In the depths of Reddit threads and tech forums, a fascinating trend is emerging that caught my attention. People are starting to push back against Agentic AI—but not in the way you might expect.

The Unexpected Source of Resistance

The resistance isn’t coming from startups and SMEs, who remain largely supportive of AI automation, drawn by the promise of “cheap” labor costs and accessible funding opportunities. Instead, the pushback is emerging from a different group entirely: employees within established companies who are feeling directly threatened by the rise of these automations.

This distinction is crucial because it reveals a fundamental disconnect in how different stakeholders view the AI revolution. While business leaders and entrepreneurs see opportunity, the workforce sees uncertainty.

The Reality Behind the Hype

What makes this resistance particularly telling is that it’s happening at a time when truly effective AI automations—ones that work reliably in real-world scenarios—are still surprisingly few and far between. This creates a paradoxical situation where people are simultaneously worried about being replaced by AI while also frustrated by AI’s current limitations.

After attending a recent session about Agentic AI and real-world use cases hosted by The Generative Beings (TGB) Singapore, I began to understand the deeper reasons behind this sentiment. The gap between AI’s marketed potential and its actual performance in production environments is creating a perfect storm of anxiety and skepticism.

The more I’ve observed these discussions and analyzed the current state of AI implementation, the more convinced I’ve become that we’re facing a critical inflection point. The enthusiasm for AI automation is colliding head-on with practical realities and human concerns that many organizations have yet to fully address.

This isn’t just about technology anymore—it’s about change management, workforce psychology, and the complex dynamics of innovation adoption in established organizations. Here’s why I believe we’re not ready for the next phase of the AI revolution.

The Human Factors: When “Efficiency” Becomes Counterproductive

The Bill Gates “Lazy Way” Philosophy and Its Unintended Consequences

“I choose a lazy person to do a hard job because a lazy person will find an easy way to do it.”

This quote, widely attributed to Bill Gates, has become a rallying cry for AI adoption. The philosophy traces back to Frank B. Gilbreth Sr. in 1920, who studied worker efficiency and noted that “lazy” workers often eliminated unnecessary movement and reduced fatigue.

But what happens when we apply this “lazy way” thinking to AI implementation at scale?

The outcome reveals significant problems. Critics argue this approach can lead to poor long-term decisions, as the “easiest way” might sacrifice quality, create future problems, or even cause harm in critical industries. In AI implementation, this manifests as companies rushing to deploy automation without considering sustainability, accuracy, or long-term consequences.

We all want to get work done quickly and efficiently—but when that desire for shortcuts drives decision-making, we end up with AI solutions that look impressive in demos but fail in production, create new problems, or simply move complexity rather than solving it.

The AI Startup Bubble: Thousands of Companies, Same Promise

The numbers surrounding AI startup funding are staggering and mirror classic bubble patterns. In 2024, generative AI companies worldwide raised $56 billion from VCs across 885 deals—a 92% increase from 2023’s $29.1 billion across 691 deals. Since 2024, thousands of new AI companies have formed, with funding to AI companies surpassing $170 billion.

The dot-com parallels are striking: There are thousands of AI startups pitching similar tools—chatbots, app builders, image generators—creating a “sea of lookalikes” fighting for the same dollars. Many AI startups are receiving billion-dollar valuations without proven revenue models or customer traction, with VCs driven by FOMO rather than fundamentals.

The bubble indicators are everywhere. Builder.ai, a Microsoft-backed AI startup once valued at $1.2 billion, filed for bankruptcy by May 2025 after exaggerating revenue by 300% and using humans to do work advertised as AI automation. The company’s so-called “AI” wasn’t doing much—behind the curtain were hundreds of engineers manually coding what was being advertised as automated magic.

But here’s the deeper question: If everyone is running a tech company, who’s focused on building real-world businesses that actually drive the economy?

The Economic Sector Imbalance: Tech Gets Billions, Agriculture Gets Scraps

While AI startups feast on venture capital, sectors that actually feed and supply the world are starving for investment. AgTech made up only 1.82% of total venture capital funding in 2024, with just $5.7 billion raised across 736 startups. The food and agriculture technology market raised just $15.6 billion in 2023, representing only 5.5% of all venture capital, down from 7.6% in 2021.

