🫧AI Bubble or Human Nerves?
Read Time: 4 mins.
Welcome back! I really appreciate everyone who voted on the new format. Most of you preferred two shorter issues each week, so that’s the plan going forward 🥳.
Tuesdays will bring the usual segments, Thursdays will focus on Declassified conversations with a quick mid-week AI news update.
Also, the This Is Declassified podcast is coming soon for anyone who prefers to listen on the go. I’ll keep you posted.
Let’s dive in.
On deck:
📡NVIDIA vs The AI Bubble
🔦Tool Spotlight: Lovable
💡Pre-Mortem Analysis: What can go wrong?
🥊Face-Off: Choose the best response
🍵AI News Quick Hits
The 📡Signal Behind the 🔊Buzz
Demystifying trending AI stories.
Spotlight🔦 by Toolfetch
Smart Tools, Clear Choices. Powered by Toolfetch.ai (coming soon)
This week’s spotlight is on Lovable.dev, an AI tool that can build real, functional software from your text descriptions.
Lovable is one of the platforms at the forefront of the “vibe coding” movement, allowing anyone to build web apps without writing code. It automatically sets up both the interface users see and the database that stores their information.
Unlike standard website builders, Lovable writes actual software code that you can export and own.
For decision-makers, this minimizes risk. You get the speed of AI automation, and you are not locked into their platform forever because Lovable syncs with GitHub (the industry standard for storing code).
That means a human developer can easily take over if your app grows.
Takeaway: Many reviews and Reddit posts call Lovable.dev “magical” for getting an app online in hours. Users particularly like the visual editing and its ability to manage complex features like user logins.
The downsides include burned credits when the AI gets stuck in error loops, and limits (credits, scale, reliability, UX) when building production-grade systems.
Overall sentiment is cautiously positive. It is a great tool for getting most of the work done; complex logic may still require a human expert.
Concept Corner💡: The Pre-Mortem Analysis
Quick, practical insights for boosting your efficiency and productivity with AI.
What it is: A pre-mortem analysis asks “what will go wrong?” before you even start a project, instead of asking “what went wrong?” after things go haywire.
It is a strategic exercise where you assume your project has already failed woefully in the future, then work backward to determine the specific causes, allowing you to fix the holes in your plan before they ever have a chance to sink the ship.
Real-world example: Imagine you are finally launching that side business you have been planning for months and daydreaming about potential revenue for longer.
One year later, the business is dead. You realize the cause wasn’t the service or product, but burnout because you didn’t automate your invoicing and it became a monster.
That’s just one example, it could be any part of the project that seems “harmless” during the pre-launch euphoria.
On our own, it can be hard to imagine how things could fail because we’re biased toward optimism, but AI is the perfect impartial cynic.
You can feed your strategy into a language model like Gemini, ChatGPT, Claude, etc., and ask it to ruthlessly simulate failure scenarios, spotting the operational blind spots that your excitement is ignoring so you can build safeguards today.
Try this prompt:
I am planning to [insert project/goal, e.g., launch a freelance design portfolio]. Assume it’s one year from now and this project has failed completely. Based on common pitfalls in this industry and my current plan, provide a narrative explaining exactly why it failed. Then, list 3 specific preventative measures I must take right now to prevent this outcome. Here are the details of my plan: [paste plan details]
🥊Face-Off🥊
AI models vie for supremacy. In the words of Bruce Buffer, “IIIIIIIT’S TIME!!”
Prompt: Create a quatrain about Thanksgiving, with rhyming scheme ABAB.
Quick Hits🍵
✍🏾 The US President’s new “Genesis Mission” order turns DOE labs and supercomputers into a national AI platform to accelerate scientific breakthroughs.
👑 Anthropic launched Claude Opus 4.5, claiming the coding crown with a new “effort” parameter that lets users trade speed for deeper reasoning intensity.
💪🏾 Google’s new Gemini 3 now powers the Gemini app and enterprise tools, bundled with Nano Banana Pro image generation and experimental Gemini Agent automation.
🧑🏻🏫 OpenAI introduced ChatGPT for Teachers, a free GPT-5.1 workspace for verified U.S. K-12 educators through June 2027 with FERPA-minded controls and “Study Mode” helpers.
🤖 Google debuted Antigravity, an “agent-first” IDE that allows developers to command swarms of AI bots to plan, code, and debug software autonomously.
💎 OpenAI CEO Sam Altman and designer Jony Ive confirmed their stealth hardware device is prototyped, “jaw-droppingly good,” and targeting a release within two years.
🧊 Meta upgraded its vision stack with SAM 3D, enabling the “Segment Anything Model” to reconstruct 3D objects and scenes from flat 2D video in real-time.
🎯 OpenAI launched “shopping research” mode plus a Target app that builds carts and checks out inside ChatGPT.
☁️ Mistral AI partnered with SAP to deploy a “sovereign AI” cloud stack, specifically targeting German and European government agencies wary of U.S. data transfer.
🐝 DeepMind applied AlphaFold to the apiary, identifying genetic markers in honeybees to help breed colonies resistant to disease and decline.
⚖️ EU Regulators unveiled a “Digital Package” that streamlines data laws, delays key AI Act duties and widens GDPR exceptions for AI training, alarming privacy groups.
🚀 Deep-learning “godfather” Yann LeCun is leaving Meta to launch an Advanced Machine Intelligence startup focused on world-model-style reasoning, with Meta staying on as a partner.
🏢 Google DeepMind announced a new Singapore lab to grow Asia-Pacific AI talent and research, framing it as a safety-minded hub for climate, health and other high-impact work.
🛡️ Microsoft and GitHub launched an AI security tool for DevSecOps that goes beyond detection to automatically remediate vulnerabilities in the pipeline.
What do you think about this week’s newsletter? Hit reply and let me know.
See you on Thursday!