Jesus Rodriguez

The best AI apps today are being built like infrastructure companies. Great breakdown from the awesome 💡 Yazan "Yaz" El-Baba on why technical depth, rapid iteration, and margin-aware architectures are becoming table stakes for AI startups. Definitely worth a read: https://lnkd.in/gMVFFXgH 

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We live in an alphabet soup of AI benchmarks - MMLU, GPQA Diamond, HLE, LiveCodeBench, SciCode, AIME 2025, IFBench, AA-LCR, Terminal-Bench Hard. Benchmarks are supposed to measure progress. Instead, they’ve become the AI equivalent of EBITDA: technically informative, practically an exercise in costume design. Two predictable failures: ▪️Saturation: Everyone hits 90% eventually. Once frontier models ace MMLU or GSM-8K, the test stops meaning anything. It’s like grading Olympic sprinters on whether they can jog a 10-minute mile. Impressive the first time, meaningless once everyone’s jogging across the finish line. The scores go up, the signal goes down. So researchers invent the next “harder” exam, and the cycle repeats. ▪️Optimization: Benchmarks get hacked. Models fine-tune for the test, not the world. You can wire a model to dominate HumanEval without making it a better programmer. You can goose GSM-8K with chain-of-thought tricks that would never help in an actual math class. In some cases (hello, Llama 4), vendors allegedly submitted “special” benchmark-only models. That’s not progress - it’s leaderboard cosplay. If you’re buying AI solutions, this is where things get tricky. On paper, everyone looks great. But none of that tells you whether Vendor X’s chatbot will resolve your customer tickets, or Vendor Y’s model will flag fraud in your transaction flow better than the rest. Benchmarks test the generic. Your business runs on the specific. Which is why you need a golden dataset: a private exam with your data, your edge cases, your definition of success. Fraud logs in finance. De-identified patient notes in healthcare. Customer chat transcripts with the slang and sarcasm only your users know. This is boring work, but it’s also the only thing that matters. Because: 1. Different vendors optimize differently. One for reasoning, one for retrieval, one for latency. All of them might be “top-ranked” broadly - but only your dataset will reveal which matches your needs. 2. The real world is messy. Benchmarks (especially static ones) sanitize the world. Your data has noise, ambiguity, system interactions, edge cases, domain jargon. A model might get 95 % on MMLU but tank on your rare edge case. 3. Contamination happens. Tests leak into training data, so models memorize. Private datasets cut through that. 4. The tails matter. In public benchmarks, everyone clusters near the top. The spread shows up in the long-tail failures. Benchmarks are like standardized tests. They’re useful for broad comparisons, but if you’re hiring, you don’t just want to know if a candidate can ace the SAT. You want to know if they can actually do the job you’re hiring for. AI is no different. So yes, clap politely when a model sets a new record. But when it comes to procurement, bring your own exam. The only benchmark that matters is the one no one else has - yours.

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ICYMI. AI is driving the cost of intelligence to zero. This innovation is going to spark rapid and massive transformations in tough industries: eg drug discovery, advanced mnfr, energy, mobility, defense, etc. https://lnkd.in/gRTinSxp

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AI is not just hype...it is a layered ecosystem. From Nvidia’s chips, to Anthropic's innovative models and Perplexity’s interface, we are witnessing a reshaping of how tech value is created and captured. I broke this down live on CNBC and followed up with a Medium post that goes deeper into the AI stack: infrastructure, models, and applications and the players leading each layer (Medium post in comments). https://lnkd.in/gaYixDjp

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Most creators blame AI for vague outputs. The truth? It’s your prompts... If you just “ask” AI to write something, you’ll get surface-level answers. That’s why so many solo creators and consultants waste hours rewriting drafts. The fix isn’t more tools. It’s more structure. 👉 Enter JSON prompting. Instead of typing a messy request, you give AI a blueprint: Task → what you want it to do Audience → who it’s for Tone → how it should sound Format → list, post, email, thread, etc. The result? Sharper, on-brand outputs the first time. In my own tests, JSON prompting cut rewrites by 60% and turned vague drafts into publish-ready posts. Here’s the bigger win: When you write prompts this way, you’re not just helping AI — you’re forcing yourself to get clear on your goals, audience, and structure before you ever hit “enter.” That’s the real leverage. So next time you’re about to prompt AI, ask: ➡️ Do I need chaos or clarity? If it’s clarity, JSON is your secret weapon. 🔎 Example ❌ Messy prompt: “Write me some LinkedIn post ideas about building an email list.” ✅ JSON prompt: { "task": "generate content ideas", "platform": "LinkedIn", "topic": "building an email list", "audience": "solo creators", "format": "list of 5 post ideas with hooks", "tone": "engaging and practical" } Which one do you think gives a sharper output? I have written a detailed guide to share how you can use JSON prompting in your creator journey. 👉 https://lnkd.in/gWpCPSJ9 🧑🏻‍💻 Follow me, M Sharyph for more tips and lessons like this. ♻️ Repost to share this post with your fellow creators.