AgTech investment dropped 25.6% in 2024, while AI startups continue to rake in billions. This creates a dangerous imbalance where speculative AI ventures receive massive funding while essential sectors like agriculture, manufacturing, and production struggle for investment.

The implications are profound: We’re creating an economy where everyone wants to build the next AI unicorn, but fewer people are working on the fundamental systems that keep society functioning. Who’s optimizing crop yields when everyone’s building chatbots? Who’s improving manufacturing efficiency when all the talent and capital flows to Silicon Valley?

The Technological Factors: The Hidden Costs of the AI Revolution

Tokenization Costs: When the Math Doesn’t Add Up

While OpenAI recently slashed GPT-4o costs to $4 per million tokens, the broader cost structure remains problematic. AI workloads demand expensive high-performance GPUs and TPUs that can be far more expensive than standard compute instances, and specialized hardware costs can quickly add up.

The reality check is sobering: McKinsey research shows that while 78% of organizations use AI in at least one business function, few are experiencing meaningful bottom-line impacts. Many companies find that operational costs often exceed the value generated, particularly for complex implementations.

Consider a typical enterprise AI deployment: Companies spend hundreds of thousands on implementation, ongoing token costs, specialized hardware, and maintenance—only to discover that the productivity gains don’t justify the expense. The “AI ROI” promised in sales pitches often evaporates when real-world complexity meets the limitations of current technology.

Data Centralization: The New Digital Oligarchy

The scale of data centralization happening right now is unprecedented and deeply concerning. Microsoft, Meta, Google, and Amazon spent a combined $125 billion on AI data centers between January and August 2024 alone. Microsoft plans to spend $80 billion in fiscal 2025 just on AI-enabled data center construction.

Tech megacaps plan to spend more than $300 billion in 2025 on AI infrastructure, up from $230 billion in 2024. This massive consolidation of computing power and data access among a few players is creating supply constraints, with data center vacancy rates below 1% in key markets and prices rising 35% between 2020 and 2023.

This reverses the democratizing promise of the internet. Instead of making information more accessible, we’re creating a system where a handful of companies control the computational resources needed for AI development. Access to these capabilities is becoming more expensive, not cheaper, effectively creating barriers to entry that favor large corporations over innovators.

The irony is profound: The technology that was supposed to democratize intelligence is instead concentrating power in the hands of a few tech giants who can afford billion-dollar data center investments.

The IP Protection Exodus: Innovation Goes Underground

Organizations are increasingly moving their proprietary data offline to prevent it from being used to train competitors’ AI models. Even in China, over 144 companies registered to develop LLMs in 2024, but only about 10% were still actively investing in large-scale model training by year’s end.

This trend toward data hoarding is reversing the collaborative, open nature that originally made the internet valuable for innovation and knowledge sharing. Companies that once shared data and collaborated on standards are now building walls around their information, viewing every piece of data as potential competitive advantage that could be stolen by AI training.

The result is a fragmentation of knowledge and a slowing of collaborative innovation. Instead of building on each other’s work, organizations are retreating into proprietary silos, reducing the overall pace of genuine technological advancement.

The Perfect Storm: Why This All Matters

What we’re witnessing isn’t just growing pains—it’s a fundamental mismatch between AI’s promises and current reality, amplified by economic incentives that prioritize speculation over substance.

The human factors create organizational resistance: When employees feel threatened by AI that doesn’t even work well yet, when companies pursue “efficiency” over effectiveness, and when entire economic sectors are neglected in favor of AI hype, we’re building systemic resistance to technological change.

The technological factors create unsustainable economics: When AI costs more than it delivers, when data access becomes oligopolized, and when innovation retreats behind proprietary walls, we’re creating a technology landscape that can’t support the transformative changes being promised.

The combination is toxic: We have a sector driven more by what economist John Maynard Keynes described as “animal spirits”—investor enthusiasm unrelated to fundamentals—than by sustainable business models or genuine technological breakthroughs.

