Buying AI Is Easy, Using AI Is Hard: The Cruel Truth US Tech Giants Reveal
Opinion | August 20, 2026
Author: David Tang, Managing Director, Standard Kepler
Originally published in HKET (香港經濟日報)
Executive Summary
As a member of a brokerage’s technology team, the author is asked daily: “Is our company using AI? How are we using AI?” The AI layoffs across the industry only intensify these questions — which CEO wouldn’t be tempted? But the reality is stark: buying AI is easy; using AI effectively is brutally hard. This report explains why, using Jeff Bezos’s electricity analogy and Palantir’s unique Forward Deployed Engineer (FDE) model as the blueprint for genuine AI transformation.
The Bezos Electricity Analogy
In a 2003 TED talk, Amazon founder Jeff Bezos used the development of electricity to explain the internet’s transformative potential. The analogy remains the best framework for understanding AI today:
When Did the Electrical Era Truly Begin?
Not when electricity was discovered, but when it fully replaced steam engines as the primary industrial power source. Before electricity, entire factories depended on a central steam engine connected to every machine through a complex system of pulleys. When electricity arrived, the first instinct was simply to replace the steam engine with an electric motor. Factories “upgraded” — yet efficiency barely improved, because the entire facility was still architected around steam power.
The Real Revolution
Electricity’s breakthrough was not that it was “more powerful” — it was that power transmission became virtually costless. A factory could have unlimited power sources; machine layouts were no longer constrained by pulley systems. Only then did production efficiency achieve a qualitative leap.
The parallel for AI: We are not meant to replace humans with AI. We are meant to re-architect our entire operating model around AI as a cognitive network.
The AI Implementation Gap
The “Steam Engine to Electric Motor” Trap
Most companies implementing AI today are making the same mistake as early factories: replacing humans with AI in the same positions, without rethinking the workflow.
| Approach | Example | Outcome |
|---|---|---|
| Replacement | Using AI chatbots for customer service | Customer complaints; rehiring humans |
| Augmentation | AI assists human agents with suggestions | Improved efficiency, maintained quality |
| Transformation | Re-architecting service around AI capabilities | Step-change improvement |
A recent case study: an Australian bank laid off 40+ customer service staff to implement AI chatbots. Result: customer complaints surged. The bank had to rehire human agents. Orgvue’s survey confirms this pattern: over half of companies that laid off staff for AI later regretted it.
The Palantir Model: Forward Deployed Engineers
Palantir (NASDAQ: PLTR), now a $400+ billion company with only 1,049 clients averaging ~$7 million in annual revenue each, has cracked the AI implementation code. Its secret weapon: the Forward Deployed Engineer (FDE).
What Is an FDE?
FDEs are not traditional software engineers sitting in headquarters writing code. They are embedded consultants-technologists who:
- Physically embed at client sites
- Work shoulder-to-shoulder with business teams
- Help enterprises truly deploy AI from the ground up
- Translate business problems into technical solutions
Why This Model Works
| Traditional SaaS | Palantir FDE Model |
|---|---|
| Sell standardized software | Sell customized solutions |
| Remote support | Embedded partnership |
| Short sales cycle | Long engagement |
| Low touch | High touch |
| Transactional | Relational |
Palantir’s approach acknowledges a fundamental truth: AI implementation is not a software deployment problem — it is an organizational change problem.
The Engineer Mindset
Before asking “Are we using AI?”, leaders should ask themselves:
- When did you last map your business processes end-to-end?
- When did you last sit with users and watch how they actually use your product?
If the answer is “a long time ago,” then no matter how many AI subscriptions you buy, you are just replacing a steam engine with an electric motor.
“Engineer mindset” means being willing to understand every step of the system, and accepting that “AI is not plug-and-play — it is disassemble and reconstruct.”
When you open ChatGPT, type “help me make money,” and wait for an answer, you are not using AI — you are making a wish.
The AGI Caveat
All of the above applies only until AGI (Artificial General Intelligence) arrives — defined as AI that matches or exceeds human capabilities in any cognitive domain. Post-AGI, the rules change entirely. A world where humans are no longer the most intelligent species on Earth is difficult to imagine, even if some experts believe it could arrive by 2027.
Until then: Buy AI with caution. Use AI with engineering discipline. Transform with AI only after understanding your own business first.
Standard Kepler Research | standardkepler.com