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 𝗟𝗲𝗴𝗮𝗰𝘆 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗱𝗿𝗮𝗴 𝗼𝗻 𝗔𝗜 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻𝘀:  ➜ Firms sitting on fragmented, outdated infrastructure are finding that retrofitting AI into legacy systems is often more costly than building new foundations. 6.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.

Last week, Chinese AI company DeepSeek shocked the AI industry with the release of R1, their open-sourced reasoning model. Yesterday, the stock market noticed too. To help us understand the significance of this technological and geopolitical moment, I’ve co-authored a piece in The Washington Post about DeepSeek and open-source models. DeepSeek-R1, which matches models like OpenAI’s o1 in logic tasks including math and coding, costs only 2% of what OpenAI charges to run, and was built with far fewer resources. And most importantly, it’s an open-source model, meaning that DeepSeek has published the model’s weights, allowing anyone to use them to create and train their own AI models.   Up until now, closed-source models like those coming out of American tech companies have been winning the AI race. But my co-author Dhaval Adjodah and I argue in our piece that DeepSeek-R1 should make us question our assumption that closed-source models will necessarily remain dominant. Open-source models may become a key component of the AI ecosystem, and the United States should not cede leadership in this space. As we conclude in our article: “America’s competitive edge has long relied on open science and collaboration across industry, academia and government. We should embrace the possibility that open science might once again fuel American dynamism in the age of AI.” It was a pleasure to collaborate on this article with Dhaval, whose company MakerMaker.AI is on the cutting-edge of AI technology, building AI agents that build AI agents. What do you think about the future of open vs. closed-source AI? Read the full op-ed here: https://lnkd.in/eXK5YdWk. 

Yesterday, Reuters reported that OpenAI finalized a cloud deal with Google in May. This might look like routine tech news. It is not. This is a strategic inflection point in the AI infrastructure wars. OpenAI, whose ChatGPT threatens the core of Google Search, is now paying Google billions of dollars to power its growth. This was not a partnership of choice. It was a partnership of necessity. Since ChatGPT launched in late 2022, OpenAI has struggled to meet soaring demand for computing power. Training and inference workloads have outpaced what Microsoft’s Azure alone can support. OpenAI had to expand. Google Cloud was the solution. For OpenAI, the deal reduces its dependency on Microsoft. For Google, it is a calculated win. Google Cloud generated $43 billion in revenue last year, about 12 percent of Alphabet’s total. By serving a direct competitor, Google is positioning its cloud business as a neutral, high-performance platform for AI at scale. The market responded. Alphabet shares rose 2.1 percent on the news. Microsoft fell 0.6 percent. There are only a handful of true hyperscalers in the U.S. AWS, Azure, and GCP dominate, with Oracle and IBM trailing behind. The appetite for compute is growing faster than any one company can satisfy. In this new phase of the AI era, exclusivity is a luxury no one can afford. Collaboration across competitive lines is inevitable. -s

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.

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. ⚡

Are you noticing that recruitment is taking longer these days? It’s not just the summer season slowing things down. Overwhelmed recruiters face a flood of generic, AI-generated CVs, delaying hiring and making it harder to spot real talent. So, why is AI making recruitment harder?  🔷AI-generated content in applications often lacks a personal touch, making it harder for recruiters to evaluate skills and motivation, especially when combined with mass, untailored applications in an already squeezed labour market. 🔷Without proper editing and the overuse of keywords, AI-generated CVs often come across as clunky and generic, making it a frustrating task for hiring managers to review them. 🔷Increased screening time: More applications mean longer review times, prolonging the recruitment process.   A recent study by ResumeGenius found that AI-generated CVs are a major red flag for recruiters, with 53% identifying them as the top indicator of an unsuitable candidate. What strategies are hiring managers using to cut through the noise? 1. The Big Four accountants, Deloitte, EY, PwC, and KPMG, have warned graduates against using AI in their applications. 2. The Coca-Cola Company clearly distinguishes between must-have and nice-to-have skills in its job ads, incorporating specific challenges to filter out unqualified applicants and assess genuine engagement early in the process. 3. Amazon is strategically leveraging automation through AI-powered ATS to analyze keywords and contextual relevance, ensuring that CVs are evaluated based on substance rather than being saturated with irrelevant buzzwords. 4. Most hiring managers have so much sensory/channel overload that reviewing hundreds/thousands of resumes from the “online job posting" channel gets turned off. Salesforce, Philips, Airbnb, Tesla, and others are concentrating more on headhunting practices and relaunching employee referral programs.  5. Dyson has found its way to ‘feed’ top talent into its recruitment funnel. It organizes campus tours for top engineering and business schools, putting a particular focus on students who are driven, curious, and passionate about creating something new. 6. While many companies hire externally to fill vacant, specialized roles, Infosys is looking within, helping employees grow their careers by upskilling and taking up more advanced roles within the company. 7. Slack replaced many traditional applications with a technical exercise and offered applicants the option to complete assessments on-site rather than online. 8. After Citrix Systems receives a promising application, the recruiter contacts the candidate and guides them through the whole hiring process. This 5-minute intro call can reveal far more about a candidate’s suitability than a generic application. And how does your company break through the noise, avoid the pitfalls of AI-driven hiring mistakes, and secure the best talent?

