𝗼𝗱𝗮𝘆.

The buzz over DeepSeek this week crystallized, for many people, a few important trends that have been happening in plain sight: (i) China is catching up to the U.S. in generative AI, with implications for the AI supply chain. (ii) Open weight models are commoditizing the foundation-model layer, which creates opportunities for application builders. (iii) Scaling up isn’t the only path to AI progress. Despite the massive focus on and hype around processing power, algorithmic innovations are rapidly pushing down training costs. About a week ago, DeepSeek, a company based in China, released DeepSeek-R1, a remarkable model whose performance on benchmarks is comparable to OpenAI’s o1. Further, it was released as an open weight model with a permissive MIT license. At Davos last week, I got a lot of questions about it from non-technical business leaders. And on Monday, the stock market saw a “DeepSeek selloff”: The share prices of Nvidia and a number of other U.S. tech companies plunged. (As of the time of writing, some have recovered somewhat.) Here’s what I think DeepSeek has caused many people to realize: China is catching up to the U.S. in generative AI. When ChatGPT was launched in November 2022, the U.S. was significantly ahead of China in generative AI. Impressions change slowly, and so even recently I heard friends in both the U.S. and China say they thought China was behind. But in reality, this gap has rapidly eroded over the past two years. With models from China such as Qwen (which my teams have used for months), Kimi, InternVL, and DeepSeek, China had clearly been closing the gap, and in areas such as video generation there were already moments where China seemed to be in the lead. I’m thrilled that DeepSeek-R1 was released as an open weight model, with a technical report that shares many details. In contrast, a number of U.S. companies have pushed for regulation to stifle open source by hyping up hypothetical AI dangers such as human extinction. It is now clear that open source/open weight models are a key part of the AI supply chain: Many companies will use them. If the U.S. continues to stymie open source, China will come to dominate this part of the supply chain and many businesses will end up using models that reflect China’s values much more than America’s. Open weight models are commoditizing the foundation-model layer. As I wrote previously, LLM token prices have been falling rapidly, and open weights have contributed to this trend and given developers more choice. OpenAI’s o1 costs $60 per million output tokens; DeepSeek R1 costs $2.19. This nearly 30x difference brought the trend of falling prices to the attention of many people. [...] [Reached length limit. Full text: https://lnkd.in/grbFH4D6 ]

Sure, anybody can call OpenAI APIs to access cutting-edge models, but let’s be real: the true opportunity for businesses isn’t just plugging into those APIs. It’s about leveraging your most unique competitive advantage: your data. Data is the foundation of any successful AI system. Yet, the journey from raw data to actual value has many challenges: 1. Not enough data? Your model can’t be generalized. 2. Poor-quality data? Expect poor-quality results. 3. Nonrepresentative data? Say hello to biased predictions. 4. Too many irrelevant features? You’re adding noise, not value. 5. Not enough diversity? Your model won’t be robust. Garbage in, garbage out. Even the most advanced model is only as good as the data it learns from. For businesses, the opportunity lies in building data pipelines tailored to their unique context — clean, representative, and enriched with meaningful features. This is how you create an AI that’s not just smart, but aligned with your business goals. The frontier isn’t just in using AI. It’s in using AI to transform your data into a moat your competitors can’t cross.

Artificial intelligence is reshaping the world. The question is not whether that transformation will happen, but who shapes it and under what conditions. The past year has made clear that the AI race ahead is not a single competition, but multiple overlapping contests unfolding at once. The United States continues to lead in frontier systems, investing heavily in models that push toward artificial general intelligence. That leadership matters. The capabilities being built today could redefine economic productivity and global power. China is pursuing a different strategy. Through its AI+ initiative, the country is embedding AI across manufacturing and key sectors with extraordinary speed. While the U.S. builds the most advanced systems, China is focused on broadly deploying AI to power its economy. Meanwhile, in 2024 the European Union adopted the first comprehensive AI law, seeking to lead through governance rather than innovation. Yet uneven enforcement and expanding exemptions risk slowing the transformation it intends to guide. Saudi Arabia and the UAE are also investing hundreds of billions of dollars in data centers to become key players in the global AI economy. This is why I’ve said the greatest risk America faces is winning the AI frontier and still losing the AI era. Leadership in this moment requires more than breakthrough models. It requires solving energy constraints, scaling infrastructure, upskilling workers, and accelerating adoption across the entire economy. Building the frontier is essential, but converting that advantage into sustained economic strength will determine who leads the era. #SchmidtSights

