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.
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.
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.
Credit: 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.
What comes next?
The AI boom (pronounced 'bubble') was never just about intelligence — it was about economics: capturing and retaining maximum value with minimum human reward.
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.