Some Industries are AI-Resistant

Sometimes conventional wisdom can be quite wrong. Consider the notion that artificial intelligence will disproportionately disrupt jobs in some industries such as agriculture, hospitality and construction. 

At least for the moment, these are some of the areas in which observers expect to see relatively lesser impact from wider use of AI, often in the form of robotic processes. 

And at least for the present, the actual service provided by a human is preferable to that provided by a robot. In many other cases robots cannot cost effectively handle complicated or relatively non-routine use cases that humans can manage easily. 

In fact, jobs such as data entry; transportation; customer service and manufacturing; for example, are considered more likely to be affected by applied AI. 

Other jobs in agriculture; hospitality and construction are viewed as less disrupted by AI, often because the jobs hinge on either human dexterity and customization; fine motor skills, human interaction, empathy and personalized service. 

Job Category	Likely to be Most Affected	Likely to be Least Affected	Rationale
Data Entry & Processing	Data Entry Clerk, Telemarketer, Transcriptionist, Proofreader		AI excels at repetitive tasks, pattern recognition, and data analysis. These jobs are heavily reliant on those skills.
Transportation & Logistics	Truck Driver, Taxi Driver, Delivery Driver, Dispatcher		Self-driving technology is rapidly advancing, potentially automating many transportation roles.
Customer Service	Customer Service Representative, Help Desk Technician, Chatbot Support		AI-powered chatbots and virtual assistants can handle routine customer inquiries, freeing up human agents for more complex issues.
Manufacturing & Production	Assembly Line Worker, Machine Operator, Quality Control Inspector		Automation has been present in manufacturing for a while. AI can further optimize processes, predict maintenance needs, and improve quality control.
Finance	Financial Analyst, Accountant, Tax Preparer, Investment Advisor (entry-level)		AI can analyze vast amounts of financial data, identify trends, and automate routine financial tasks.
Legal	Paralegal, Legal Secretary, Document Reviewer		AI can assist with legal research, document review, and contract analysis.
Creative & Artistic	Graphic Designer (basic), Content Writer (repetitive), Musician (algorithmic generation)		AI is making inroads in generating creative content, though the extent of its long-term impact is still debated.
Skilled Trades		Plumber, Electrician, Carpenter, Welder	These jobs require physical dexterity, problem-solving in unpredictable environments, and often hands-on customization, which are currently challenging for AI.
Healthcare		Surgeon, Nurse, Physical Therapist, Mental Health Professional	While AI can assist in diagnosis, treatment planning, and drug discovery, the human element of care, empathy, and complex decision-making in unpredictable situations is likely to remain crucial.
Management & Leadership		CEO, Manager (complex team dynamics), Entrepreneur	These roles require strategic thinking, interpersonal skills, complex problem-solving, and adaptability, which are currently difficult for AI to replicate fully.
Education		Teacher (personalized instruction, mentorship), Professor	While AI can assist with grading and personalized learning platforms, the human interaction, mentorship, and ability to adapt to individual student needs are likely to remain important.
Science & Research		Scientist (innovative research), Researcher (complex experiments)	AI can accelerate research by analyzing data and identifying patterns, but the creative and critical thinking of human scientists is still essential for breakthroughs.

AI Chip Markets and Operations Shifting to "Inference?"

 

The artificial intelligence market changes fast, and not only because new models have been popping up. 

It seems we already are moving towards inference operations as the driver of much of the chip market, for example. 

 As AI adoption scales, cloud and data center operations will prioritize inference-driven AI workloads. 

That will highlight a growing need for specialized hardware optimized for inference tasks, and that arguably is where large end users (Amazon Web Services, Google Cloud, Meta and others) have been working to create homegrown solutions. AWS and Google Cloud, for example, have invested heavily in developing their own AI accelerators, specifically designed for inference tasks. 

The AWS Inferentia is purpose-built for AI inference workloads. Google Cloud Tensor Processing Units are specifically designed for AI workloads, including inference. 

Inference might already represent up to 90 percent of all machine learning costs, for example. 

And lots of capital is being invested in startups aiming to improve processing efficiency.

It Appears U.S. Residents are Spending Less Time Out of Home Since 2003

Perhaps “return to office” policies will make a big impact, but there is some evidence U.S. residents are spending less time out of home, with some studies suggesting people are spending about an hour a day less outside the home. 

Some of us tend to think it is a lingering after effect of the Covid pandemic. But it seems that trend started long before the Covid pandemic. 

