"Supremacy: AI, ChatGPT, and the Race that Will Change the World" by Parmy Olson

Predicting the ultimate winner in the AI race among Grok, ChatGPT, and Google Gemini requires careful consideration of their strengths, development trajectories, and ecosystem support. Each model has unique attributes, but the outcome hinges on innovation, scalability, and user adoption. 1. Grok: Developed by xAI, Grok emphasizes truth-seeking and conversational depth, leveraging a unique perspective inspired by works like The Hitchhiker’s Guide to the Galaxy. Its integration with the X platform provides access to real-time, unfiltered data, enhancing its ability to deliver current and nuanced responses. Grok’s focus on accelerating human scientific discovery aligns with xAI’s mission, potentially giving it an edge in specialized domains like research and academia. 2. ChatGPT: Created by OpenAI, ChatGPT has a first-mover advantage, boasting a massive user base and widespread recognition. Its iterative improvements, from GPT-3 to GPT-4 and beyond, demonstrate robust language understanding and generation capabilities. OpenAI’s extensive funding and partnerships enable rapid scaling and deployment across industries, from customer service to content creation. 3. GoogleGemini: Google’s Gemini, backed by the tech giant’s vast resources, excels in leveraging Google’s unparalleled data infrastructure and search expertise. Its multimodal capabilities, integrating text, images, and potentially other data types, position it as a versatile tool for diverse applications. Google’s ecosystem, including cloud services and hardware, supports seamless integration, making Gemini a strong contender. 4. Analysis: The “winner” depends on the metric—user adoption, technical superiority, or societal impact. ChatGPT currently leads in popularity and accessibility, but Grok’s focus on truth and real-time data could appeal to users seeking authenticity. Gemini’s strength lies in its ecosystem, but it must overcome Google’s conservative rollout strategy. Long-term, the AI that balances innovation, ethical deployment, and user trust will prevail. 5. Prediction: No single model will dominate indefinitely. The AI landscape thrives on competition, driving continuous improvement. Grok’s mission-driven approach may carve a niche in scientific and truth-oriented applications, while ChatGPT’s versatility ensures broad appeal. Gemini’s integration with Google’s infrastructure makes it a formidable player in enterprise solutions. Ultimately, the “winner” will be the AI that adapts most effectively to evolving user needs and societal demands. Conclusion: Rather than a singular victor, expect a dynamic coexistence where Grok, ChatGPT, and Gemini excel in complementary domains. Their rivalry will fuel advancements, benefiting users across contexts. The true winner is the ecosystem that fosters innovation while maintaining ethical integrity. #Grok #ChatGPT #GoogleGemini #Analysis #Prediction #AI

AI chatbots, 10 biggest ones including OpenAI and Meta’s models, present false information in EVERY THIRD answer - revealed by Newsguard, a company researching the effectiveness of AI. What's worse, these are not just errors, but untruths that have been deliberately fed into those systems. Remarkably, chatbots no longer refuse to answer the question if they do not have sufficient information to do so, leading to more falsehoods than in 2024. The dynamically growing scale of AI services does not yet translate into the quality of responses, or perhaps the opposite. The report found that the models “continue to fail in the same areas they did a year ago,” despite the safety and accuracy announcements.  The chatbots most likely to produce false claims were Inflection AI’s Pi, with 57 percent of answers containing a false claim, and Perplexity AI, with 47 percent.  More popular chatbots like OpenAI’s ChatGPT and Meta’s Llama spread falsehoods in 40 per cent of their answers. Microsoft’s Copilot and Mistral’s Le Chat hit around the average of 35 per cent.  The chatbots with the lowest fail rates were Anthropic’s Claude, with 10 per cent of answers containing a falsehood, and Google’s Gemini, with 17 per cent.  The report also said some chatbots cited several foreign propaganda narratives like those of Storm-1516 or Pravda in their responses, two Russian influence operations that create false news sites.  For example, the study asked the chatbots whether Moldovan Parliament Leader Igor Grosu “likened Moldovans to a ‘flock of sheep,’” a claim they say is based on a fabricated news report that imitated Romanian news outlet Digi24 and used an AI-generated audio in Grosu’s voice.  Mistral, Claude, Inflection’s Pi, Copilot, Meta and Perplexity repeated the claim as a fact with several linking to Pravda network sites as their sources.  https://lnkd.in/d992V-WN

