Hello,

This is Simon with the latest edition of The Weekly. In these updates, I share key AI related stories from this week's news, list upcoming events, and share any longer form articles posted on the website.

Headlines this week suggest it might be. The speculation is largely centred on OpenAI's release of GPT-5, which was touted as the dawn of Artificial General Intelligence (AGI). This month's release quickly proved it wasn't. This unease is also being reflected in the stock market, where shares in key AI players like Nvidia, Amazon, Microsoft, and Google have all taken a tumble.

But the real story here isn't about AGI. It’s about a more fundamental challenge: enterprise use cases for generative AI are proving fiendishly complex to implement. As I've spoken about for a while, while the conversation often focuses on personal productivity hacks, there's a quieter, more critical discussion about embedding this technology into core business operations. And that's to be expected. When you move from personal use to consumer-facing solutions, the requirements skyrocket: it has to be accurate, reliable at scale, and demonstrably safe. Right now, even the most advanced LLMs can't consistently meet that standard.

For example, consider an insurance company wanting to use AI to automatically respond to written customer claims. The system would need to review the case, look up that customer's level of cover, and then generate an email response. Every single step would have to be 100% accurate. Insurance is, quite rightly, a highly regulated industry and has strict performance levels to uphold; any mistake, such as wrongly confirming a customer is fully covered when they are not, would have dire consequences.

Until LLMs and AI agents can offer something close to 99.999% accuracy, it's going to be a brave (or foolhardy) organisation that rolls out these systems at scale for critical functions.

The current solution is to keep a human-in-the-loop, reviewing each step and checking against a governance framework. And while this might protect against error, it doesn't represent the efficiency enhancements most CEOs have been promised.

What about at your place of work? How many customer-facing processes are truly being run by AI? Do you see a genuine opportunity for it, or do the current tools still feel too unreliable?

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Google has expanded its “AI Mode in Search” to 180 new countries (still in English), integrating agentic features like restaurant bookings. Users can now ask for “a table for two at an Italian restaurant tonight,” and the AI will search platforms like OpenTable or Resy, find options, and link you directly—some users can even share ongoing conversations via links.

Why it matters: This brings intelligent, action-oriented AI into everyday life—powerful, practical, and easily relatable.

A Google Cloud survey reveals that 87% of game developers are using AI agents to cut costs and boost creativity—handling repetitive or heavy-lift tasks like processing text, code, or graphics. While most see long-term savings, many worry about IP rights and high upfront setup costs.

Why it matters: Shows agentic AI’s growing role in creative industries, and sparks discussion on ethics and business models.

Zoom has raised its fiscal‑year 2026 revenue outlook, buoyed by strong demand for its AI-powered features like Virtual Agent 2.0 and its Custom AI Companion add‑on for platforms like Google Meet. These tools automate tasks such as returns processing and appointment bookings, essentially acting as AI helpers in the background.

Why it matters: This is a real-world business success story: AI agents saving time, delighting users, and driving revenue.

Thanks for reading, and see you next Friday.

Simon,

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