Navigating AI Competition

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 ]

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-𝘀𝗽𝗲𝗰𝗶��𝗶𝗰 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.

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

After months of rumors, OpenAI finally made its play to own the browser, the most coveted chokepoint in a user’s digital life. Atlas is a web browser with a built-in ChatGPT sidebar that reads, summarizes, compares, and rewrites pages. Agents can execute multi-step tasks like travel research or shopping, moving across sites with user permission. The browser has long been the front door to the internet - and, for Google, the key to its kingdom. Chrome dominates global market share, gathering user data for ad targeting and funneling traffic into Search, Google's profit engine. Weeks ago, Google infused Gemini directly into Chrome, collapsing its assistant into the same layer. Now, OpenAI wants a slice of that gateway. It’s an all-out race for the interface of intent. The trillion dollar question is: Can OpenAI - with 800M weekly active users and unmatched cultural mindshare - convince people to switch from Chrome? Or will Chrome’s entrenched defaults keep Atlas a sideshow? 2 years ago, Google was seen as the company that wrote the transformers paper but failed to capitalize on it - the giant that blew its lead. That story’s been rewritten. Today, Google’s models are as good or better, often cheaper. Case in point: OpenAI’s Sora 2 launched to massive fanfare… a week later, Veo 3.1 quietly took the top spot. Still, narrative matters. While Google may be back on top technically, OpenAI still owns the story. Nobody markets innovation with more drama. This will be an interesting match-up. Honorable mention: Perplexity - their taste and execution are elite. They pioneered a UX with citations and follow-on questions, embedded checkout in chat, and were first to market with their AI-native browser, Comet. Their Achilles’ heel? Distribution. Every feature they ship gets copied in weeks. It’s a constant paddle-to-stay-afloat game against giants who have reach baked in. Then there’s Apple. Rumors swirl of a Safari overhaul, but if their pace with Siri is any indication, the race may be over before they enter the arena. Zoom out and this whole fight is less about browsers, more about collapse - not in the doomer sense, but in the “everything’s merging” sense. Assistants, operating systems, and browsers were once distinct. Now they’re fusing. The assistant lives in the browser. The browser behaves like an OS. The OS politely steps aside. What remains is a persistent digital self - context-rich, portable, adaptive. When Jobs unveiled the iPhone in ’07, he said: “An iPod. A phone. An Internet communicator. Are you getting it? These are not three separate devices.” 18 years later, it’s happening again. Only this time, it’s software collapsing into something new: a digital twin that travels with you across tools, devices, and contexts, orchestrating your life. The user interface dissolves. What’s left is the relationship between you and the intelligence that knows you. That’s why this is such a big deal. 

My biggest fear as an AI startup founder? Getting crushed by giants before proving our value. 6 counterintuitive strategies that helped CrewAI win against better-funded competitors: When I started CrewAI, we faced tech giants with unlimited resources and VC-backed startups with massive teams. I was just a Brazilian developer with an open-source project. Today, we power 50M+ agents monthly and partner with IBM, Cloudera, PwC, and NVIDIA. 1. Turn "small" into speed While others debated in meetings, we shipped product. Our size became our superpower - we could experiment faster than anyone else. 2. Build in public, strategically We shared every win and lesson learned. This wasn't about transparency. It was about creating a movement people wanted to join. Our community became our strongest evangelists. 3. Education drives adoption Two courses with Andrew Ng on Deeplearning.[ai] changed everything. Instead of pushing features, we taught AI agent orchestration. Our customers became champions because they truly understood the value. 4. Focus on tomorrow's problems We looked 3-5 years ahead: Companies will deploy thousands of AI agents. They'll need ways to manage this complexity. While others chase today's features, we're building the control plane for the agentic future. 5. Be a partner, not a vendor Enterprise leaders don't want another tool. They want partners who share their vision for AI transformation. This mindset attracted IBM and PwC as partners. 6. Let competition fuel growth Each new competitor made us stronger: • Their presence validated our market • Their size made us more agile • Their complexity highlighted our simplicity The key insight? Today's AI winners aren't just building tools. They're preparing for what's next. Soon, every enterprise will run hundreds of AI agents handling sales, support, content, and analytics. How will you manage them all? That's why we built CrewAI - tomorrow's AI infrastructure to help enterprises orchestrate agents, ensure compliance, and scale securely. Want to future-proof your AI strategy? DM me or follow @joaomdmoura for insights on the agentic future. ⚡