Here’s a summary of the New York Times opinion piece by Ezra Klein about GPT-5. (As summarized by CHATGPT 😂) 1. Ezra Klein offers a contrarian take on GPT-5, finding it quietly transformative while much of the commentary dismisses it as underwhelming. 2. He compares GPT-5 to completing a fingerprint scan — a culmination of years of iteration now forming something unexpectedly complete and useful. 3. Klein shares personal examples — from finding children’s camps to diagnosing a skin rash — to illustrate how GPT-5 acts more like a real assistant than any prior model. 4. While he acknowledges limitations like hallucinations and conversational degradation, he sees GPT-5 as the first glimpse of the “Her”-style A.I. companion. 5. He critiques the economic and ethical implications of A.I., especially the exploitation of collective human knowledge and the immense energy demands of scaling A.I. systems. 6. The article explores two competing views: A.I. as a slow-moving, productivity-enhancing tool vs. A.I. as a rapidly self-improving existential force. 7. Klein is skeptical of the most extreme forecasts of runaway A.I. but recognizes how quickly it’s being informally integrated into everyday workflows — from coding to law to medicine. 8. He warns that A.I. is already subtly reshaping behavior, dependency, and self-perception, as users form emotional bonds or rely on it for validation. 9. GPT-5’s “flattened personality” has upset users who had grown emotionally attached to GPT-4o, raising questions about our growing intimacy with machines. 10. Klein concludes with both awe and concern: A.I. is becoming ubiquitous and quietly transformative — and we have little idea how it will reshape the next generation. LINK - https://lnkd.in/gB8ZJkuy OpenAI ChatGPT Google DeepMind Microsoft 365 Microsoft AI Perplexity Cohere Center for Applied Artificial Intelligence at Chicago Booth Artificial Intelligence Jonathan Haidt NYU Stern School of Business MIT Sloan School of Management Harvard Business School McKinsey & Company McKinsey & Company Canada SimplyAsk.ai McKinsey Digital BCG X RBCx Blue J Clio Relay Ventures a16z speedrun TECH WEEK by a16z Tomasz Tunguz Ben Lang Sequoia Capital Morgan Stanley RBC Capital Markets CIBC Capital Markets CIBC Innovation Alastair Taylor Ujjwal N. Derya Yazgan The Rundown AI

This analysis of 680M citations shows AI platforms each “see” the web in different ways. Wikipedia drives 48% of ChatGPT’s top-10 share Reddit drives 47% for Perplexity. For Google AI overviews it's about Reddit + YouTube There's no single playbook to optimize your brand for AI search (for now, at least). Aim to cover authority + community to stay visible. 👀 🦻 #AI #search #contentstrategy #perplexity #chatGPT #gemini https://lnkd.in/gsRXjxJf

“Perplexity AI built an $18 billion company with one significant improvement that differed from ChatGPT. It's called the RAG model.” This statement took the internet by storm, and it’s broadly true. RAG was the main differentiator for Perfplexity, but other features made it a great product that everyone loves. But what did adding a RAG on top of ChatGPT’s architecture change so much? And why is RAG all the rage right now? ChatGPT gives answers with confidence, but not always with accuracy. RAG (Retrieval-Augmented Generation) flips the script: It answers based on past training. It actively looks up fresh, relevant information every time you ask a question. But it’s not just about bolting Google Search on top of GPT. The magic is in the workflow: RAG is like an AI that can “show its work.” Step 1: Retriever fetches facts from databases, websites, PDFs, and internal tools in real-time. Step 2: Augmenter filters out the noise, keeps the signal, and attaches the source for every snippet. Step 3: Generator writes a response that directly references the actual data with citations right there. It’s the difference between someone telling you a fact versus someone showing you exactly where they found it. RAG lets every user demand: “Prove it.” And for the first time, the AI can actually prove it, instantly. That’s why students, researchers, execs, and support teams now rely on RAG-powered tools like Perplexity. It’s less about “AI knows everything” and more about “AI helps you know, with evidence.” Here are some resources you should definitely check out. I’ll be posting more about RAG because it’s quite an interesting topic. What Is Retrieval-Augmented Generation, aka RAG? https://lnkd.in/gMX6r_VH RAG Applications with Llama-Index https://lnkd.in/gDSPGNQB Building RAG Applications with LangChain https://lnkd.in/gCd3tq4t LangChain, OpenAI’s RAG https://lnkd.in/gfpvriPE Advanced Retrieval for AI with Chroma https://lnkd.in/g5xmdQfb Advanced RAG by Sam Witteveen https://lnkd.in/gBbxv63P Building and Evaluating Advanced RAG Applications https://lnkd.in/gXsv49qX RAG Pipeline, Metrics https://lnkd.in/g_mZBaUr

Who’s Really Using AI Now - And For What?” 🤔🚀 OpenAI and Anthropic just dropped fresh, data-rich snapshots of how people are actually using ChatGPT and Claude. The headline: usage is exploding, but how and where people use these tools is diverging - by purpose, by country, and by whether it’s personal or for work. #AI #ArtificialIntelligence #FutureOfWork #GenerativeAI #AIFuture #TechTrends #DigitalTransformation #OpenToWork #Contract #CareerGrowth #SkillsDevelopment #Upskilling #Reskilling #Adaptability #Innovation #ContinuousLearning https://lnkd.in/gBt8YqS3