On August 1, 2024, the European Union's AI Act came into force, bringing in new regulations that will impact how AI technologies are developed and used within the E.U., with far-reaching implications for U.S. businesses. The AI Act represents a significant shift in how artificial intelligence is regulated within the European Union, setting standards to ensure that AI systems are ethical, transparent, and aligned with fundamental rights. This new regulatory landscape demands careful attention for U.S. companies that operate in the E.U. or work with E.U. partners. Compliance is not just about avoiding penalties; it's an opportunity to strengthen your business by building trust and demonstrating a commitment to ethical AI practices. This guide provides a detailed look at the key steps to navigate the AI Act and how your business can turn compliance into a competitive advantage. 🔍 Comprehensive AI Audit: Begin with thoroughly auditing your AI systems to identify those under the AI Act’s jurisdiction. This involves documenting how each AI application functions and its data flow and ensuring you understand the regulatory requirements that apply. 🛡️ Understanding Risk Levels: The AI Act categorizes AI systems into four risk levels: minimal, limited, high, and unacceptable. Your business needs to accurately classify each AI application to determine the necessary compliance measures, particularly those deemed high-risk, requiring more stringent controls. 📋 Implementing Robust Compliance Measures: For high-risk AI applications, detailed compliance protocols are crucial. These include regular testing for fairness and accuracy, ensuring transparency in AI-driven decisions, and providing clear information to users about how their data is used. 👥 Establishing a Dedicated Compliance Team: Create a specialized team to manage AI compliance efforts. This team should regularly review AI systems, update protocols in line with evolving regulations, and ensure that all staff are trained on the AI Act's requirements. 🌍 Leveraging Compliance as a Competitive Advantage: Compliance with the AI Act can enhance your business's reputation by building trust with customers and partners. By prioritizing transparency, security, and ethical AI practices, your company can stand out as a leader in responsible AI use, fostering stronger relationships and driving long-term success. #AI #AIACT #Compliance #EthicalAI #EURegulations #AIRegulation #TechCompliance #ArtificialIntelligence #BusinessStrategy #Innovation 

The AI landscape has rapidly evolved beyond just large language models. Today’s systems rely on a wide range of foundational model types—each designed for specific modalities, tasks, and constraints. This visual covers 12 foundational AI models and their core workflows. This is intended for engineers, researchers, and builders who want a structured view of the ecosystem. Here’s a breakdown of what’s included: → LLM (Large Language Models) – GPT, LLaMA Trained using transformer architecture to generate coherent, human-like text. The workflow involves data collection, tokenization, pattern learning, fine-tuning, and deployment. → SLM (Small Language Models) – Phi, TinyLLaMA Lightweight and efficient for on-device or low-resource environments. Focuses on model compression, compact training, and benchmarking. → VLM (Vision-Language Models) – CLIP, Flamingo Learns joint understanding between images and text. Ideal for tasks like image captioning and visual QA. → MLLM (Multimodal Large Language Models) – Gemini Designed to process and align multiple modalities such as text, image, audio, and video. → LAM (Large Action Models) – RT-2, InstructDiffusion Generates sequences of executable actions using behavioral and reinforcement learning data. → LRM (Large Reasoning Models) – DeepSeek-R1 Structured for tool use, chain-of-thought reasoning, and test-time modularity in logic-heavy tasks. → MoE (Mixture of Experts) – Mixtral Activates a subset of specialized models per input to reduce computation cost and improve performance. → SSM (State Space Models) – Mamba, RetNet Efficient at long-context sequence modeling using dynamic systems and parallelism. → RNN (Recurrent Neural Networks) – LSTM, GRU Uses hidden states to process time-dependent data, maintaining memory across input sequences. → CNN (Convolutional Neural Networks) – EfficientNet Learns spatial patterns in image data via convolution layers, pooling, and hierarchical stacking. → SAM (Segment Anything Model) – Meta Segments objects from images based on prompts (text, points, or boxes), making it useful for dynamic image understanding. → LNN (Liquid Neural Networks) – LFMs Leverages differential equations to adapt in real-time, supporting applications in time-sensitive environments. This chart is designed to help you understand not just what these models are, but how they work under the hood. If you're working in AI,  this foundational understanding is crucial for making informed architectural decisions.

For years, I thought the unfair advantage in healthtech was obvious: better tech, faster scale, more capital. But after watching dozens of companies rise and fall, I’ve realised something harsh: Those advantages disappear the moment a bigger competitor shows up. The real winners build moats so deep that competitors can’t even see the bottom. Here are the moats I’ve seen work - the ones founders rarely talk about: ▶︎ 1. Data that no one else can touch Flatiron’s AI wasn’t magical. Their oncology datasets, built over a decade of hospital partnerships, were the real moat. Roche paid $1.9B not for the algorithm, but for the data. ▶︎ 2. Turning regulation into a shield AliveCor leaned into the FDA while others avoided it. Every clearance became another wall for competitors to climb. What others saw as a roadblock, they turned into protection. ▶︎ 3. Becoming part of the bloodstream Epic isn’t loved for its UI. But ripping them out feels like open-heart surgery. By embedding deep into hospital workflows, they made themselves almost impossible to replace. ▶︎ 4. Building trust as infrastructure Patients, clinicians, payors - in healthcare, trust is the real currency. Companies that earn it early (privacy, credibility, reliability) build advantages no money can buy. ▶︎ 5. Distribution you can’t churn out of Teladoc didn’t just build telehealth tech. They locked into employer benefit plans. Once inside, churn was close to zero. That’s a distribution moat. ▶︎ 6. Compounding networks Doximity became more valuable with every doctor who joined. Today, 80% of U.S. physicians are on it. That kind of network effect compounds every single day. ▶︎ 7. Surviving regulation without bleeding out Babylon Health raised billions and collapsed in months. In this space, being capital-efficient while navigating regulation isn’t optional — it’s a moat. Notice the pattern? These moats aren’t flashy. They don’t fit neatly in a pitch deck. They’re slow. Painful. Unsexy. But they’re the only advantages that survive when Amazon, Google, or CVS enter your category. So here’s my challenge to healthtech founders: Stop obsessing about speed. Start asking: What asset will compound in value the longer I hold it? What trust can I build that others can’t buy? What constraint am I willing to suffer through that competitors will avoid? Because in healthtech, your unfair advantage often looks like your biggest constraint. What’s the moat you’re building that others can’t copy? #entrepreneurship #startup #funding

