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AI for Managers

280x Cheaper in Two Years: The AI Economy Has Flipped

9 min read

In 2023, a single query to GPT-4 cost enough that you had to count carefully. In 2025, the equivalent query became 280 times cheaper. Not 280 percent – 280 times. In two years, the cost of using AI went from a barrier to a rounding error.

Stanford AI Index – the annual report that compiles data on the AI industry from hundreds of sources – flagged this collapse in its 2025 edition. The 2026 report added context: AI investment exploded to $285.9bn, consumers are extracting $172bn of value a year, and data centres are eating electricity at the scale of New York State. The economy flipped – just not the way most people expected.

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280x Cheaper in Two Years: The AI Economy Has Flipped
4 Prompt Engineering Techniques Tested on 7 Models: Workshop Guide
30 min

4 Prompt Engineering Techniques Tested on 7 Models: Workshop Guide

“Analyze this project and give recommendations” – one prompt, seven models, and GPT-5.4 produced 2,231 words of vague advice while Claude Sonnet delivered 11 complimentary phrases like “excellent budget structure.” Rewriting the prompt using a five-element structure brought all seven models down to 346–443 words, and the praise disappeared. Token savings: 41% to 79% depending on the model.

This isn’t theory. This is data from a “Prompt Engineering in Practice” workshop I ran at the IIBA conference. One project brief, four techniques, seven models, 28 runs – and $0.054 total. Cheaper than a vending machine coffee.

99% Quality at 1.4% of the Price: What's Wrong with the AI Model Market
8 min

99% Quality at 1.4% of the Price: What's Wrong with the AI Model Market

Most managers pick an AI model the same way: grab the most expensive one available. The logic makes sense – pricier means better. That’s how enterprise software worked for the last twenty years.

The AI model market in 2026 works differently. The cost per query ranges from $0.0001 to $0.17 – three orders of magnitude. And the actual quality difference between the top ten models? 0.24 points on a five-point scale. Meanwhile, Wharton / GBK Collective reports that a third of corporate AI projects never get past the pilot stage. And Epoch AI shows that only 5.6% of users apply AI in any genuinely deep way.

Maybe the question isn’t which model is best, but whether paying a premium delivers proportionally better results for typical management tasks.

We tested it. The answer was harsher than we expected.

Local LLMs for Managers: What You Can Actually Run at Home
20 min

Local LLMs for Managers: What You Can Actually Run at Home

Anyone who has spent enough time with ChatGPT or Claude eventually asks the question: can I run something similar right on my own laptop – without a subscription, without data leaving the machine, without depending on someone else’s servers?

In 2026, the answer is yes – but the caveats matter more than the answer.

This article is for people already using cloud LLMs who want to understand what local execution actually gives you, what hardware you need, and where expectations break down. No deep technical dive, but concrete numbers.

P5.express and Agentic AI: Where It Helps, Where It Breaks Things
24 min

P5.express and Agentic AI: Where It Helps, Where It Breaks Things

In the PMI world, portfolio management is typically imagined as something monumental: steering committees, Tableau dashboards, hundreds of Jira fields, weekly status meetings with a deck of slides. P5.express offers a different approach. Three cycles, five documents, two roles. The entire system fits on a single page.

This is exactly the kind of system where agentic AI makes sense: minimalist architecture that’s easy to understand, clearly defined roles, structured data. But “makes sense” doesn’t mean “everywhere.” Some parts of P5.express stop working when automated – not because the AI is bad, but because those parts derive their value from the human process itself.

Below is a cycle-by-cycle breakdown. What’s worth delegating to an agent, what’s better left to people, and which model fits these tasks best.

The Agent Instead of Chat: Data Analysis Without Copy-Paste
11 min

The Agent Instead of Chat: Data Analysis Without Copy-Paste

You have three data files: an activation funnel, A/B test results, and support tickets. The task – figure out why onboarding is underperforming. You open ChatGPT, upload the first file, ask your question. You get an answer. You upload the second file. ChatGPT asks: “Can you remind me of the context?” You upload the third. The context of the first file has already been pushed out.

Forty minutes later you have three separate conversations, none of which answer the original question. Because the question was one, and the data was in three places.

This isn’t a ChatGPT problem. It’s a problem of approach.

Make Weak Model Great Again: Can Prompting Fix a Weak AI Model?
6 min

Make Weak Model Great Again: Can Prompting Fix a Weak AI Model?

Every team has a ‘good enough’ model. Maybe it’s the one your company approved. Maybe it’s the one that runs locally because legal won’t let data leave the building. Maybe it’s the only option your country’s regulations allow.

In Russia, this is about to become very literal. In March 2026, the Ministry of Digital Development published a draft law on “trusted AI models.” If passed – enforcement is planned from September 2027 – government agencies and critical infrastructure will be limited to models from an approved registry. ChatGPT, Claude, and Gemini, which route data through foreign servers, could be restricted. Russian-made AI assistants would be pre-installed on devices by default.

The problem is that Russian models lag behind. In our study of 54 models, GigaChat scored 3.26 out of 5. GPT-5.4 scored 4.80. That’s a 32% gap.

If GigaChat is all Russian managers have, can smart prompting pull acceptable quality out of it?