Apple’s AI Doubts, ChatGPT’s Rabbit Holes, and the Diplomacy Deathmatch

Hi Innovators!

Today, we’re cracking open a fresh batch of AI tea, served Apple-style, spiced with hallucinations, and chased with rabbit-hole wanderings and geopolitical games. From academic shade to existential design flaws, we're pulling no punches.

Let’s dig in.

10x Your Outbound With Our AI BDR

Scaling fast but need more support? Our AI BDR Ava enables you to grow your team without increasing headcount.

Ava operates within the Artisan platform, which consolidates every tool you need for outbound:

• 300M+ High-Quality B2B Prospects, including E-Commerce and Local Business Leads

• Automated Lead Enrichment With 10+ Data Sources

• Full Email Deliverability Management

• Multi-Channel Outreach Across Email & LinkedIn

• Human-Level Personalization

Book a demo and supercharge your sales team.

Apple’s AI Research: Burn Book or Big Warning?

In a deliciously timed paper, Apple’s AI researchers just dropped a truth bomb about their rivals’ large language models: they’re “easily quitters.” According to Apple, popular LLMs (cough ChatGPT cough) struggle to stay coherent when prompted to reason through multiple steps. They hallucinate. They give up. They spiral.

Sound familiar?

Meanwhile, Axios is digging into the same issue, pointing out that these so-called hallucinations aren’t random glitches, they’re the inevitable result of how these models are built. LLMs aren’t logic engines. They’re pattern predictors. That means asking them to “reason” is like asking a fortune cookie to explain philosophy: fun, but fundamentally broken.

Why enterprises should care:

• If you’ve got complex workflows that require step-by-step reasoning, don’t bet the farm on current LLMs.

• Apple’s critique is a signal that reliability and reasoning will define the next AI arms race.

• We may be nearing the limit of “bigger is better.” Now it’s about “smarter is safer.”

What to Watch: ChatGPT’s Rabbit Holes, Explained

Ever asked ChatGPT one simple question and ended up four scrolls deep into a dissertation on Venetian trade routes, a Wikipedia knockoff, and an unsolicited poem?

Apparently there’s a scientific reason behind ChatGPT’s tendency to go full Alice in Wonderland on your queries. It all comes down to the training data: the model tries to maximize “usefulness,” but it interprets that as more words = better answer. That means you get a deluge of semi-relevant but tangential info. Hello, rabbit hole.

Enterprise takeaway:

• Be strategic in prompt design: brevity is your friend.

• Use guardrails, especially in customer-facing apps. No one wants a 5-paragraph essay when they asked for store hours.

In the News

1. Britain vs. Getty: AI Copyright Smackdown
The UK is proposing new AI copyright rules, and Getty is not amused. The stock image giant says the proposed law gives AI firms a free pass to scrape its content.
Get the report
→ Read more

2. Meta Goes Bigger, Again
Meta just pumped billions more into scaling its AI infrastructure. Zuckerberg’s betting on model-size supremacy—again—despite increasing skepticism about scalability vs. usability.
→Full story

3. Who Wins at AI Diplomacy?
Anthropic, OpenAI, Meta, and Gemini just went head-to-head on a new Diplomacy benchmark. Spoiler: Gemini didn’t dominate.
Read more
→Guess who

TL;DR:

• Apple says other LLMs "collapse and quit" at reasoning.

• Hallucinations aren’t bugs, they’re the blueprint.

• ChatGPT’s long-winded answers are the design, not a glitch.

• UK copyright laws are ruffling Getty’s feathers.

• Meta doubles down on “big AI.”

• Diplomacy benchmark test throws curveballs at top LLMs.

That’s all for today, friends. Whether you’re scaling models, watching for hallucinations, or just trying to keep your AI from turning into a verbose philosopher, remember: smart beats shiny every time.

Stay sharp,

Cat Valverde
Founder, Enterprise AI Solutions
Navigating Tomorrow's Tech Landscape Together