What This Means for the Future

The research reveals we’re facing a classic bubble that will require significant correction before AI can deliver on its transformative promises. The current trajectory is unsustainable:

  • Economically: The cost structures don’t support the promised returns
  • Socially: The workforce disruption is happening faster than the benefits
  • Technologically: The centralization is stifling the innovation needed for genuine breakthroughs

This doesn’t mean AI is worthless or that the revolution won’t happen. But it does mean we’re not ready for it yet. We need:

  1. Honest ROI assessments that account for total cost of ownership
  2. Investment rebalancing toward sectors that actually drive economic fundamentals
  3. Workforce transition strategies that address legitimate employee concerns
  4. Decentralization efforts that prevent AI from becoming a tool of oligopoly
  5. Sustainable business models that create genuine value rather than speculative hype

The next phase of the AI revolution will happen—but first, we need to soberly address the human and technological factors that are creating resistance and unsustainable economics.

Until then, the backlash will continue to grow, and the gap between AI’s promise and reality will keep widening. The question isn’t whether AI will transform the world—it’s whether we’ll build that transformation on solid foundations or watch it collapse under the weight of its own contradictions.

The Reddit threads and employee resistance aren’t just noise—they’re early warning signals that our current approach to AI adoption is fundamentally flawed. It’s time we started listening.

This article was written with the help of research from, ironically, Claude Pro and Perplexity Pro.

References

Bill Gates Quote and Origins

  1. Quote Investigator (2014). “Quote Origin: Choose a Lazy Person To Do a Hard Job Because That Person Will Find an Easy Way To Do It.” Retrieved from: https://quoteinvestigator.com/2014/02/26/lazy-job/
  2. Africa Check (2023). “No evidence Bill Gates said he would ‘choose a lazy person to do a hard job’.” Retrieved from: https://africacheck.org/fact-checks/meta-programme-fact-checks/no-evidence-bill-gates-said-he-would-choose-lazy-person-do
  3. Birkman (October 11, 2023). “The Lazy Guy’s Guide to Waste Management.” Retrieved from: https://birkman.com/resources/articles/the-lazy-guys-guide-to-waste-management
  4. Medium – Denis Avguštin (July 24, 2023). “To Hire a Lazy Person.” Retrieved from: https://www.linkedin.com/pulse/hire-lazy-person-denis-avgu%C5%A1tin
  5. Medium – Ugur Yagmur (November 3, 2022). “Why was Bill Gates Wrong about Hiring Lazy People?” Retrieved from: https://medium.com/codex/why-are-all-good-software-engineers-successful-investors-why-was-bill-gates-wrong-8ff8a5b90a01

AI Startup Funding and Bubble Analysis

  1. TechCrunch (March 18, 2025). “Generative AI funding reached new heights in 2024.” Retrieved from: https://techcrunch.com/2025/01/03/generative-ai-funding-reached-new-heights-in-2024/
  2. CB Insights (1 week ago). “AI 100: The most promising artificial intelligence startups of 2025.” Retrieved from: https://www.cbinsights.com/research/report/artificial-intelligence-top-startups-2025/
  3. Mintz (2025). “The State of the Funding Market for AI Companies: A 2024 – 2025 Outlook.” Retrieved from: https://www.mintz.com/insights-center/viewpoints/2166/2025-03-10-state-funding-market-ai-companies-2024-2025-outlook
  4. Tech Startups (May 24, 2025). “Builder.ai, a Microsoft-backed AI startup once valued at $1.2 billion, files for bankruptcy: Is AI becoming another .com bubble?” Retrieved from: https://techstartups.com/2025/05/24/builder-ai-a-microsoft-backed-ai-startup-once-valued-at-1-2-billion-files-for-bankruptcy-is-ai-becoming-another-com-bubble/
  5. Crunchbase News (January 9, 2025). “Startup Funding Regained Its Footing In 2024 As AI Became The Star Of The Show.” Retrieved from: https://news.crunchbase.com/venture/global-funding-data-analysis-ai-eoy-2024/
  6. Crunchbase (December 31, 2024). “The Largest AI Startup Funding Deals Of 2024.” Retrieved from: https://news.crunchbase.com/ai/largest-ai-startup-funding-deals-2024/
  7. TechCrunch (1 month ago). “Here are the 24 US AI startups that have raised $100M or more in 2025.” Retrieved from: https://techcrunch.com/2025/06/18/here-are-the-24-us-ai-startups-that-have-raised-100m-or-more-in-2025/
  8. Medium – JEC Mississauga (May 25, 2025). “The Rise of AI Startups: The Future of Technology or a Bubble Waiting to Burst?” Retrieved from: https://medium.com/@jecmississauga/the-rise-of-ai-startups-the-future-of-technology-or-a-bubble-waiting-to-burst-79fe6c4879da
  9. EdgeDelta (March 13, 2025). “7 Vital AI Startup Funding Statistics for 2024 Revealed.” Retrieved from: https://edgedelta.com/company/blog/ai-startup-funding-statistics/