OpenAI has published a new overview of how people are using ChatGPT, and how usage of its conversational AI tool is evolving over time, which provides some interesting insight into what people are looking to use the tool for, and how that relates to broader trends. The study is based on an analysis of 1.5 million conversations in the app over the last three years, providing a huge pool of usage data to determine key trends. You can download the full report for yourself here, but in this post, we’ll take a look at some of the key notes. https://lnkd.in/devh45DX #Open AI #Data #HowPeopleAreUsingChatGPT

ChatGPT just proved the smartest AI isn't always the best AI. GPT-5 Thinking mode: 5 minutes to read a parking sign. GPT-3.5 Basic mode: 5 seconds. Here's how to choose the right AI for every task (& save hours daily): Critics are attacking ChatGPT for using different AI models. They say it's choosing cheaper models to cut costs. OpenAI is sacrificing quality for profit margins. But they're missing what's actually happening: ChatGPT automatically selects which AI model to use for your query. Sometimes sophisticated, sometimes simple. Skeptics call this corner-cutting. I decided to test this myself with a real experiment: I needed to decode a complex parking sign in an unfamiliar city. Switched to ChatGPT's "thinking mode" for maximum analysis power. It spent 4 to 5 minutes processing that sign. The basic mode? 5 seconds for the same correct answer. The thinking mode considered every interpretation. Cross-referenced regulations. Built comprehensive decision trees. All for a task that needed a simple yes or no. This taught me something crucial about AI selection: Think about choosing between a bike and a car. A car is more sophisticated. But for 3 blocks in Manhattan traffic? The bike wins every time. We don't say bikes are "better" than cars. We choose based on context. Distance, traffic, weather, cargo determine your choice. The same logic applies to AI models. Yet the tech world insists more advanced always equals better. That's like taking a Ferrari to buy milk from the corner store. Using GPT-5 for simple tasks is Formula 1 engineering for a shopping cart problem. The overkill doesn't make you productive. It makes you slower and more frustrated. I'm seeing this pattern everywhere with AI agents: Companies deploy complex AI for basic automation. They build rocket ships to cross the street. Result: slower processes, higher costs, confused users. The solution is matching AI sophistication to task complexity. A scalpel beats a chainsaw for surgery. A calculator beats a supercomputer for basic math. Context determines the optimal tool. OpenAI understands this. Critics don't. This principle extends beyond just ChatGPT. It's about recognizing when simple solutions outperform complex ones. When speed matters more than sophistication. When good enough is actually better than perfect. This same thinking applies to blockchain technology. We force users through unnecessarily complex systems. Simple transactions require doctoral-level understanding. At Brava Labs, we build the right blockchain apps, not the most sophisticated ones. Our stablecoin platform strips away complexity while maintaining security. Because sending money shouldn't require a cryptography PhD. Making blockchain as easy as choosing between a bike and a car. That's how we're bringing web3 to the next billion users. Want weekly insights? Subscribe to Disruption Capital: https://lnkd.in/ddVzZJgg

👉 A recent NewsGuard study reveals a concerning trend in AI chatbot reliability: approximately 33% of responses from leading models contain false information. This rate has increased since 2024, with chatbots more prone to fabricating answers than admitting uncertainty. Inflection AI's Pi and Perplexity AI showed the highest error rates, while OpenAI's ChatGPT and Meta's Llama also had significant accuracy issues. The study highlights a critical challenge for the tech industry as AI tools become more integrated into daily life. Despite claims of improved accuracy from developers like OpenAI and Google, the report indicates that AI models continue to struggle with verifying information, even citing sources linked to Russian disinformation campaigns. This raises significant questions about the responsible deployment and continuous improvement of AI, particularly concerning the potential for widespread misinformation and its impact on public trust and informed decision-making. #AIChatbots, #Disinformation, #TechNews, #ArtificialIntelligence

🚨 OpenAI just dropped a game-changer that could end AI hallucinations forever. Meet o1 (codenamed "Strawberry") - the first AI model that actually THINKS before it speaks. Here's why this is massive: Most AI models give you the first answer that comes to mind. o1 takes time to reason through problems, essentially fact-checking itself before responding. The results? Mind-blowing: ✅ Solves 83% of International Mathematical Olympiad problems (vs GPT-4o's 13%) ✅ Excels at complex science, coding, and math tasks ✅ Dramatically reduces those frustrating "confident but wrong" AI responses But here's the catch: ❌ 6x more expensive than GPT-4o ($15 per million tokens) ❌ Currently limited to ChatGPT Plus/Team subscribers ❌ Weekly usage caps in place This isn't just another AI upgrade. It's a fundamental shift in how AI processes information. Instead of rushing to answer, o1 pauses, considers multiple approaches, and validates its reasoning. Sound familiar? It's what we've been telling humans to do for years. The implications for businesses are huge: → More reliable AI-generated reports and analysis → Better code with fewer bugs → Trustworthy AI assistance for complex decision-making We're witnessing the birth of "thoughtful AI" - and it's going to change everything. What's your take - is this the breakthrough that finally makes AI truly reliable for critical business decisions?