The AI Bubble: Efficiency Theatre at Scale

Photo Credit: Mick Haupt

AI is booming because of a narrative that it can deliver more with less.

Investors and founders alike have fully embraced the idea that AI lowers costs, accelerates growth, and signals efficiency at a time when capital is harder and more expensive to secure.

The claim is that startups can ship products with fewer people, burning less capital on salaries, and still be disruptive in a capital-constrained market.

In today's venture capital environment, shaped by rising interest rates and valuation collapses, that promise is potent and powerful.

And yet, despite the hype, the startup failure rate remains as predictable and steady as ever.

2017: The Spark

Google publishes 'Attention Is All You Need' introducing the 'Transformer' model architecture that made training large language models (LLMs) feasible and viable.

2018-2020: Scaling up

OpenAI builds on this breakthrough, releasing successive GPT models, and LLMs move from academic novelty to viable commercial tech.

2022: The Cultural Moment

ChatGPT launches and goes viral, reaching 1 million users in just five days — the fastest adoption of any consumer product in history.

Generative AI bursts into the public consciousness, reshaping expectations and creating an entirely new product category overnight.

2022-2023: The SaaSacre

Central banks raise interest rates to curb post-pandemic inflation, driving up the cost of capital and shrinking investor appetite for risk.

Venture funding drops by 70% as capital tightens, while average SaaS revenue multiples fall from 17x in 2021 to around 6x — the lowest level in a decade.

High-growth companies once buoyed by cheap credit and future potential see valuations cut by as much as 75%.

In response, VC funds press their portfolios for leaner, more capital-efficient business models.

The SaaSacre of 2022

Public cloud stocks have taken a drubbing in 2022. But not all cloud companies have been impacted equally. Why is this happening, how severe is the compression, & where are the areas of resilience?

The Three Valuation Lows in SaaS: 2013, 2016 … and 2022-2024

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The exit market for SaaS dried up in the second half of 2022, which saw the lowest exit activity since 2016.

2023 — Investor Logic

With payroll consuming ~75% of startup operating costs, human labour becomes an obvious target for efficiency. Nowhere is this more visible than in tech, where workers are among the highest-paid in the world, and a perceived culture of frivolous perks came to symbolise the excesses of venture-backed growth.

The 'correction' begins in late 2022, when Elon Musk’s takeover of Twitter leads to a near 80% workforce reduction. Boards and investors signal that the era of unchecked headcount expansion is over. By the end of 2023, hundreds of thousands of tech workers across the industry have been laid off, with Meta, Amazon, Google, and Microsoft all cutting deeply.

In this environment, AI emerges as the perfect narrative: automate more, hire fewer, scale faster.

Investors bet more on startups that promise automation-driven margins, even when the business cases are thin. Founders quickly realise that without an 'AI angle', funding becomes harder if not impossible to secure.

AI shifts from exciting differentiator to prerequisite for fundraising.

2024 – A Digital Workforce

By 2024, the narrative shifts from 'AI as a feature' to 'AI as labour substitute'.

OpenAI rolls out ChatGPT Enterprise with premium tiers — including $200 per-user plans — positioning LLMs directly against the cost of hiring knowledge workers.

For the first time, enterprises can weigh a digital employee at SaaS prices against a human salary. With average entry-level tech salaries in the US between $80,000–$120,000, the comparison to a $2,400 annual licence is sobering.

The framing lands as companies continue cost-cutting after the mass layoffs of 2023, and some begin testing the logic in the open.

Klarna report AI assistants doing more and more work in customer service and marketing, the equivalent workload many hundreds of workers, saving an estimated ~$50 million annually.

Hiring freezes in many roles reinforce the message: AI is being treated not just as a productivity tool, but as a headcount reduction strategy.

This becomes the first large-scale price test of human productivity in dollar terms. Investors and executives alike watch closely for signals that digital workers can shift operating margins.

Adoption remains experimental, but the signal is clear: AI is no longer just another product category — it has become a benchmark against which the value of human labour is measured.

Klarna using GenAI to cut marketing costs by $10 mln annually

Fintech firm Klarna, one of the early adopters of generative AI (GenAI), said on Tuesday it is using AI for purposes such as running marketing campaigns and generating images, saving about $10 million in costs annually.

AI replaces 700 customer service reps at fintech startup Klarna - Tech Startups

In a clear sign of things to come with the rise of AI, Klarna, Europe’s most valuable fintech startup, just released a post highlighting the success of its OpenAI-powered customer service chatbot. According to Klarna, the chatbot managed an impressive 2.3 million conversations in the last month alone. That's not all. The company claims that

2025: A reckoning

Today in 2025, AI capabilities have become almost ubiquitous in product launches. Equally, existing products and platforms scramble to retrofit AI features to stay competitive.

And yet Gartner places generative AI squarely in the 'trough of disillusionment' where initial excitement gives way to harsh realities as implementations fail to meet overhyped expectations.

Figure: Gartner Hype Cycle for Artificial Intelligence 2025

The disconnect between hype and reality is stark to say the least. Companies are discovering the hard way that AI is not the magic bullet they were sold.

A recent report from McKinsey & Company found that 71% of companies reported using AI, and more than 80% reported no 'tangible impact' on earnings. A recent MIT study reveals that 95% of generative AI pilots failed to impact profits. Klarna's ambitious AI-driven customer service overhaul exemplifies this downturn: a disastrous decline in service quality forced the company to rehire human workers and pivot their strategy at great cost.

Startup economics remain relatively unchanged: failure rates persist at a rocksteady 70–90%, demonstrating that AI adoption has yet to alter the fundamental dynamics of venture success.

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What comes next?

The AI boom (pronounced 'bubble') was never just about intelligence, it was about economics: capturing and retaining maximum value with minimum reward for workers.

In a time of rising costs and scarcer capital, AI became the perfect story for VCs and founders alike: do more with less, automate the expensive parts, scale faster than the competition and achieve the holy grail of VC-backed software: drive marginal costs to zero.

But narratives can't change market realities: building sustainable businesses still requires people, time, patient capital and well-built products that solve real problems that customers will pay for. The current wave of AI enthusiasm is just another bubble primed for correction.

The question isn't whether AI will transform business. It's whether this generation of 'AI-first' AKA 'people-second' companies can survive long enough to find sustainable business models before the hype and capital runs out.