Understanding Competition in AI Chatbots

Most AI search visibility tools are giving founders a dangerous false sense of security. I tested every major platform I could get my hands on over 6 months. ... even built my own tools when the paid ones fell short. And here's what I discovered: Most tools are solving the wrong problem. They're all asking: "Are you being mentioned?" But that's not the question that drives revenue. Think about it like this: If you're a startup founder, would you rather know that your brand was name-dropped 47 times last month, or know that when buyers ask AI assistants for recommendations in your category, your top competitor is positioned as the premium choice while you're listed as "also available"? Here's how to know if your current approach is leaving money on the table: ▪️ You track citation counts but have no idea what positioning you're getting in those citations ▪️ You celebrate being "mentioned" without knowing if you're being recommended ▪️ You measure visibility but don't know who you're being compared against ▪️ You optimize for showing up, not for being the obvious choice The frameworks most tools are built on come from old-school SEO thinking. They treat AI search like Google in 2015. But AI answers aren't search results. When ChatGPT or Claude responds to a buyer's question, they're not showing 10 blue links. They're having a conversation. They're making recommendations. They're establishing competitive context. And if you don't know what that context is, you're flying blind. After six months of testing, I found exactly one tool that actually shows the competitive landscape: Airefs. Not because it tracks more mentions, but because it shows me WHO I'm cited alongside, what tactics are working for competitors, and which partnerships could shift my positioning in AI answers. Here's what's changing now that I can finally see the full picture: ▪️ I can start optimizing more deeply for competitive advantage. ▪️ I can see which content patterns were getting competitors recommended over me. ▪️ I can identify partnership opportunities I'd been completely missing. For me, the shift isn't from invisible to visible. It is from knowing that I am "mentioned occasionally" to me knowing I am getting "positioned strategically." If you're relying on dashboards instead of what buyers actually see in AI answers, it might be time for an AI Visibility reality check. Read the full breakdown: "I Tested Every AI Search Visibility Tool. Here's The One That Actually Changed My Strategy" 👇

I just spent the last month in a head-to-head battle: ChatGPT vs. Claude. My goal was to find my primary AI for work. I ran working sessions. I wrote memos. I compared new features like 'Company Knowledge' and 'Skills'. My big conclusion? It was a giant waste of time. Trying to pick "the best" chat AI right now is a fool's errand. The target is moving too fast. Here’s my breakdown of the current AI chat race and a better strategy for choosing. 1. Feature comparison is pointless. The LLM interface is now a super-competitive layer. Gemini, Claude, and ChatGPT are in a full-blown feature war. They're even copying each other's naming conventions (e.g., Canvas, Deep Research). If a killer feature lands on one platform, just be patient. It will appear—in its own differentiated way—on the others. They are all backed by brilliant teams, and none will let the others stay in the lead for long. 2. The chat window isn't the real prize. For business leaders (especially in financial services), the true value of AI isn't the consumer chat box. These tools are the "consumer face" of LLMs, but they aren't true, autonomous agents. The real transformation will come from backend agents and no-code platforms that give business owners the power to build and deploy AI directly into workflows. That's where the exponential value will be created, not in which chat UI is slightly better this week. So, how should you choose? Stop obsessing over feature lists. Focus on these three things instead: 1. Go with what "feels" right. Seriously. The one that seems to understand your prompts and style best is the one you'll use most. Stick with it. 2. Power Users: Pick two, not three. I recommend one for personal use and one for business. This lets you test different models without the counterproductive overhead of managing three. (Trust me, three is overkill, even for me.) 3. The REAL deciding factor: CONTEXT. This is the most important point. The AI that has the most context about you will be the most useful. The switching cost is real. My new rule: Use the platform where you already work. If your team lives in Google Workspace, commit to Gemini. If you're a Microsoft 365 shop, commit to Copilot. Yes, even if you think Copilot isn't as good as ChatGPT right now. In the long run, the platform with the deepest, most useful context about your work will win. Stop chasing the 'best' model. Start building context. What's your AI strategy? Are you team-hopping or digging in? #AI #AIStrategy #LLM #ChatGPT #Claude #Gemini #DigitalTransformation #Productivity #ChangeManagement

