FOBO - Fear of Being Obsolete

I don’t want to sound like a broken record—but maybe I do.

It seems like every week, I talk about the impact of AI on jobs, but I do that because I want to see a positive impact, not a negative one. And I want to help everyone I can.

The reality is the number of headlines warning of AI taking jobs is on the rise, fueling anxiety across industries.

And while AI will inevitably replace some jobs, the bigger threat isn’t the technology itself—it’s losing your job to someone who knows how to use AI better than you do.

FOBO, which stands for "Fear of Becoming Obsolete," was a prominent buzzword at this year's Davos conference, reflecting growing anxiety among workers about being replaced by advancements in artificial intelligence (AI) and potentially becoming irrelevant in the job market.

While I am not a fan of the elitist conference run by Klaus Schwab, it is a global platform for leaders in business and government to discuss world topics. And they are doing quite a bit of research on how AI will affect us all.

So if you don’t want to be obsolete, keep reading.

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FOBO - Fear of Being Obsolete

The K-Shaped Market: Who Thrives with AI and Who Falls Behind?

Despite our massive technology improvements economist Robert Gordon documented the slowdown in U.S. productivity since the 1970s, alongside exponential technological growth. This is a shocking paradox. We have seen great advancements in tech —the PC, the Internet, and smartphones, but we are still slowing down.

Nobel Laureate Robert Solow echoed this sentiment, "You can see the computer age everywhere but in the productivity statistics.”

However, AI has the potential to change that—but it’s still early for most of us. Rather than replacing people, it augments them. I hope that means that we’ll be able to avoid a rift between the haves and the have-nots.

I believe that “AI won’t take your job—someone who knows how to use AI will.” A quote from Economist Richard Baldwin at the 2023 World Economic Forum's Growth Summit.

So my analysis is around finding tasks that can be enhanced with AI and looking at jobs for the future, many that don’t exist today.

Anthropic the makers of the popular Claude AI model, this month released the Anthropic Economic Index, an initiative aimed at understanding AI's effects on labor markets and the economy over time.

Their findings so far are AI use leans more toward augmentation (57%), where AI collaborates with and enhances human capabilities, compared to automation (43%), where AI directly performs tasks. AI use is more prevalent for tasks associated with mid-to-high-wage occupations like computer programmers and data scientists but is lower for both the lowest- and highest-paid roles. This likely reflects both the limits of current AI capabilities, as well as practical barriers to using the technology.

But that still doesn’t assuage the fears of workers, especially knowledge workers, who may be afraid of their professional future.

Fear of Being Obsolete

Firstly, the fear of being obsolete is a rational fear for most. Even before the dawn of generative AI. Imagine being a farmer when tractors came on the scene, or a blacksmith when automobiles took over for horses, it’s part of progress.

Farmers became fewer but they went to work in factories or started other businesses. As someone who spent a lot of my childhood working on farms, it’s a tough job even with modern machinery. It increased our food availability and gave us more choice in our diet along with our choice in career.

Those blacksmiths could reskill to become machinists for cars and the horse and buggy drivers drove cabs, now they are being replaced by gig workers driving Uber and Lyft.

H.G. Wells expresses the sentiment, “Adapt or perish, now as ever, it’s nature’s exorable imperative.”

Workers who embrace AI can automate repetitive tasks, analyze vast amounts of data, and optimize decision-making—making themselves indispensable Those who resist AI, however, risk being outpaced by colleagues who can work smarter and faster with it.

This isn’t just an isolated issue—it’s a pattern unfolding across industries. Despite AI’s growing capabilities, many companies struggle to integrate it into daily operations. The biggest obstacle isn’t the technology itself; it’s the people using (or avoiding) it. Take a look at the people who are successful in AI emergent enterprises, where companies like Moderna are making AI upskilling a priority for everyone in their company to improve productivity not to decrease headcount.

A growing AI skills gap and fear of job displacement are driving resistance. Over a third of managers admit they’ve never used AI tools like ChatGPT, and a staggering 74% of employees worry AI could make their roles obsolete.

Without a strategy to bridge this gap, companies risk falling into a K-shaped divide—where those who embrace AI surge ahead, while those who resist it are left behind.