Agriculture and Manufacturing Investment Trends

  1. Farmonaut (1 week ago). “Venture Capital Trends In Agriculture Tech & Funds 2025.” Retrieved from: https://farmonaut.com/blogs/venture-capital-trends-in-agriculture-tech-funds-2025
  2. Global Ag Tech Initiative (January 13, 2025). “2024 AgTech Venture Capital Investment and Exit Round Up.” Retrieved from: https://www.globalagtechinitiative.com/digital-farming/2024-agtech-venture-capital-investment-and-exit-round-up/
  3. CropLife (January 22, 2025). “2024 AgTech Venture Capital Investment and Exit Round Up.” Retrieved from: https://www.croplife.com/precision-tech/2024-agtech-venture-capital-investment-and-exit-round-up/
  4. AgFunder (January 30, 2025). “AgFunder Global AgriFoodTech Investment Report 2024.” Retrieved from: https://agfunder.com/research/agfunder-global-agrifoodtech-investment-report-2024/
  5. StartUs Insights (February 13, 2025). “AgriTech Report 2025.” Retrieved from: https://www.startus-insights.com/innovators-guide/agritech-report-2024/
  6. Agriculture Dive (January 27, 2025). “Investment in agtech startups plummeted in 2024.” Retrieved from: https://www.agriculturedive.com/news/agtech-vc-deals-plummet-startups-2024/738361/
  7. Climate Insider (June 19, 2024). “5 Agtech-Focused VCs You Should Know in 2024.” Retrieved from: https://climateinsider.com/2024/05/14/5-agtech-focused-vcs-you-should-know-in-2024/
  8. Agritech Digest (December 19, 2024). “Top Venture Capitalists to Watch Out for in 2025.” Retrieved from: https://agritechdigest.com/top-venture-capitalists-to-watch-out-for-in-2025/
  9. Visible.vc “10+ Foodtech Venture Capital Firms Investing in 2025.” Retrieved from: https://visible.vc/blog/foodtech-venture-capital/
  10. Agriculture Dive (December 4, 2023). “Agtech funding is rebounding. Which startups attract investors?” Retrieved from: https://www.agriculturedive.com/news/agtech-startup-investment-vc-funding-ai-biologicals-fertilizer/701433/

