Kurt Beard’s Post

Just read a fascinating report on where AI startup dollars are ACTUALLY going. The conventional wisdom is getting flipped on its head: • Horizontal AI apps (60%) are winning over vertical solutions (40%) • The biggest revenue generators? Replit, OpenAI, and Anthropic • "Vibe coding" isn't just a buzzword - it's making serious money in enterprise • Creative tools that used to be for specialists only? Now everyone in the company is using them Most interesting to me: 70% of these top AI tools start with individual adoption before moving upmarket. The playbook of going from consumer → prosumer → enterprise is happening faster than ever. If you're building an AI startup, this means product-led growth is the move. Get in the hands of individual users first, then scale up. What's your take? Are you seeing these patterns in your startup's tool adoption? https://lnkd.in/e-nirMyY

Andreessen Horowitz released its AI Spending Report, analyzing transaction patterns from fintech startup Mercury’s 200,000+ customers to show which AI companies are capturing real startup dollars versus just generating traffic. 🔑 OpenAI took the top spot, with Anthropic in the second place, and Perplexity (No. 12) and Merlin AI (No. 30) rounding out the list of general assistants. 🔑 Four vibe coding platforms (Replit, Cursor, Lovable, and Emergent) appear on the list, showing the trend seeping beyond consumers into the enterprise. 🔑 Creative tools make up the biggest category with 10 featured, including Freepik (No .4), ElevenLabs (No. 5), Kling (No .15), and Canva (No .17). While ChatGPT and Claude at the top of the chart is no surprise, some of the other popular categories are — with vibe coding becoming much more than just a personal tool, and agentic AI platforms starting to proliferate beyond novelty and across actual work use cases. #ai #technology https://lnkd.in/gG58gjRa

Just spent my morning reading about the AI development landscape for 2025 and I'm seeing a MASSIVE shift happening. AI isn't just a feature anymore - it's becoming the backbone of how businesses operate. The most interesting part? It's finally becoming accessible to smaller players. What used to require enterprise-level budgets is now scalable for startups. No-code platforms and open-source models are democratizing access in ways I couldn't have imagined 5 years ago. For founders building right now: this isn't just about adding an AI feature to your product. The companies leading in this space are rethinking entire systems with intelligence at the core. If you're bootstrapping a startup and thought AI was out of reach, that barrier is crumbling. The tools are getting better and cheaper simultaneously - which rarely happens in tech. Anyone else noticing this shift? Or working on something in this space? Would love to hear about it. https://lnkd.in/eMP7BmJK

𝐅*𝐜𝐤 𝐢𝐭, 𝐡𝐞𝐫𝐞’𝐬 𝐰𝐡𝐲 𝐢𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐀𝐈 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐭𝐞𝐜𝐡 𝐭𝐞𝐚𝐦 𝐢𝐬 𝐭𝐡𝐞 𝐟𝐚𝐬𝐭𝐞𝐬𝐭 𝐰𝐚𝐲 𝐭𝐨 𝐛𝐞𝐜𝐨𝐦𝐞 𝐨𝐛𝐬𝐨𝐥𝐞𝐭𝐞. I believe timing is everything in AI deployments. I’ve seen too many startups rush, only to crash hard because they ignored fundamental readiness. This article nails the five critical factors you have to align before flipping the AI switch. It’s a sobering reminder that most AI rollouts fail — but that failure is avoidable with the right approach. Anyone considering AI should read this before pushing code to production. reyemte.ch/qby 𝑾𝒉𝒂𝒕’𝒔 𝒕𝒉𝒆 𝒃𝒊𝒈𝒈𝒆𝒔𝒕 𝒍𝒆𝒔𝒔𝒐𝒏 𝒚𝒐𝒖’𝒗𝒆 𝒍𝒆𝒂𝒓𝒏𝒆𝒅 𝒇𝒓𝒐𝒎 𝒂 𝒕𝒆𝒄𝒉 𝒓𝒐𝒍𝒍𝒐𝒖𝒕 𝒈𝒐𝒏𝒆 𝒘𝒓𝒐𝒏𝒈? #AI #TechLeadership #DigitalTransformation #AIAdoption

We’ve barely scratched the surface of what’s possible when scaling existing businesses through AI infusion. Most of the focus today is on tools, not transformation. The real challenge isn’t in learning new tools & picking up courses, but in figuring out what to build with them. AI in itself doesn't build a business. What it does is amplify a strong business idea, turning solid operations into scalable, intelligent systems, bringing in disruption in its wake. I spent some time mapping out ideas where AI is the catalyst to power up existing operations models and ways you can use it to ride the first real wave of industry transformation. https://lnkd.in/gyT2b93y #AI #BusinessStrategy #AIInnovation #AIAutomation #Entrepreneurship

Report by MiT revealed that 95% of AI pilot companies are failing which i dont think is suprising. LLM Technology hype cycle is not over yet and people started to realize that relying on what we read alone does not necessarily translatable to having a general intelligence.. Some say that this AI bubble is the reminisence of dotcom bubble but even so, dotcom still continue to evolve and so will AI. AI is here to stay while investors and startups need to figure out how to correct this market situation It will be interesting to watch how OpenAi will respond as they are earning relatively small revenue compared to staggering day to day cost of operation . If they increase their prices, startups who rely on API will suffer as they have no control over this cost and their services can be matched by any other startup at any time.. This add up to the importance of having a sovereign AI infrastructure, it emphasizes the importance of gaining control over not only the asset(infra, data, model) but the cost of providing services (ie controlling the supply chain) Companies who use AI to protect their moat or sustainable business advantage will survive. AWS, Microsoft , Google are probably not as worried as they leverage on AI to add values to their already strong product and service offering. Telcos and Other CSPs can benefit from this too, by positioning Sovereign AI based on these 2 justification 1)setting legal boundaries for the assets and 2) controlling cost of providing services. https://lnkd.in/gPu3zVss