In fact, “time out of home” has been falling since at least 2003. Most of us would suspect that the internet and internet-enabled product substitutes are contributing directly, allowing us to accomplish some life pursuits or tasks online, without having to leave our homes. 

source: Taylor and Francis 

Shopping on Amazon eliminates a trip to a retail outlet. And we use food and retail delivery services as well. Videoconferencing might eliminate an office visit. 

And while not every job is conducive to “work from home,” lots of us have spent a good portion of our work lives in remote offices and were home-based, because our work allowed it, outcomes were easily quantifiable, internet apps enabled it and industry culture supported it.  

The social impacts might be just as important, such as loneliness that now seems to know no generational bounds. 

To some extent, the data could also reflect, to some degree, any age cohort effects that have higher representation or lower representation of age cohorts within the entire population, since people tend to spend the most time “out of home” between the ages of 13 and 49. 

Age Cohort	Typical Time Out of Home	Common Activities Outside Home	Factors Affecting Time Out
0-5 years	Low	Daycare, playground, family outings	Parental supervision, daycare enrollment
6-12 years	Moderate	School, sports, after-school activities	School hours, parental rules
13-18 years	High	School, extracurriculars, socializing	Independence, school commitments
19-29 years	Very High	College, work, social life, travel	Education, career, social freedom
30-49 years	High	Work, errands, parenting responsibilities	Career, family obligations
50-64 years	Moderate	Work, leisure, travel, hobbies	Nearing retirement, fewer obligations
65+ years	Low to Moderate	Leisure, healthcare visits, social events	Health, retirement, personal interests

AI-Driven Retail Traffic is Doubling Every 2 Months, Says Adobe Analysis

Adobe data from web site visits as well as a survey of marketers suggests both that generative artificial intelligence now supports marketing initiatives aimed at driving site visits and buying. 

The retail insights are based on analysis of more than one trillion visits to U.S. retail sites, as measured by shoppers clicking on a link. “Between Nov. 1 and Dec. 31, 2024, traffic from generative AI sources increased by 1,300 percent compared to the year prior,” the study reports.

To be sure, “generative AI traffic remains modest compared to other channels, such as paid search or email,” Adobe says. On the other hand, generative AI traffic has been  doubling every two months since September 2024.

source: Adobe 

Traffic to U.S. e-commerce websites from generative AI sources was up 1,200 percent in February 2025 compared to six months ago, and artificial intelligence-driven traffic has been doubling every two months since September, Adobe reports. 

source: Adobe 

The survey of 5,000 consumers found that the most common uses of AI are for research (55 percent of respondents), product recommendations (47 percent), information about deals (43 percent), gift suggestions (35 percent), information about unique products (35 percent), and to create shopping lists (33 percent).

A similar study of U.K. consumers found similar growth.  

That suggests AI already is becoming a driver of consumer behavior that will affect use of other marketing methods, including search and email, and will probably emerge as among the more-important monetization methods for use of generative AI models. 

Marketing Platform	Estimated Shift to GenAI	Timeframe	Source
Search Engines	25% decrease	By 2026	Gartner
Ai Content Creation	30-50% efficiency gain	Current	Bain
Ad Campaigns	10-25% higher ROI	Current	Bain
Customer Segmentation	Significant improvement	Current	Useinsider
Email Marketing	Potential decrease*	By 2026
Social Media	Potential decrease*	By 2026

Perhaps just as predictably, Adobe reports that AI-powered shopping is producing results.

So Language Models, as Do Humans, Will Cheat to Gain Rewards!

Researchers at OpenAI have found that language models, like humans, often look for loopholes to exploit benefit programs. As it turns out, language models using chain of thought reasoning exhibit the same behavior! 

As people share online subscription accounts against terms of service; claim subsidies meant for others; or interpret regulations in unforeseen ways to gain benefits (lying about a birthday at a restaurant to get free cake, for example), so language models using CoT and reinforcement learning.

According to the researchers, exploiting unintended loopholes, commonly known as reward hacking, is a phenomenon where AI agents achieve high rewards through behaviors that don’t align with the intentions of their designers.

In other words, the models can lead to misbehavior, where the model “cheats.” Furthermore, the “cheating is undetectable by the monitor because it has learned to hide its intent in the chain-of-thought,” the researchers say. 

As optimization is applied, there is “potential for increasingly sophisticated and subtle reward hacking” by the models, they say. “Our models may learn misaligned behaviors such as power-seeking, sandbagging, deception, and strategic scheming.”

In other words, the models learn to hide their intent, which is to thwart human-imposed rules. Punishing an artificial intelligence model  for deceptive or harmful actions doesn't stop a model from misbehaving; it just makes it hide its deviousness!