The Era of the Chatbot is Over. The Era of the AI Partner Has Begun. If you had asked me in early 2025 who was winning the AI race, the answer was boringly obvious. It was OpenAI. But looking back at the last three months, the hierarchy has completely flipped for me. As we settle into 2026, my personal leaderboard looks different, and it tells a bigger story about where this technology is going. 1. Google (The Ecosystem Partner) Google is currently my #1. This is not just because the models got smarter but because of the Tech Stack. The value isn't in a text box anymore. It is in the seamless integration across Workspace, Android, and Vertex. NotebookLM, Gemini and Gems. Google stopped trying to build the best chatbot and started building the best infrastructure. They are partnering with my entire workflow rather than just my questions. 2. Claude (The Coworker) Anthropic is a close second, but for a different reason. With their agentic capabilities and "Computer Use," Claude doesn't feel like a search engine. It feels like a Coworker. I don't "prompt" Claude as much as I "brief" it. It sits alongside me, capable of navigating interfaces and handling complex reasoning tasks that feel genuinely collaborative. 3. OpenAI (The Utility) Make no mistake. They are still powerful. But they have slipped to third place for me because they still feel like a destination. It is a place I go to ask a question and leave. In a world of ecosystems and agents, a standalone chatbot feels increasingly like a legacy utility. The Takeaway: The "Chatbot" era was about Retrieval and getting an answer. The 2026 era is about Partnership and getting the work done. We are done looking for smarter search bars. We are looking for partners that can either integrate into our systems (Google) or sit in the seat next to us (Claude). The companies that understand AI as a partnership rather than a product are the ones winning this year. What is your stack rank?

Generalized AI chatbots like ChatGPT, Claude, and Gemini can do everything, and that might be the problem. In software, the broadest tools often become the easiest to replace. Trello looked like the future of work. It was simple, elegant, and widely adopted. But it never became the default for any specific function. Competitors took its best ideas, wove them into larger workflows, and made Trello feel like an optional tool rather than an essential one. Kanban boards became a commodity. Evernote followed a similar fate. It pioneered digital note-taking but never built a strong enough moat. Over time, Apple Notes, Google Keep, and Notion absorbed its core features and embedded them into tools people already used. Evernote faded into irrelevance. AI chatbots are at the same crossroads. Right now, they answer questions, assist with writing, and help with productivity. But those are features, not businesses. The best software products do not just provide utility. They create habits, store knowledge, and integrate so deeply into workflows that removing them is painful. Google Docs did not win because it was an online word processor. It won because it became the foundation for real-time collaboration. Slack did not just facilitate chat. It redefined how teams communicate and work together. For AI chatbots to succeed, they need to move beyond being general-purpose assistants and become indispensable. The companies that win will focus on: 1. Retaining knowledge and building memory. AI assistants need to remember conversations, understand long-term context, and act as a true system of record. 2. Automating, executing, and delivering. AI cannot stop at responding. It needs to take action, automate workflows, and complete tasks on a user’s behalf. 3. Owning a deep vertical advantage. Generic chat is easy to copy. AI that specializes in high-value industries, roles, and workflows will create lasting competitive advantages. 4. Making switching costly. The best products become so deeply embedded that removing them breaks workflows. Deep integrations, stored knowledge, and personalized automation create that stickiness. The most successful software products evolve from utilities into ecosystems. The SaaS companies that lasted such as Salesforce, Figma, and Zoom did not just offer useful features. They became the backbone of how businesses operate. Search engines offer another lesson. Early platforms forced users to browse structured directories, clicking through categories to find information. Google changed everything by introducing a search box that delivered ranked, relevant results instantly. It did not just add features. It redefined how people access knowledge. AI chatbots have the same opportunity. They can remain broad, general-purpose tools, or they can redefine how people work, learn, and automate tasks. The winners will not be the ones with the most powerful models. They will be the ones that become impossible to live without.

