Dwayne Gefferie

ChatGPT became a store. Claude got smarter. 21 AI shifts that changed business last week: 1. Big Software ↳ OpenAI turns ChatGPT into an online store with instant checkouts as U.S. users can now buy Etsy products directly in chat using Stripe’s Instant Checkout, marking the start of conversational commerce. ↳ OpenAI announces Sora 2 and the Sora app (now in Top 3 on the App store) delivering hyper-realistic video generation and a TikTok-style feed where users can insert themselves into AI-created scenes. ↳ Anthropic releases Claude 4.5 Sonnet with a 30-hour attention span that tops coding benchmarks and ships alongside the new Claude Agent SDK. ↳ Claude also launched a Slack MCP, so teams can summarize chats, draft replies, and analyze documents directly in their workspace. ↳ Meta will use AI chat data to target ads across Facebook and Instagram from December 16, boosting ad precision but raising new privacy questions. ↳ Apple gives developers free on-device AI access through its 3 billion parameter model, enabling smart features that run privately with zero API cost. ↳ Microsoft adds Agent Mode to Office allowing Excel, Word, and PowerPoint to handle data analysis, drafting, and presentation design through chat. ↳ Google makes search visual as AI Mode lets users find and shop using photos or natural-language prompts, transforming product discovery. ↳ Perplexity launched a Search API and and made its Comet browser free. ↳ Cloudflare launched VibeSDK allowing anyone to deploy their own natural-language coding sandbox. 2. Hardware and Infrastructure ↳ Meta buys chip startup Rivos Inc. and signs a $14.2B deal with CoreWeave to secure Nvidia GB300 systems through 2031. ↳ Microsoft invests $33B in compute capacity partnering with neocloud providers to meet surging AI demand. ↳ Amazon unveils next-gen Echo devices with Alexa Plus and custom AZ chips running Vega OS for faster, smarter voice automation. ↳ Google launches new Nest cameras and a $99 AI speaker with 2K HDR and Gemini-powered alerts for homes and offices. ↳ DoorDash unveils “Dot,” a 30-pound delivery robot that cuts missing-item errors by 30 percent and begins pilot testing in Phoenix. 3. Policy and Safety ↳ CNN uncovers North Korean AI job scams exposing fake developers using AI deepfakes to secure U.S. remote jobs and funnel millions back to the regime. ↳ California passes the first U.S. AI safety law requiring large developers to disclose AI risks and report incidents to regulators. 4. Startups and Emerging Players ↳ Thinking Machines, founded by ex-OpenAI CTO Mira Murati, launches a fine-tuning API for LLMs. ↳ Z.ai releases GLM 4.6 with stronger reasoning and coding abilities joining the new wave of open-source models challenging the giants. ↳ Mercor introduces the APEX benchmark showing that AI handles routine work well but still struggles where nuanced human judgment matters. Follow Gaurav (Rav) Mendiratta for weekly AI updates that help business owners grow faster.

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𝗢𝗽𝗲𝗻𝗔𝗜’s Product Leader just revealed the playbook for 𝗔𝗜 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 and why it decides who wins and who fails 🤯 Chegg lost 90 percent of its value because it waited too long. Jasper raised at a $1.2B valuation but collapsed under costs and commoditization. Duolingo lost trust and users by forcing AI in without strategy. AI does not forgive bad decisions. Features get copied in weeks. Costs spiral faster than revenue. The market punishes hesitation brutally. That is why Miqdad Jaffer, 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗟𝗲𝗮𝗱 at 𝗢𝗽𝗲𝗻𝗔𝗜, created a step by step guide to AI product strategy https://lnkd.in/dn2SCSUb Inside you will find: ▪️ The brutal economics of inference costs and why SaaS style pricing breaks in AI ▪️ The only three moats that matter in AI which are data, distribution, and trust ▪️ The 4D Framework that stress tests your direction, differentiation, design, and deployment ▪️ Pricing and positioning models that protect you from death by API wrapper Most AI startups fail not because of technology, but because of their strategy. 🎁I am sharing the full guide and frameworks here: https://lnkd.in/dn2SCSUb

