OpenAI's o1: A game-changing AI model that thinks before it speaks

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

“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

🚀 Chat GPT-5 is here, and it's a business game-changer! Just weeks after launch, GPT-5 is rolling out to OpenAI's Free, Plus, Pro and Team users and the results are already impressive. API usage has surged since launch, with the model now processing more than twice as much coding and agent-building work and reasoning use cases jumping more than 8X. But here's what really matters for your business: GPT-5 isn't just faster—it's smarter. ✅ 75% fewer hallucinations = More reliable outputs for critical decisions ✅ PhD-level reasoning = Complex problem-solving that actually makes sense ✅ 1M+ token context = Handles your longest documents without losing the thread ✅ Autonomous agent capabilities = Give it a goal, watch it execute multi-step plans Our thoughts: We're not dealing with a "fancy chatbot" anymore. This is enterprise-grade intelligence that can transform how you work. Quick wins to try this week: • Automate your customer support FAQ responses • Create a company knowledge base that actually answers complex questions • Let it qualify sales leads while you focus on closing deals • Generate marketing content that doesn't sound robotic The businesses winning with AI aren't the ones with the biggest budgets—they're the ones moving fastest to integrate these capabilities. Ready to see what GPT-5 can do for your business and explore other AI Tools? 👉 Drop us a line: support@thisainow.com 🌐 Learn more: www.thisainow.com Source: https://lnkd.in/gK7sx9M3 - CNBC, August 2025 #GPT5 #OpenAI #ChatGPT #ArtificialIntelligence #AI #BusinessAutomation #AITools #TechTrends #MachineLearning #DigitalTransformation #AIStrategy #Innovation #Productivity #CustomerSupport #MarketingAI #SalesAI #EnterpriseAI #AIAdoption #FutureOfWork #ThisAINow

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

Since the release of ChatGPT (enabled by GPT 3.5) in 2022, the default narrative has been one of AI takeoff and divergence. The imagined scenario is one where a leading AI lab develops a recursively self-improving model. In doing so, there is an intelligence explosion. That leading lab (and the nation behind it) then uses that model to diverge from the rest of the market to become strictly dominant across domains. Whether or not you believe (and believe others believe) we are headed towards a divergent or convergent future of model performance, is core to any forward looking assessments of technology or (geo)politics. Thus what we found most significant about the release of GPT-5 is that, despite its impressive performance, it gave some of the best evidence for a convergent future of AI (based on current model architectures). Historically, when OpenAI releases a new frontier model, they have dominated across benchmarks. In a convergent scenario, instead of seeing a widening gap between the top frontier model and the rest, we would expect to see frequent swaps at the top of benchmarking leaderboards. Frontier performance clusters as improvements become more and more incremental. With GPT-5, OpenAI did not dominate. Whether or not you adopt the belief that foundation model performance is converging, at the very least you should re-weight your expectations. We have. Read the full essay for more on ... → The reliability problem with agentic AI → Why we believe in is selling services, not tokens → How this should update our views on energy, jobs, and chip policy https://lnkd.in/epnEspbq

The launch of OpenAI's ChatGPT sparked huge buzz around #AI stocks. While you can't invest in ChatGPT directly, here are 12 companies giving investors exposure to AI #chatbot technology. 🤖📈 #AI #ChatGPT #ArtificialIntelligenceInvesting https://lnkd.in/g-8AcQ2Z

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

Chatbots are now smart enough to answer most users' questions, which is misleading people to believe AI is stagnating. If you look at the population, a small fraction (14% in the US) has a master's degree, professional degree, or doctorate. AI chatbot models have largely saturated benchmarks that would be relevant to the 86% of the population with a bachelor's degree or less, at least in terms of their ability to answer questions. This explains a lot of how AI has been developing recently. For example, it helps explain why with GPT-5 OpenAI focused a lot on efficiency, routing the majority of requests to faster and cheaper models that don't need deeper intelligence, while simultaneously beefing up GPT-5's coding capabilities, and its maximum intelligence level in the exclusive GPT-5 Pro model. It also explains why we're now seeing focus beyond just answering questions, to things like improved agentic abilities and longer horizon tasks, job-specific training via reinforcement learning, and maximizing scientific and technological understanding and innovation. For most chatbot uses on a day-to-day basis, the majority of people simply won't notice much difference now between answers from ChatGPT, Gemini, Grok, or Claude. They've all saturated relevant benchmarks. Those people will, however, care about things like speed, cost, accuracy, memory, personality, integration with apps, and ability to do things on their behalf. And a handful of users as well as businesses WILL need models that are maximally intelligent and capable in specific domains, like software engineering, medicine, and scientific research. So I think what we'll see is a bifurcation. On the one hand, you'll see labs focus on maximizing intelligence per dollar and second to address the majority of queries as fast and cheaply as possible without sacrificing quality. On the other, you'll see them stop at nothing to push the frontier for the minority of high-value tasks for which people are willing to pay and wait.

New reports from OpenAI and Anthropic give a clearer view of how people are using ChatGPT, Claude and similar AI models—and it turns out use is dominated by everyday tasks over hardcore professional work. Most users engage AI for personal-oriented stuff: looking up facts, asking for advice, writing or editing text, organizing daily plans, translations, etc. Work-related uses do exist—such as drafting documents, supporting coding or research—but those are a smaller slice. The data also shows a trend toward augmentation (AI helping humans) over automation (AI doing everything on its own), with many preferring tools that enhance their productivity over ones that replace major parts of their workflow.