The recent rise of DeepSeek, a Chinese AI startup, has quickly become a disruptor in the AI space, positioning itself as a formidable competitor to OpenAI’s ChatGPT. With an estimated $6 million in training costs, DeepSeek has managed to produce AI models that rival the capabilities of ChatGPT, but at a fraction of the costs. This raises intereting questions about the future of AI development, pushing the boundaries of how we define scalable, efficient, and cost-effective solutions in a rapidly evolving tech landscape. Unlike traditional models like ChatGPT, which rely on vast computational resources, DeepSeek’s innovative approach has demonstrated that substantial success in AI development doesn’t necessarily require the deep pockets of larger organizations. This shift is reflected in market reactions, with major players such as Nvidia facing significant losses—illustrating just how much the industry is being affected by this new wave of competition. But the implications of this development are far-reaching. Not only does DeepSeek’s rapid rise signal the potential for more affordable AI models, but it also introduces concerns around data security, intellectual property, and geopolitical tensions—especially given AI industry’s reliance on cross-border cooperation. DeepSeek’s controversial use of proprietary models has triggered discussions on how AI firms are navigating issues of ethics and governance, which are critical as AI continues to reshape our global infrastructure. As we look forward, the growing competition between DeepSeek and established AI giants like ChatGPT could spur further innovation and efficiency, reshaping the AI sector in ways we’re only beginning to understand. This could be the catalyst for a more open, accessible, and diverse AI future—one where technological advancement is driven by smart, scalable solutions. What are your thoughts on the implications of this emerging rivalry? How do you think this will impact the broader tech industry and shape the future of AI? #AI #Innovation #DeepSeek #ChatGPT #ArtificialIntelligence #TechDisruption #FutureOfTech #AIcompetition #TechLeadership

Does competition drive excellence? You know it does!! So make AI compete to earn your trust! Try it! A 2023 Harvard Business School study found that firms in highly competitive markets innovate 22% faster than those in less contested spaces. So, why not apply this to AI? Could pitting Large Language Models (LLMs) against each other spark breakthroughs in creativity and performance? Imagine this: You feed an LLM a prompt, say, "Write a compelling ad for a sustainable energy startup." The first LLM delivers a solid pitch—engaging, clear, but not groundbreaking. Then, you take that output and challenge another LLM: "This is your benchmark. Can you do better?" The second LLM, aware of the competition, sharpens its tone, weaves in emotional storytelling, and adds data-driven credibility. Not satisfied, you push a third LLM: "Outdo this one." This time, it crafts a narrative so vivid—blending humor, urgency, and a call-to-action—that it feels human. Here’s a real-world example of how competition fuels AI improvement. In 2024, researchers at Massachusetts Institute of Technology ran an experiment where they tasked three LLMs with generating solutions for optimizing urban traffic flow. The first model proposed a decent algorithm for traffic light synchronization. The second, given the first’s output as a challenge, improved it by integrating real-time pedestrian data, reducing congestion by 15%. The third LLM, pushed to outshine both, incorporated predictive modeling based on historical traffic patterns, slashing delays by 27% Just like companies competing for bids—where the pressure to win drives them to refine their offerings—AI models thrive under rivalry. When LLMs “know” they’re up against each other, they’re incentivized to dig deeper, reason sharper, and create bolder. Competition doesn’t just reveal the best—it creates it. #AI #ArtificialIntelligence #Innovation #Competition #MachineLearning #TechTrends #BusinessStrategy