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The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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AI is getting smarter. But most teams are still missing the point. When I was at ByteDance, we built internal AI tools to move faster: Auto-generate copy. Draft strategy. Predict winning creatives. It worked.  +70% NPS $6.4M saved in operational costs. But here’s one thing. You can build the best AI tools, and still miss what people actually need. Because AI can map behavior, but it can’t feel hesitation. It can scan data, but it doesn’t know why someone smiled at an ad.  It can write a love letter but it has never had its heart broken. So we had to shift focus. Not just for speed, but to sense. Because productivity doesn’t always mean progress. Features don’t always equal understanding. And outputs don’t equal insight. Even now at Atria, we use AI to help brands create faster than ever. But the judgement call? The taste? The “this feels right”? That still needs a human. And follow me Ray Jang for daily posts as I scale Atria, an AI platform for marketers to create killer ads with ease.

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I’ve been testing every workflow I can find. And this is the playbook that changed how I ship products: 1. Start with AI for velocity In 15 minutes, I can draft 5 landing pages or campaign frameworks. It’s the fastest way to brainstorm at scale. Other folks move fast, you need to move faster! 2. The stall point AI outputs aren’t production-ready. No integrations. No polish. No durability. This is where most teams lose momentum. 3. The unlock: hiring Fiverr experts asap I dropped my AI blueprints into Fiverr. Found devs + designers in under 48 hours. They shipped a fully functional build FAST (Hire here + get 10% with my code LINDSAY10!!! https://lnkd.in/gugD3Sdb) Allowing me to GTM, get in front of customers, scale further. So yes, but all means dive in with every AI tool out there. But don’t get stuck prototyping, prompting, vibing…. …and actual launch, partnering with leading pros. If you’ve been drowning in AI ideas but struggling to ship, steal this workflow. Fastest way I’ve found to get from raw concept → real product. #FiverrPartner

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💸 Everyone’s talking about outcome-based pricing in AI software right now. It sounds great on paper: instead of charging per seat or per token, you only get paid when you deliver results. It’s clean. It aligns incentives. It solves a lot of problems with SaaS bloat and unpredictable AI usage bills. ⚠️ But that simplicity hides a deeper complexity: In a sense, usage-based pricing is already a form of outcome-based pricing; just one where the model decides what the outcome is. You pay per token, per function, per interaction. However the "outcome" is inferred, often implicitly. Meanwhile, human-defined outcomes, like "issue resolved" or "churn prevented", are explicit, interpretable, and contract-worthy. The trouble comes when those two diverge. If the model believes it completed the task, but the human disagrees? Or if a user drops off mid-flow and the vendor counts it as a resolution? That dissonance creates billing disputes, mistrust, and risk. In the latest edition of Tokenomics I am trying to dive deeper into that dissonance. To help think through that, here’s a simple 2x2 framework I’ve been using: What are you charging for? 🛠️ Work Completed – Task finished, regardless of impact 📈 Upside Captured – Business result delivered Who’s your buyer? 💼 Cost Center – Efficiency-driven, budget-sensitive 📊 Revenue Driver – Growth-oriented, outcome-focused Putting it together you get four pricing archetypes 🔹 Ops Backbone (Cost Center × Work Completed) → Think: support automation, per-resolution → ✅ Clear ROI, but limited alignment 🔹 Skeptic’s Gambit (Cost Center × Upside Captured) → Think: fraud prevention, chargeback recovery → 🧾 High bar for proof, slow cycles 🔹 Volume Multiplier (Revenue Driver × Work Completed) → Think: meetings booked, emails sent → 📬 Easy to scale, hard to tie to success 🔹 Skin-in-the-Game (Revenue Driver × Upside Captured) → Think: upsell revenue, new pipeline closed → 🧠 Deepest alignment, toughest attributionThe Outcome Pricing Matrix Outcome based pricing isn't just about measuring success, but also about agreeing on who defines it and when. Most AI startups today are landing in hybrid pricing: platform fee + usage. But if you’re serious about moving toward outcomes, you need a clear POV on where you live in this matrix, and what failure modes you’re willing to accept.

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