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 

Lately I've heard top AI companies offering seven-figure packages for specialized talent from San Francisco to Singapore 🤯 🇺🇲 U.S. reports confirm this: AI roles saw 10.4% wage growth in 2024 (3x national average) according to Veritone, with AI freelancers earning 21-40% more than peers per Oxford Institute research. 🌎 We validated this with Deel's global data spanning 150+ countries across full-time and contract roles. Turns out, these trends are even more pronounced globally: - Contracts with "AI" in job titles surged 585% from 2023 to 2024 - We've processed more AI-related contracts in 2025 than in all of 2023 - AI Engineers saw a 340% increase in contracts, while senior AI leadership roles tripled - The median AI salary is 120% higher than all other roles – up 6% YoY What's the impact? In the short-term: a widening pay gap between AI and traditional tech positions. But in the long-term: I see three major shifts coming: 📈 1 - Rising compensation across tech roles as market adjustments ripple out 💰 2 - Premium salaries for professionals combining AI with domain expertise (finance, healthcare, etc) 🌐 3 - Innovation hubs diversifying globally beyond traditional tech centers The AI talent wars will rewrite the global playbook for how technical talent is valued everywhere. What are you seeing? I'd love to hear from others who’ve experienced this firsthand. And we’ll unpack this global trend more deeply in our upcoming AI Jobs Report – stay tuned.

Trump wants 15% of NVIDIA's China revenue. Beijing wants zero dependence on American chips. DeepSeek now trains on Huawei hardware. Alibaba built its own AI processor. The real challenge for NVIDIA isn't Washington. It's irrelevance. The chip containment strategy isn't working. For most Chinese companies, switching from NVIDIA still means accepting worse performance. But that's changing. Once you combine software breakthroughs with local hardware, the gap shrinks fast. DeepSeek shocked everyone with R1, achieving OpenAI performance at a fraction of the cost through algorithmic innovations. Now they're moving to Huawei chips for R2, showing the hybrid approach works. The numbers tell the real story: China produces 23,695 AI papers annually vs America's 6,378. They file 35,423 AI patents vs 2,678 from US, UK, Canada, Japan, and South Korea combined. Half the world's AI researchers are in China, creating most leading open-source models. To compete, America needs to invest in fundamentals, not restrictions. Quantum computing, nuclear-powered data centers, attracting global talent. These take decades, not election cycles. DeepSeek's shift to Huawei isn't just one company's decision. It's a preview. Alibaba's new chip works with NVIDIA's CUDA platform today, but that's transitional. Cambricon's revenue hit $247 million last quarter on domestic demand alone. Their market cap exceeds $87 billion despite warnings about "irrational exuberance." When chips are "good enough" and software is clever enough, dependence becomes choice. Jensen Huang said it best: "To win the AI race, U.S. industry must earn the support of developers everywhere, including China." He estimates China's AI market at $50 billion this year, growing 50% annually. Trump wants 15% of that. Beijing wants 0% dependence. When you block the front door, innovation finds the back window. TAKEAWAY Getting to technological supremacy is the promised land for superpowers. Washington wants quick wins, usually through restrictions that backfire. China isn't trying to match NVIDIA anymore. They're changing what "good enough" means. When half the world's AI researchers decide Huawei chips running clever algorithms IS good enough, being "the best" becomes irrelevant. America knew the fundamentals playbook once. Quantum computing, nuclear-powered data centers, attracting global talent. These take decades, not election cycles. But we're debating export controls while they're shipping products. P.S. The biggest problem with export controls is their reverse network effect. The more restrictions you add, the faster alternatives develop. When "good enough" becomes the new standard, being the best becomes irrelevant. (See my first comment for why this pattern was inevitable...)