AI Costs, ROI, and Tokenization

  1. Hypersense Software (January 31, 2025). “2024 AI Growth: Key AI Adoption Trends & ROI Stats.” Retrieved from: https://hypersense-software.com/blog/2025/01/29/key-statistics-driving-ai-adoption-in-2024/
  2. PwC (2025). “2025 AI Business Predictions.” Retrieved from: https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html
  3. Morgan Stanley (2025). “5 AI Trends Shaping Innovation and ROI in 2025.” Retrieved from: https://www.morganstanley.com/insights/articles/ai-trends-reasoning-frontier-models-2025-tmt
  4. NVIDIA Blog (May 1, 2025). “Explaining Tokens — the Language and Currency of AI.” Retrieved from: https://blogs.nvidia.com/blog/ai-tokens-explained/
  5. Medium – Arnav Malik (November 17, 2024). “Embracing the Future: How Falling Token Prices and Technological Advances Are Shaping the AI Industry.” Retrieved from: https://medium.com/@arnavseas/embracing-the-future-how-falling-token-prices-and-technological-advances-are-shaping-the-ai-28ce8db8a2bc
  6. Rapid Innovation (September 19, 2024). “AI Agents Revolutionize Asset Tokenization | Ultimate Guide 2024.” Retrieved from: https://www.rapidinnovation.io/post/ai-agents-in-asset-tokenization-the-new-digital-ownership
  7. McKinsey (March 12, 2025). “The state of AI: How organizations are rewiring to capture value.” Retrieved from: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
  8. PYMNTS.com (December 15, 2023). “Tokens, Characters and Usage Fees: Decoding the AI Price War.” Retrieved from: https://www.pymnts.com/news/artificial-intelligence/2023/tokens-characters-and-usage-fees-decoding-the-ai-price-war/
  9. CloudZero (June 12, 2025). “AI Costs In 2025: A Guide To Pricing + Implementation.” Retrieved from: https://www.cloudzero.com/blog/ai-costs/

Data Center Investment and Infrastructure

  1. CNBC (April 28, 2025). “AI data center boom isn’t going bust but the ‘pause’ is trending at big tech companies.” Retrieved from: https://www.cnbc.com/2025/04/27/ai-data-center-boom-isnt-going-bust-but-the-pause-is-trending.html
  2. Yahoo Finance (December 30, 2024). “How many billions Big Tech spent on AI data centers in 2024.” Retrieved from: https://finance.yahoo.com/news/many-billions-big-tech-spent-171500839.html
  3. Visual Capitalist (December 23, 2024). “Visualizing Big Tech Company Spending On AI Data Centers.” Retrieved from: https://www.visualcapitalist.com/visualizing-big-tech-company-spending-on-ai-data-centers/
  4. Bloomberg Professional Services (December 11, 2024). “Big tech 2025 capex may hit $200 billion as gen-AI demand booms.” Retrieved from: https://www.bloomberg.com/professional/insights/technology/big-tech-2025-capex-may-hit-200-billion-as-gen-ai-demand-booms/
  5. CNBC (January 4, 2025). “Microsoft expects to spend $80 billion on AI-enabled data centers in fiscal 2025.” Retrieved from: https://www.cnbc.com/2025/01/03/microsoft-expects-to-spend-80-billion-on-ai-data-centers-in-fy-2025.html
  6. CNBC (February 8, 2025). “Tech megacaps plan to spend more than $300 billion in 2025 as AI race intensifies.” Retrieved from: https://www.cnbc.com/2025/02/08/tech-megacaps-to-spend-more-than-300-billion-in-2025-to-win-in-ai.html
  7. McKinsey (April 28, 2025). “The cost of compute: A $7 trillion race to scale data centers.” Retrieved from: https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-cost-of-compute-a-7-trillion-dollar-race-to-scale-data-centers
  8. MIT Technology Review (March 26, 2025). “China built hundreds of AI data centers to catch the AI boom. Now many stand unused.” Retrieved from: https://www.technologyreview.com/2025/03/26/1113802/china-ai-data-centers-unused/
  9. McKinsey (October 29, 2024). “AI power: Expanding data center capacity to meet growing demand.” Retrieved from: https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/ai-power-expanding-data-center-capacity-to-meet-growing-demand
  10. Axios (December 23, 2024). “Tech dollars flood into AI data centers in capital expenditure boom.” Retrieved from: https://www.axios.com/2024/12/20/big-tech-capex-ai

Additional AI Market Analysis

  1. Y Combinator “AI (Artificial Intelligence) Startups funded by Y Combinator (YC) 2025.” Retrieved from: https://www.ycombinator.com/companies/industry/ai

Note: All URLs and publication dates are based on the research conducted. Some articles may have been updated since the time of research. Access dates for web sources: July 2025.