The K-Shaped AI Economy

A K-shaped recovery describes an economic rebound in which segments recover at markedly different rates. Some industries, such as technology and finance, resume rapid growth, while others, like travel or traditional retail, continue to struggle.

The aggregate recovery appears split—one arm of the “K” represents fast-growing sectors and the other stagnates or declines. This concept gained attention following uneven rebounds after economic shocks, such as the COVID-19 crisis.

In a K-shaped AI economy, the divergence mirrors that recovery model. Companies that integrate artificial intelligence into operations—through process automation, predictive analytics, and enhanced customer insights—can achieve superior performance.

These firms drive innovation, optimize efficiency, and gain competitive advantages. In contrast, businesses that fail to adopt AI risk operational inefficiencies and declining market share, eventually becoming obsolete.

Example

• AI-Driven Enterprise: A manufacturing firm invests in AI to optimize supply chain logistics and predictive maintenance. This leads to reduced downtime, lower costs, and enhanced competitiveness.

• Non-Adopter: A similar firm that ignores AI integration continues with outdated processes. Over time, it faces higher costs, reduced productivity, and loss of market share as competitors innovate.

This divergence creates two distinct economic trajectories within the same industry. Companies using AI will thrive along the upward curve of the “K,” while those that do not will follow the downward curve.

Non-adopters Face Stagnation or Job Loss

Those resistant to AI adoption risk being left behind. These are the “losers” in the AI economy. AI is already replacing administrative roles, with up to 3 million UK private-sector jobs at risk. Knowledge workers in legal, finance, and marketing who fail to adopt AI tools are seeing job functions automated out from under them.

This shift isn’t decades away—it’s happening right now. Companies and employees must act fast to gain AI skills before the divide becomes irreversible. This is a simple idea, if you want to remain relevant you need to increase your use of AI to make your company and yourself more productive.

Why AI Adoption Is Stalling

Despite the clear advantages, many businesses struggle to integrate AI into daily workflows. Why?

• The AI Skills Gap - Many employees lack hands-on experience with AI tools, making it difficult to adopt new workflows.

• Job Fragmentation & Misunderstanding - Most professionals think of their job as one unified role rather than a collection of tasks—many of which can be augmented or automated with AI.

• Fear of Replacement - Employees aren’t just hesitant—they’re worried. 74% of workers fear AI will make them obsolete, creating an emotional resistance to adoption.

• Tool Overload - With AI tools flooding the market, businesses struggle to identify which solutions deliver real value versus hype.

A study analyzing the impact of AI on translators found that as AI performance improved, translators experienced a decline in both the number of accepted jobs and earnings, highlighting the tangible effects of AI on specific professions.

So what can translators do to stay relevant? In many cases, adaptation is necessary—and sometimes even a career shift. It’s a tough pill to swallow, but an important reality. Some jobs will require a change in and will replace jobs.

The only sure way to lose though is to not adapt.

How to Stay on the Right Side of the AI Divide

To avoid obsolescence, professionals and businesses must rethink how they integrate AI.

Break Jobs Into AI-Augmentable Tasks

• Identify which parts of your role AI can optimize—automating the mundane, so you can focus on high-value work. I look at roles in customer support, sales, and marketing to be optimized.

• Sales teams, for example, can use AI for prospecting, CRM updates, and email generation, freeing them to focus on relationship-building.

Invest in AI Literacy & Training

• AI isn’t replacing jobs outright—it’s replacing tasks. Employees need structured training on how to collaborate with AI tools effectively.

• Companies should invest in AI training programs and hands-on experimentation with AI-powered workflows.

Develop AI Management Skills

• The most valuable employees won’t be the ones doing manual tasks—they’ll be the ones orchestrating AI systems for maximum efficiency.

• Businesses should encourage AI adoption not just for tech teams, but across all departments.

Your Next Steps: Don’t Get Left Behind

AI won’t replace people—people who know how to use AI will replace those who don’t.

• Break down your role into AI-enhanced tasks. Find areas where AI can save you time and boost output.

• Adopt AI tools early. The faster you integrate AI into your workflow, the more value you’ll gain.

• Train your team. Businesses must prioritize AI literacy and empower employees to use AI effectively.

• Create AI-powered roles. Shift from execution-based jobs to AI-enhanced decision-making.