We are entering a phase where 𝘬𝘯𝘰𝘸𝘪𝘯𝘨 AI isn’t enough — 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 and 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 powerful, responsible AI systems will set you apart. To help navigate this rapidly evolving landscape, here’s a structured 𝟵-𝘀𝘁𝗮𝗴𝗲 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 to mastering Generative AI in 2025: → 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗔𝗜: Understand the real differences between AI, ML, and DL. Master the fundamentals like optimizers, activation functions, and gradient descent. → 𝗗𝗮𝘁𝗮 & 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: High-performing AI starts with high-quality data. Learn how to clean, normalize, tokenize, engineer features, and balance datasets for better model accuracy. → 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀): Go deeper than just using GPTs. Study how transformers work, what positional encoding means, and how scaling laws govern large models. → 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Learn how to design effective prompts, create structured prompt chains, manage token budgets, and optimize model outputs systematically. → 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 & 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴: Master advanced techniques like PEFT, LoRA, and RLHF to fine-tune and optimize models with minimal data and efficient resource usage. → 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 & 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝘀: Expand beyond text to images, audio, video, and cross-modal generation. Understand diffusion models, captioning, and multimodal search. → 𝗥𝗔𝗚 & 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Learn how retrieval-augmented generation (RAG) systems ground models with external knowledge. Explore vector databases like Pinecone, ChromaDB, and FAISS. → 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 & 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜: Identify biases, ensure transparency, and integrate responsible AI practices into your systems — because trust and accountability are not optional. → 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗨𝘀𝗲: Turn prototypes into production-grade systems. Focus on API serving, scaling, inference optimization, logging, and setting usage controls. Each stage is mapped with the most relevant 𝘁𝗼𝗼𝗹𝘀, 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀, and 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 to focus on. The world does not just need more AI models. It needs 𝗯𝗲𝘁𝘁𝗲𝗿, 𝘀𝗮𝗳𝗲𝗿, and 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱-𝗿𝗲𝗮𝗱𝘆 AI systems. Built by those who deeply understand the full lifecycle from idea to deployment. → 𝗦𝗮𝘃𝗲 𝘁𝗵𝗶𝘀 𝗿𝗼𝗮𝗱𝗺𝗮𝗽. → 𝗥𝗲𝗳𝗹𝗲𝗰𝘁 𝗼𝗻 𝗶𝘁. → 𝗨𝘀𝗲 𝗶𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗺𝗲𝗮𝗻𝗶𝗻𝗴𝗳𝘂𝗹 𝗶𝗻 𝟮𝟬𝟮𝟱 𝗮𝗻𝗱 𝗯𝗲𝘆𝗼𝗻𝗱.

Is Apple reshaping AI’s future? We've been waiting for the Apple shoe to drop, and here it is 🍎 Apple quietly unveiled MM1, a family of multimodal AI models that can handle both images and text. This is such a big year for multimodal AI. A few key callouts: - It has up to 30B parameters and is already competitive with Google Gemini 1.0 (and considering how new it is…) - It has in-context learning, so it can understand and respond to queries based on the conversation context without needing to be retrained for each new task - It can reason across multiple images at once (love!), drawing conclusions and generating descriptions Apple already released MLX for developers, so I wouldn’t be surprised if they put MM1 in the hands of every iOS app builder. Plus, they recently acquired DarwinAI, a startup that specializes in making AI models smaller and faster (I wonder what they might want with that tech... 😉). If we see this integrated with Siri, I could see a lot of businesses changing their strategy. Read the full paper here: https://lnkd.in/eqQU-fqE