AI Inference Costs Will Become More Predictable, as Did Cloud Computing "As a Service" Costs

Though it is rational to note that AI inference costs are somewhat unpredictable at the moment, that also was true of cloud computing in general. But as the technology matures, it is likely--probably inevitable--that AI inference “as a service” will develop methods of providing customers more cost predictability. 

 

Cloud computing, after all, also featured unpredictable, often expensive usage-based pricing, that made customer budgeting difficult. 

But the market responded, creating pricing models (reserved instances, committed use discounts, and hybrid approaches) that provided cost predictability.

AI inference is likely following a similar trajectory. Token-based pricing means the cost per inference can fluctuate based on model complexity, input length, and provider capacity.

But providers already are experimenting with different approaches beyond pure pay-per-token models: subscription tiers, reserved capacity options or volume discounts that provide more predictable monthly costs. 

Enterprise contracts increasingly include committed usage terms that offer better rate predictability as well. 

And  competition also will drive providers to offer more customer-friendly pricing structures. AWS, Google Cloud, and Azure all evolved toward more predictable pricing options as the market matured and customers demanded better cost management tools.

At the same time,  models, hardware and inference acceleration will naturally drive down costs. So will analytics. 

Some of us cannot see the cost unpredictability being a long-term issue for AI inference.

Does AI Use Really Reduce "Critical Thinking?"

Lots of people, and some studies, might suggest frequent use of artificial intelligence reduces the ability to think critically, generally thought to include skills such as:

• Analysis: Breaking down information into its components, identifying underlying structures, and understanding relationships among ideas.

• Interpretation: Understanding and explaining the meaning of information, data, or events.

• Evaluation: Assessing the credibility, strength, and relevance of arguments, evidence, and sources.

• Inference: Drawing conclusions from available information and recognizing possible implications or consequences.

• Explanation: Clearly and coherently expressing one’s reasoning and justifying conclusions.

• Self-regulation: Monitoring and correcting one’s own thinking processes and biases.

• Open-mindedness: Considering alternative viewpoints and being willing to revise one’s own beliefs in light of new evidence.

• Problem-solving: Identifying problems, generating solutions, and implementing effective strategies.

• Creativity: Making connections between seemingly unrelated ideas and generating innovative solutions.

• Communication: Effectively articulating ideas, listening to others, and engaging in constructive dialogue.

Be honest: how many people do you actually know these days who actually are good at critical thinking? 

Maybe studies of AI chatbot use on “critical thinking” only measure pre-existing propensities. If you have ever taught college students, in the days before AI existed, you might already believe that critical thinking skills are unevenly distributed, and it has nothing to do with AI. 

You might also tend to believe that graduate school often sharpens such skills far more than did your undergraduate education. If so, then more education might enhance critical thinking skills to a significant extent. 

The rejoinder might be that all of us might have experienced some lack of proficiency in one or more of the bullet points noted above, ranging from ability to evaluate sources to remaining open minded. 

That noted, at least some studies suggest a possible link between use of AI chatbots and a decline of “critical thinking.” 

Factor	Negative Impact on Critical Thinking	Neutral or Positive Impact on Critical Thinking
Frequent, passive use	Yes 1,2,3	—
Thoughtful, interactive use	—	Yes 5
Younger age	Yes 1,2	—
Higher education	—	Yes 1, 2
Low motivation	Yes 4, 5	—

When Does AI Not Add Much Value for Consumer Hardware?

As useful as artificial intelligence is for software, that might not mean it is equally compelling for many types of consumer hardware. 

I always see the display of the Ray-Ban smart glasses when at my optometrist’s office, but that does raise issues: where’s the value? How much added value is there, compared to the extra cost? And for some of us, there are other tradeoffs.

How do smart glasses integrate with one’s prescription lens requirements? How about frame selection? 

One issue with embedding AI into consumer devices is the willingness to pay. Most consumers are careful about paying too much of a premium for such features. The other issue is simply value: it remains unclear whether the features are important enough, on a regular basis, to make the investment worthwhile. 

And then there are the other physical impediments. Until recently, I wore prescription corrective lenses, so it has not been clear whether I’d have the choice of frames I’d prefer, or how much additional cost the corrective lenses feature would add to the smart glasses. 

Use Case	Description
Real-Time Translation	Live translation of speech/text, projected in view
AI Voice Assistant	Hands-free digital assistant for tasks and queries
AR Overlays	Navigation, notifications, and contextual info in the user’s field of view
Object Recognition	Identifies and describes objects, scenes, or people
Accessibility	Features for visually/hearing-impaired users (captions, scene description, hearing aids)
Hands-Free Communication	Dictation, messaging, calls, content capture without hands
Health & Wellness Monitoring	Eye tracking, fatigue detection, and health feedback
Spatial Computing & Gestures	Air typing, gesture-based controls, and virtual interface interaction
Professional/Industrial Use	Workflow optimization, remote support, and real-time data for industry-specific tasks

But similar questions can be asked about embedding AI in most consumer appliances, especially the ones we use most often, including PCs, smartphones, and household appliances. 