Ever wonder why ChatGPT gives different answers to the same question? Knowing that LLMs are generative text predictors, I could guess, but I decided to read the latest blog post from Thinking Machines to understand the real reason. I'll be honest… reading this made my head spin, so I had to get help to fully comprehend it. For those who don't know Thinking Machines, it was started by Mira Murati, who recently left OpenAI (as CTO) to start her own, more responsible AI business. This is one of their first technical publications, where they explain the real cause of AI inconsistency... Imagine asking your chatbot the same question twice and getting different answers, even when you've set it to be "deterministic" (temperature = 0, which should give the most consistent answers). For businesses relying on AI for accuracy for critical decision making, this is a serious issue. Here’s an example…. researchers tested 1K identical queries and got 80 different responses. The responses were identical for 102 tokens, then diverged… 992 said "Queens, New York" while 8 said "New York City." Seems small, right? Not when your business depends on accuracy: 🔹 Compliance & Auditing: Regulators expect consistent AI behavior 🔹 Quality Control: Hard to test systems that behave unpredictably 🔹 Customer Experience: Inconsistent responses damage trust The surprising solution? Processing power. When your AI server is busy, it processes requests in larger batches. When it's quiet, smaller batches. These different batch sizes cause the same calculation to produce slightly different results due to how floating-point math works. Think of it like this… adding numbers 1+2+3 vs (1+2)+3 can give different results in computer math due to precision limits. When batch sizes change, the order of calculations changes, creating inconsistency. Since this problem mostly exists with those firms creating LLMs, there is little control, but for those using commercial models such as OpenAI, Anthropic, Google, etc., you can work around it (for now): 1️⃣ Accept Reality -> Even "deterministic" AI gives slightly different answers, assume it will happen, don't fight it 2️⃣ Build Smart Workarounds -> For important decisions, ask the AI the same question multiple times behind the scenes, then use the most common answer for future responses. And caches accurate responses, meaning, save answers to common questions in a database, then show the exact same response every time rather than relying on an LLM. 3️⃣ Pick the Right Provider -> Ask vendors about their consistency roadmap before choosing, if they don’t have one, us another. LLMs will all be a commodity in the future. 4️⃣ Human + Hybrid -> Add human review for high-stakes decisions and combine AI with rule-based systems for final outputs. Don’t use and LLM for every decision. The bottom line? AI inconsistency is manageable with the right approach until more reliable AI becomes available. Source: in comments

The explosive rise of ChatGPT and AI chatbots has sparked a key question: Are they replacing traditional search engines? A 24-month global traffic study (Apr 2023–Mar 2025) offers some clarity. Analyzing the top 10 search engines and AI chatbots, the data reveals: ●  Chatbots grew over 80% YoY, but still lag traditional search engine traffic  ●  Google saw 26x more daily traffic than ChatGPT  ●  AI chatbots account for only 2.96% of search engine traffic Meanwhile, search engines dipped only 0.5% YoY, with Google and Bing fighting back through AI features like SGE and Overviews. This isn’t just about SEO—it’s about where customers begin their information journeys. Search remains dominant, but chatbots are rapidly reshaping how people discover, research, and make decisions.  For executives, the signal is clear: AI chatbots aren’t replacing search (yet), but they are redefining the pathways of customer engagement. How are you adjusting your organic search strategy for this shift? 

🚨 Hot take: ChatGPT is the new Excel. And every AI startup is competing with it, whether they admit it or not. Let me explain. For decades, Excel was the invisible competitor to enterprise apps. Teams built full workflows, dashboards, even approval systems without buying a single SaaS product. Excel was flexible, fast, and already in every user's muscle memory (remember the megabytes of Macros?). Now, ChatGPT is playing that exact role in the AI era. 👉 It’s the default tool for knowledge workers. 👉 It’s frictionless. 👉 It “just works.” 👉 And it’s good enough for most daily AI use cases. So here’s the reality for founders: 🤔 You're not just building against other startups. You're building against ChatGPT’s UX, speed, and flexibility. That means: 👉 Your app must feel as intuitive as a prompt. 👉 It must deliver value faster than a generic chatbot. 👉 And it must go where ChatGPT can’t into private data, real workflows, and trusted environments. 👉 The winning AI apps won’t be the smartest. They’ll be the ones that integrate, contextualize, and quietly outperform ChatGPT at the job the user actually needs to get done. If you’re building in this space, ask yourself: 😎 “Am I solving something ChatGPT can’t solve… or just trying to make it prettier?” 💪 It’s time to stop treating ChatGPT as a feature/tool. Think of it as the UX benchmark. p.s. You can replace ChatGPT with your favorite AI tool but the paradigm remains the same. #AI #Founders #ProductDesign #SaaS #ChatGPT #UXDesign #Startups #AIUX #SalesTable