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🚀 The AI-Native Revolution: Why Re-engineering SaaS Won't Cut It in the Enterprise 5.0 Era After decades of SaaS dominance, we're witnessing a fundamental shift that's reshaping how businesses operate and compete. This isn't just another tech upgrade—it's a paradigm change from traditional software to AI-native architectures. 💡 The Dawn of Enterprise 5.0 We're entering Enterprise 5.0, where human ingenuity combines with intelligent machines to create autonomous workflows. Unlike previous eras, employees become orchestrators of intelligent workflows rather than manual processors of information. 🏗️ Architecture is Destiny The difference between AI-native and traditional SaaS isn't in surface features—it's in architectural DNA: Traditional SaaS: Static workflows, bolt-on AI features, fragmented data AI-Native:Intelligence embedded from the ground up, continuous learning, unified data flows Adding AI features to legacy SaaS is like attaching a jet engine to a horse-drawn carriage. You might go faster, but you haven't built an airplane. ❌ Why SaaS Re-engineering Fails Three critical failure modes: 1. Fragmented Data Access - AI features only see siloed data, missing the full picture 2. Broken Learning Loops - Insights don't flow across systems, limiting compound intelligence 3. Task vs Process Automation - Bolt-on solutions automate tasks, not entire workflows 🎯 The Real Challenge Building AI-native solutions faces unique hurdles: • 85% of AI projects fail due to data quality issues • Only 12% of organizations have AI-ready data • 67% of employees show low-to-moderate AI acceptance • Requires entirely different tech stacks and organizational transformation 📈 The Market is Moving VCs have invested $2+ billion in agentic AI startups in the past two years. 25% of companies using generative AI will launch agentic AI pilots in 2025, growing to 50% by 2027. 🔮 The Path Forward Success requires: ✅ Full-stack AI rearchitecture ✅ Human-centric design that amplifies capability ✅ Different economic models (higher initial investment, lower marginal costs) ✅ Continuous learning architectures The Bottom Line:Just as budgets moved from TV to digital and software migrated to cloud, this transition will be swift and decisive. Companies retrofitting AI onto legacy architectures will be outpaced by AI-native competitors built for this new era. The future belongs to those who understand that we're not just adding intelligence to existing software—we're fundamentally reimagining how software and services work. At BGV, we're backing founders building these human-centric, AI-native solutions that combine the best of human ingenuity with intelligent machines. This is Enterprise 5.0, and architecture is destiny. #AI #Enterprise #Startups #VentureCapital #AINative #Enterprise50 #BGV #FutureOfWork --- What's your take?

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On-Prem is the New Cloud: Why Enterprises are Reversing Stance in the Age of AI Remember when cloud was the battleground for innovation, agility, and scale? Everyone was tracking the % of on-prem compute moving to the cloud and it was growing every year? Well now there’s a reversal. The reality today? On-prem is the new cloud. Here’s why. AI’s Dark Side: Legal Risk and Data Governance Headaches Enterprises are excited about AI, but also uneasy as vendors like OpenAI face intensifying legal pressure. In a high-stakes lawsuit, a judge ordered OpenAI to preserve all ChatGPT chats (even deleted ones), overriding its 30-day deletion policy. Imagine that: you delete sensitive conversations, but they’re still sucked into legal limbo. The precedent is clear: AI prompts and outputs are discoverable records, demanding integration into enterprise ESI (Electronic Stored Information) policies. For large organizations handling sensitive data, the risk is simple: you can’t afford your AI tools to turn into legal liabilities. Especially when global regulations (GDPR, etc.) and internal governance collide with unpredictably broad discovery orders. Sovereignty, Control, Trust Many enterprises still ban ChatGPT or impose strict rules on usage. Even when allowed, token limits are throttled. Enterprises are cautiously letting AI in (as they should). Enterprises need to have control and trust. SAP is leaning into this with its Sovereign Cloud On-Site, deployable on customer premises with SAP-managed infrastructure. It is built to deliver data, operational, technical, and legal sovereignty in one package. Doubt it will be the last. Scaling Pains Talking to founders, three reasons stand out why cloud isn’t scaling with AI: 1/ The rate at which AWS/GCP lets you provision compute and the compute it wants you to provision both falter as AI and AI driven developers deploy a lot more code more frequently than before. So sometimes it’s not about data governance, but actually about performance and functionality. 2/ Shadow AI is rampant—employees paste sensitive info into models despite restrictions—creating demand for better permissioning tools. 3/ Data leakage remains a live risk, like the August 2025 Grok incident where hundreds of thousands of private chats became publicly accessible and indexed. One thing is for sure - founders selling into the enterprise are scaling faster if they offer the ability to run on-prem. The Future is Hybrid Don’t get me wrong - cloud is not going anywhere. But neither is on prem. A few years agoI thought we were headed for a full cloud shift, but now I think the future is truly hybrid.

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