As I pause to absorb the conversations, perspectives, and energy from the recent MTN Group Leadership Gathering, I’ve found myself reflecting deeply on the evolving role of AI across our business and the 16 markets we serve. What follows are some of my personal reflections—shaped by the insights, challenges, and possibilities that surfaced when our leadership came together under one roof. 1. The real threat of the AI era is not disruption — it is delay. Every major technological shift has reshaped economic leadership. AI is doing so faster than any before it — and hesitation now carries exponential cost. 2. AI is not a layer we add — it is a capability we engineer into the enterprise. Its real power emerges when intelligence is embedded across networks, operations, customer platforms, and decision engines. This is not about isolated tools, but about creating a connected, learning digital nervous system. 3. Data is no longer exhaust — it is economic capital. With nearly 94% of the world’s data still untapped, those who activate it will build the strongest data moat of the future. 4. Competitive advantage will be defined by intelligence velocity. Organizations that learn faster consistently outcompete those that merely grow bigger. 5. Africa stands at a rare leapfrogging moment. Generative AI alone represents an estimated $100 billion annual opportunity — a chance to reset growth trajectories rather than incrementally improve them. 6. Compute sovereignty is economic sovereignty. High-performance AI data centers are the factories of the modern age. Without local compute, nations become consumers of intelligence instead of producers of it. 7. Open-source LLMs have democratized intelligence – platforms will unlock its value. Access to advanced models is no longer the barrier. The real differentiator is the ability to integrate, secure, govern, and scale them across enterprise systems through standardized architectures. 8. Impact comes from embedding AI into core operations. From network optimization and fraud prevention to customer experience and supply-chain orchestration, value is realized when AI becomes part of everyday decision flows — not when it remains confined to pilots. 9. Real value beats experimentation theater. From biometric livestock identification reducing theft by up to 90% to national digital registries creating tens of thousands of jobs, AI proves its worth when applied at scale. 10. Today’s AI decisions will shape decades of competitiveness. Our role is to architect platforms that scale intelligence responsibly, cultivate talent that can sustain innovation, and ensure technology becomes a durable source of competitive advantage for decades to come.

We used to browse the internet. Soon, it’ll browse for us. The AI browser wars are just beginning, with Chrome, Comet (Perplexity) and Atlas (OpenAI) competing for the future of work. The browser used to be a passive shell. You searched, clicked, and navigated. AI browsers act, infer, and execute. Under the hood, most of them still run on Chromium. The difference lies in memory, context, and orchestration. Arc is rebuilding the user experience: cleaner design, smart tabs, and adaptive workflows. Comet leans agentic. It reads, fills, books, and compares for you. Atlas pushes further with persistent memory and API-level autonomy, turning the web into a workspace. These browsers are trying to out-execute Google, making the web a programmable layer that agents can act on safely. This is the start of the agentic web, where AI systems transact across sites, compare, verify, and close the loop. Search collapses into action. Monetization shifts from ads to execution. The endgame is negotiation: AI will browse, transact, and orchestrate across the internet while you oversee outcomes, not clicks.

The AI landscape is undergoing a fundamental shift, and it’s not the one you think. The competitive frontier isn’t only about building the largest proprietary models. There are two other major trends emerging that haven’t had enough discussion: Open source models have made tremendous strides, especially on cost relative to performance. Compact, fit-for-purpose models can meaningfully out-perform general purpose LLMs on specific tasks and do so at dramatically lower cost. Our Chief Technology Officer and AI team share how we are using open source AI models at Pinterest to achieve similar performance at less than 10% of the cost of leading, proprietary AI models. They also share how Pinterest has built in-house, fit-for-purpose models that are able to significantly outperform leading, proprietary general purpose models. The race to build the largest, most powerful models is profound and meaningful. If you want to see a thriving ecosystem of innovation in an AI-driven world, you should also want to see a thriving open source AI community that creates democratization and transparency. It’s a good thing for us all that open source is in the race. For our part, we’ll continue to share our findings in leveraging open source AI so that more companies and builders can benefit from the democratizing effect of open source AI. https://lnkd.in/gmT6UNXs