The AI revolution is happening now. The choice is clear: embrace AI and level up—or be left behind.

That’s almost how I ended this newsletter. But on reflection, it sounded too harsh.

So I rethought my phrasing and tried to make things more actionable. I usually include a link to my free email course because that is actionable but I also want everyone reading this to look at the opinions of others, gain insights from both companies and governments on how AI will change our world.

Finally, I think being an advocate for yourself and your business to accelerate AI adoption is a critical step in making sure AI makes your life better and doesn’t cause a downward spiral.

Normally, I recommend only AI applications, but in this case, a time-tracking tool can help identify automation opportunities.

• Timely - Timely’s AI-powered automation minimizes manual time entry by automatically categorizing your work activities. It not only tracks your time but also learns your work patterns to suggest optimizations and identify repetitive tasks.

• Rescue Time - RescueTime runs in the background to automatically log time spent on applications and websites. It helps you identify which digital tasks consume your time, highlighting opportunities for AI automation.

• Memtime - With Memtime you can generate custom timesheets and reports to analyze your productivity and logged hours. Memtime remembers every minute of your work as soon as you install it. You can take the reports and upload them to ChatGPT to look for your work patterns and find more opportunities for augmentation with AI.

Self-Assessment: Time Allocation and AI Automation

I mentioned in the Deep Dive this week that you should consider your job as a set of tasks, and analyze what tasks could be augmented by AI. Here’s a prompt that in combination with this week’s AI Toolbox could help you accomplish that.

This prompt provides a systematic approach to reviewing your current workload and identifying tasks that could be streamlined with AI. By following each section step by step, you can develop a clear picture of your time allocation and create a focused action plan to implement AI tools where they offer the highest return on investment.

Using the tools in this week’s AI Toolbox could help you with this exercise.

# Self-Assessment: Time Allocation and AI Automation

Welcome! Today, we'll work through a self-assessment designed to help you review how you spend your time and determine which tasks might be augmented or replaced with AI. I will guide you through each section. Let’s get started!

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## Section 1: Task Inventory

1. **List your daily and weekly tasks.**  
   - Break tasks into categories (e.g., administrative, operational, creative, strategic).  
   - *Example:* "Email management, scheduling, report generation, data analysis."

2. **Record your estimated time spent on each task over the past week.**  
   - Use a table or bullet list for clarity.

---

## Section 2: Task Analysis

1. **For each task, answer:**
   - What is the primary objective of the task?
   - Is the task repetitive, or does it require complex human judgment?
   - How critical is human intuition in this task?

2. **Rate each task’s potential for AI augmentation or replacement on a simple scale (e.g., 1 to 5).**  
   - *Example:* "Email filtering – 4 (high automation potential)."

---

## Section 3: Identifying AI Opportunities

1. **For tasks with high automation potential:**
   - List current AI tools or platforms that could manage or assist with the task.  
   - *Example:* "Email management could use AI-based sorting and response generation tools."

2. **For tasks with moderate potential:**
   - Consider hybrid approaches where AI assists in data processing or preliminary analysis while you review results.  
   - *Example:* "Data analysis reports could be drafted by AI and then refined manually."

3. **For tasks with low automation potential:**
   - Note why human intervention remains essential (e.g., strategic decision-making, creative input).

---

## Section 4: Action Plan & Reflection

1. **Summarize which tasks to automate, augment, or maintain as is.**

2. **Define measurable goals:**
   - Examples include time saved per week, improved accuracy, or increased output.  
   - *Example:* "Reduce time spent on email management by 50% with AI tools."

3. **Outline next steps:**
   - Research specific AI tools for high-potential tasks.
   - Test and compare solutions on a trial basis.
   - Schedule periodic reviews to assess impact.

4. **Reflect on your findings:**
   - Which tasks have the highest ROI for automation?
   - How will reallocating time free you to focus on strategic or high-value work?

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## Output Guidance

- **Format:** Provide your analysis in a table or detailed list.
- **Detail Level:** Be specific about time estimates and task functions.
- **Examples:** Include real tasks from your schedule and refer to specific AI tools or approaches you are considering.

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This prompt framework provides a clear, actionable picture of your time management and highlights areas that may benefit from AI assistance. For additional insights, refer to recent business reviews on productivity and automation improvements. 

I appreciate your support.