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.

If AI Cannot Master Emotional Intelligence, These Jobs are "Safe"

If AI will never be able to truly master emotional intelligence  or originality, there are some possible implications for skills that could retain or gain value in job markets.

For example, roles that demand empathy, interpersonal judgment, and nuanced social interaction will retain value. That might be a good thing for:

• Therapists, counselors, and social workers

• Nurses, doctors, and caregivers

• Teachers and early childhood educators

• Some personnel  professionals, coaches, and conflict mediators

• Customer service roles involving delicate or high-stakes interaction

• Sales personnel (especially complex sales in business-business areas)

• Hospice workers

• Occupational therapists

• School counselors

• Childcare providers and nannies

• Youth mentors

• Leaders in any organization

• Hospitality managers

• Flight attendants

• Diplomats

• Negotiators

• Crisis intervention specialists

• Journalists (some, at least)

• Creative directors

• Public relations leaders

• User experience designers

• Nonprofit leaders

• Community organizers

• Volunteer coordinators.

These roles require real-time adaptation to complex emotional cues and relational context, and AI struggles to do so.

Also, jobs that rely on genuinely original thinking in art, design, entrepreneurship or strategic vision will remain human-led, even if AI aids the process. That should be good for:

• Artists, writers, and filmmakers

• Architects and designers

• Entrepreneurs and product innovators

• Strategy consultants and futurists.

So note that not all these roles are necessarily “high-paying” or “thinking-based.” You might conclude that many of the “empathy required” jobs, though, tend to be dominated by females, which might have some implications for job protection and value. 

I’m not sure we can reach any similar conclusions about the “originality” roles.

Why Meta Invested in Scale AI

Language model “hallucinations” might always be an issue to some degree, but Meta’s recent investment in Scale AI shows the importance of techniques such as "human-in-the-loop" data labeling. 

“Edge cases” often are the issue, as human language is inherently ambiguous. A single word can have multiple meanings depending on context, tone, and cultural nuances. Machines struggle with this without explicit human guidance. And that’s where humans help. 

When undertaking tasks involving sentiment analysis, summarization or dialogue generation, subjectivity is involved. There isn't always one "correct" answer, and human guidance is helpful there. 

It often is noted that language models do not possess common sense or real-world knowledge in the way humans do, so HITL helps prevent models from generating nonsensical or logically flawed responses.

And while AI models are generally good at learning from patterns, they often struggle with "edge cases" involving unusual, rare, or complex scenarios that aren't well-represented in the training data.

Human annotators can identify, interpret, and correctly label these edge cases. 

Likewise, human-in-the-loop processes allow for the identification and mitigation of biases in the source data.

Also, HITL helps models LLM generate responses that are more aligned with human preferences and ethical guidelines: safe, useful and contextually appropriate for human users.