Two months ago, the consensus was that Apple had "lost" the AI race. The narrative was that their lack of a frontier model was a failure of innovation. On CNBC in November last year, I argued the opposite: Apple’s silence was not weakness. It was discipline. While competitors were locking themselves into massive capital expenditure cycles to build intelligence, Apple was waiting for the market to mature enough to buy it. As I noted in this clip: "The companies racing ahead on AI may be running faster...but Apple is the only one not running into a margin trap." Last week's news that Apple will license Gemini for ~$1B validates that strategy. They effectively swapped tens of billions in CapEx risk for a predictable, fixed-cost OpEx line item. They did not lose the race. They just refused to run a race that did not make economic sense. Tomorrow, I am publishing a full breakdown of this new dynamic, which is a concept I call "Reverse TAC"...and why the Apple-Google deal marks the end of the "Training Era" and the beginning of the "Inference Economy." Start with the clip below. The math drops tomorrow. #Apple #Google #AI #InferenceEconomics #Strategy #TechInvesting

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.

Your people strategy will fail. "If we’re investing in AI and we don’t change our workforce strategy, we’re just automating the past," a CEO, "Danny", said to me last week. Most leaders are bolting AI onto yesterday’s org chart and pitching it as transformation. It isn’t. What you should be doing. 1. Design for outcomes, not headcount. Stop asking “how many FTE do we need?” Start asking “what outcomes must we deliver, and what mix of humans + AI gets us there?” AI changes the "unit of productivity." Your org structure has to reflect that. 2. Invest in "translators," not technologists. You don’t need data scientists. You need people who can bridge the gap between business strategy and AI capabilities. Translate risk into operational controls. Explain AI decisions to boards, regulators and customers. 3. Build governance capability now. AI without workforce governance is dumb. You need to oversee AI models. This includes ethical review. Data stewardship. Cyber and privacy assurance. This isn’t compliance for compliance's sake. It’s risk containment. 4. Reskill before you recruit. There is enormous capability inside your organisation. Yet most of us overlook the obvious. Train your high performers in AI workflow orchestration. Designing prompts. Automation mapping. Data fluency. The people who understand your business best are already inside your company. They will be the fastest to adapt. 5. Reward adaptability. Make learning a performance metric. Curiosity. Cross-functional thinking. Comfort with ambiguity. If your incentive structures reward only stability and tenure, you will fail. What to avoid? 1. Don’t hire an “AI project team” and isolate them. AI capability must be embedded in functions and core processes. Finance. Customer. Operations. Risk. Otherwise, it becomes a "side quest" with no ownership or commercial weight. 2. Don’t measure productivity the "old way." If you still equate productivity with hours worked, you misunderstand what AI is doing. AI collapses task time. Your new KPIs must reflect that. 3. Don’t pretend workforce reduction is a strategy. It's not. Yes, AI may reduce roles. But if your only lens is cost out, you’ll hollow out the very capability you need to compete. 4. Don’t leave middle managers behind. Danny says, "We all know that this is where most resistance lives." Managers need support, tools, and clarity; otherwise, they become blockers. 5. Don’t separate AI from trust. Security. Governance. Ethics. If your people strategy doesn’t integrate these from day one, you’ll move fast and then spend years repairing credibility. Workforce strategy in the AI era is not about replacing humans with machines. It’s about redesigning value creation. As Danny said, the question isn’t “How many jobs will AI replace?” It’s: "What will our best people do once the repetitive work is gone?" The winners won’t be the companies with the most AI tools. They’ll be the ones who promote trust and rewire their talent mix.