Consider the trend of consumers using minimalist phones, for example. Sometimes the AI features are not desired. For many basic household appliances, it isn’t clear that the added AI features add enough value (toaster, kettle, blender). For other appliances, such as audio and video playback gear, users might only want faithful linear performance. 

In many cases, the increased complexity also means higher failure rates, in addition to greater cost. And then there are often battery life issues as well.   

The success rate  for new consumer electronics products is generally low, with failure rates commonly cited between 50 percent and 90 percent, depending on the source and how "failure" is defined. Some studies suggest success rates are as high as 60 percent, and failure rates as low as 40 percent. 

Packaged goods products likely fail at higher rates, perhaps as high as 95 percent. 

So it might not be too surprising that “smart” devices have not yet generally “caught on” with consumers. That noted, smart glasses seem to be faring better than the other alternatives for smart devices that are not PCs or phones. 

Device Type	2024 Installed Base / Sales (Global)	2025/2026 Projection	Notes
AI/AR Smart Glasses	~1.5 to 2 million units sold	10M units/year (by 2026)	Ray-Ban Meta is leading growth3,4,6
US Smart Glasses Users	~13 million (2024)	~14.4 million (2025)	Includes all smart glasses 6
Smart Pins/Buttons	Not reliably reported	N/A	Still niche, early-stage market 6,7

Will AI Displace "Thinking" Functions More than "Physical?"

Artificial intelligence driven job cuts are going to remain an issue for some time, with some uncertainty about the types of job functions at most risk. Most of the early analysis has suggested AI will disrupt jobs that have large amounts of repetitive or routine work, including data entry, remote customer service, manufacturing, bookkeeping or marketing content creation, for example. 

Job Category/Role	Industry/Function	Present Impact (2025)	Forecast (2030–2045)
Data Entry Clerks	Administrative/Finance	Significant automation ongoing	Continued decline, likely major reduction12
Customer Service Representatives	Retail/Tech/Finance	Widespread chatbot adoption	Large-scale job cuts expected123
Manufacturing Workers	Manufacturing	Robotics increasing automation	Major workforce reductions13
Administrative Assistants	All industries	AI tools augmenting tasks	Moderate to high risk of cuts3
Bookkeepers, Accountants	Finance	Routine tasks automated	Continued automation, fewer jobs13
Content Writers, Copywriters	Marketing/Media	AI writing tools proliferating	Entry-level jobs at high risk43
Paralegals/Legal Clerks	Legal	Document review automation	Significant job cuts expected123
Loan Officers, Underwriters	Finance	Loan processing automated	Ongoing reduction in roles12
Financial Analysts	Finance	Data analysis automated	Augmentation, some job loss13
Truck Drivers, Delivery Couriers	Logistics, Transportation	Autonomous vehicle trials	Major disruption by 203013
Warehouse, Logistics Workers	Logistics	Robotics and AI in operations	Heavy automation, fewer jobs13
Entry-Level Programmers	Tech	AI code generation increasing	Reduction in entry-level roles14
Human Resources Staff	HR/All industries	AI-driven HR automation	Significant cuts in some functions

In many cases, though, it might be hard to quantify the direct impact of AI substitution compared to other more-prosaic pressures, such as the simple need to align workforce costs with expected revenues in declining or no-growth industries. “Using AI” might in many cases be a convenient excuse for labor force cuts, even when AI really is not the actual driver of behavior. 

That noted, as AI keeps getting more proficient and trustworthy, the threat of displacement will increase. Automated vehicles displacing Uber drivers provides a good example. 

Roles requiring creativity, empathy, critical thinking, and physical adaptability (healthcare, education, leadership, social work) are less likely to be automated. Also probably safe: labor-intensive jobs in construction, skilled trades, installation and repair and maintenance.

In fact, wouldn’t it be odd if AI eventually winds up replacing “thinking” functions more than “physical” functions? Many projections seem to assume it is physical work that gets automated. 

But AI might continue to have issues that make “thinking” functions easier to displace, while manipulation of the physical world requires humans.  

Still, some estimates suggest up to 50 percent of jobs could be fully automated by 2045, with 30 percent of current U.S. employment at risk of automation by 2030. The magnitude of such forecasts typically seems exaggerated, at least to me, but the larger point of eventual significant displacement seems reasonable enough.