Tech has always been an industry where fortunes can change overnight. Billionaires are made, not by sound business practices, but by the strength of their ideas and the stories they tell. Silicon Valley has become a place where showmanship is valued over competence, and innovation is often more about perception than reality.
Venture capitalists (VCs) and founders have a symbiotic relationship. VCs need radical ideas to invest in, while founders want to start businesses with someone else’s money. Unprofitable companies are kept afloat with injections of capital, inflated valuations, and media hype, all with the goal of going public or being acquired. This ecosystem is designed to keep the talent pipeline flowing, with inspirational speeches and generous salaries ensuring a steady stream of new graduates into the sector.
Founders rely on VCs for capital, talent, and operational mentorship, while VCs depend on founders to educate them on technology. However, many VCs are former Wall Street bankers or celebrities with little tech experience. This often results in poor decision-making, with investments made in minutes rather than through thorough analysis. The pressure to report publicly leads to financial gamesmanship, often at the expense of consumers.
The tech sector is a place where winning big once is enough to make a career. This creates a near-infinite pool of aspiring founders and VCs, quick to forgive and move on to the next big thing. When investors turn bearish, the sector quickly pivots to a new growth story. In 2022, when the market soured on big data and SaaS (Software as a Service), AI (Artificial Intelligence) became the new frontier.
Big data was once touted as revolutionary, promising deep insights and innovation from massive amounts of data. It was supposed to transform industries, from predicting crime to detecting cancer early. However, despite the hype, the vast majority of consumer startups and SaaS companies continued to bleed money. The promises of big data never materialised, and the market began to question its value.
Enter AI. In less than two years, AI has become the new darling of Silicon Valley. Every tech company is now an AI company, and every product has an AI component. VCs are pouring money into AI startups, and everyone from Congress to the White House has been convinced of its potential. The public believes that AI will displace artists, animators, translators, and programmers, despite the lack of concrete evidence.
This pattern of hype is not new. Before AI, there was crypto, blockchain, virtual reality, augmented reality, big data, IoT (Internet of Things), and wearables. All were supposed to be revolutionary, but none lived up to the hype. The cycle repeats with AI, driven by the same market dynamics that pushed companies and individuals into previous trends.
The late 2000s saw genuine innovation with the advent of smartphones and tablets, creating new markets and opportunities. Startups like Groupon, Pandora, and Yelp emerged, leveraging data to drive business. However, as the market matured, these consumer startups struggled to maintain growth and profitability.
By the early 2010s, data had become the new gold. The narrative was that data could unlock business value and innovation in any industry. Startups and established companies alike jumped on the bandwagon, investing heavily in data-driven strategies. However, despite the promises, many of these companies continued to struggle financially.
The mid-2010s saw the rise of big data, with companies claiming that more data and sophisticated tools were needed to unlock its potential. Consumer startups embraced this narrative, but the results were disappointing. Many of these companies continued to lose money, and the promise of big data remained unfulfilled.
Despite the lack of results, the Fortune 500 also jumped on the big data bandwagon, investing billions in technical capabilities. This demand for talent and tools spawned a stream of enterprise startups, providing ready-to-go solutions for big data. Companies like Datadog, Splunk, and Snowflake thrived, selling the tools needed to manage and analyse data.
However, the real winners were the cloud providers—AWS, Google Cloud, and Azure—who saw massive growth during this period. The demand for cloud services skyrocketed as companies adopted big data technologies. Software engineers, in turn, became highly sought after, driving up salaries and shifting the power dynamics within companies.
Today, the same dynamics are playing out with AI. Thousands of new consumer startups are emerging, claiming to leverage AI for business value. Enterprise vendors are selling AI tools, and the Fortune 500 are once again running scared. Cloud providers and chipmakers are reaping the benefits, while engineers jump into the latest open-source projects to pad their resumes.
The promises of AI are even more confusing than those of big data. We’re told that data is too complex for humans to analyse, and we must rely on AI models to find hidden insights. However, the lack of concrete results from big data should make us sceptical of AI’s promises.
In the end, the winners from big data were the founders, executives, and VCs who cashed out at IPO. The losers were the public, investors, and employees. As the hype around AI continues, we must ask ourselves: who will benefit, and what is the true value of innovation without critical thinking, art without authenticity, and creation without originality?