🚨 A $4.5 billion valuation in customer service AI? Here's why this matters more than you think. Sierra just raised $175M, but this isn't just another AI funding story. Here's what caught my attention: → Co-founded by OpenAI's chairman Bret Taylor → Serving major brands like WeightWatchers and Sirius XM → Claims to solve AI hallucinations (the biggest problem in customer service AI) → $4.5B valuation after raising only $285M total But here's the real insight: While everyone's talking about ChatGPT and generative AI, the smart money is betting on specialized AI applications. Sierra isn't trying to be everything to everyone. They're laser-focused on one massive problem: customer service that actually works. The numbers don't lie: ✅ Customer service costs businesses $75B annually ✅ 67% of customers prefer self-service options ✅ AI can handle 80% of routine inquiries This funding round signals something bigger: We're moving from "AI as a novelty" to "AI as essential infrastructure." The companies winning aren't building general AI tools. They're building AI that solves specific, expensive problems. What's your take—is specialized AI the real opportunity, or are we still in the early days of general AI dominance?

“42% of enterprise AI initiatives were discontinued in 2024." Why? The models are already good. The process isn’t. Most teams add AI as a feature without changing how the work gets done. AI forces a rethink of basic ops that are usually out of scope for a software trial: - team size and structure - how departments collaborate - how documents are created, named, and stored That's why we are seeing more tech companies own the service layer. Confident they can capture more of the upside and expand the addressable market. Good article on this: https://lnkd.in/e9ZTMEV7

Why Agentic AI Matters Now - More Than Ever I've seen a lot of "next big things" come and go in tech. But here's what's different about Agentic AI... This isn't just another tech trend. The internet changed where we work. Mobile changed when we work. Agentic AI is transforming the way we work—fundamentally. And if you think you have time to "wait and see"? You don't. The next 2-3 years will separate the leaders from the laggards. The gap is already widening. Fast. Why now? The technology finally caught up. For years, we've been promised AI that could "think" and "act." But it wasn't possible. What changed in 2023-2024: - AI reasoning: Pattern matching → Multi-step reasoning - Integration: Custom for every tool → Universal APIs - Reliability: 60-70% accuracy → 90%+ with guardrails - Cost: $100+ per million tokens → $0.50-$5 (20-100x reduction) But here's the urgent part: Your competitors aren't waiting. While you're validating, they're: - Qualifying leads in minutes, not days - Shipping features in 2 weeks, not 6 - Responding to customers in 2 minutes, not 24 hours And it compounds. By Quarter 4, they're operating in a different league entirely. Here's a real example: PR reviews used to take 2-4 hours per review. Manual code review, documentation updates, tech debt checks, feedback loops—repeat until resolved. With Agentic AI: "Review pull requests, maintain documentation, and ensure tech debt is addressed" The AI handles the recursive cycle. Senior devs just review and approve. The difference? Immediate feedback. Always-current docs. Early tech debt detection. Senior devs focus on architecture, not repetitive reviews. My take: We're at an inflection point. The next 2-3 years will separate the leaders from the laggards. Not because of the technology itself... but because of what it enables. The window is narrow: - 2023: Technology became viable - 2024: Early experiments - 2025: 👉 We are here (production deployments, real ROI) - 2026: Mainstream adoption (necessity, not advantage) - 2027+: Table stakes Start now? You're an early adopter with a competitive advantage. Start in 6 months? You're playing catch-up. Start in 12 months? You're fighting for survival. That's why I founded FLYTEBIT Technologies. To help organisations navigate this shift before the window closes. --- Want to dive deeper? 📖 Read my full article: https://lnkd.in/g_KDhJm6 I break down: → The tool proliferation problem (with real PR review example) → Why the technology wasn't ready before 2023 → Why your competitors aren't waiting → The compounding effect (10% → 30% → 60% → 100%+ efficiency) → The cost of waiting 6 months What's your biggest challenge with AI adoption right now? Drop a comment. I'd love to hear what you're wrestling with. --- #AgenticAI #AITransformation #TechLeadership #FutureOfWork #DigitalTransformation #Innovation #BusinessStrategy #ArtificialIntelligence

95% of AI initiatives fail... In August MIT released their State of AI in Business report for 2025. I read it a few weeks ago while on holiday, and although not typical beach reading, it is a bit of a page-turner. About 21 pages of content, with hardly a wasted word, and some really sobering insights. The headline is that 95% of AI initiatives “fail” (deliver no measurable business value). Against $30 - $40 billion of enterprise investment, that adds up to a lot of wasted money. The report found that success or failure did not depend upon budgets or model performance, it was about adaptability, integration, and design approach. Firstly, the failing systems didn’t learn or adapt. Most GenAI systems studied don’t retain feedback, remember context, or improve over time. They never actually get better at the tasks they’re automating, and each interaction is a reset, requiring users to re-explain context and repeatedly correct errors. As one contributor to the study said: “It’s useful the first week, but then it just repeats the same mistakes. Why would I use that?” Whereas the successful implementations used adaptive systems that learned from user feedback, retained context, and evolved. These involved what are often described as agentic systems, typified by * Persistent memory * Contextual awareness * Continuous improvement loops Maybe the AI industry is getting ahead of this problem. The rise of agentic solutions is dramatic, and the number of firms focusing on that memory and contextualisation is growing, including some high-profile startups like Parag Agrawal’s Parallel Web Systems. It will be interesting to see how this failure rate changes over the next year, and if companies start to learn from these mistakes.