One day, we didn’t have ChatGPT, then the next day, we did. It felt like AI went from being nowhere to everywhere in a nanosecond. Since then, experts have been shocked by the pace of innovation as more and more breakthroughs continue to disrupt industries. These scientific breakthroughs can end up feeling like stock shocks every time they happen. As AI shortens the timeline from breakthrough to commercialization, investors may want to rethink how quickly innovation can reshape sectors, and how portfolios are positioned to capture those shifts.

After the release of ChatGPT in 2022, generative AI reached 53% population adoption within three years — faster than the PC or the internet.¹ Some researchers estimate AI capabilities are doubling approximately every seven months.² To illustrate the potential impact of unimaginable scientific breakthroughs in the near future, we can look back to see how far we’ve come in such a short amount of time.

Predicting Proteins

DeepMind was founded in 2010 to build AI systems that could some day outperform humans. In 2016, its AI famously beat one of the world’s greatest players at Go.³ The company immediately pivoted to the world of biology, trying to predict protein structures, something one of DeepMind’s top scientists had been working on since 1994. Just two years after the company’s pivot, their AI beat the world standard.³ Two years after that, the company’s AlphaFold2 model was able to predict protein structures with 90% accuracy,³ thereby officially solving a problem biologists had been toiling over for 50 years.⁴

Within a few months, DeepMind had predicted all 20,000 proteins in the human body. It predicted 200 million protein structures two years later, a feat that would have taken hundreds of millions of years to achieve without their AI.⁴ The technology has enabled drug companies to break records in both the speed and cost of drug development. In the five years since its release, AlphaFold2 has been used by more than 3 million researchers in 190 countries.⁴

Discovering Drugs

In 2019, a biotech company in Boston wondered if they could use an AI transformer model in the drug discovery process. 18 months later, they had a drug candidate that would have taken over four years to discover without AI. Today, less than 7 years after the initial idea, the company now has over 40 AI-developed drugs in the pipeline. The methodology has since garnered widespread adoption and commercialization within drug discovery and is estimated to grow from $3.8 billion in 2025 to $15.2 billion in 2030.⁵

Quickening Quantum

In the early days, quantum physicists never thought quantum computing would become a practical tool.⁶ Then Google built the first quantum computer in 2019 and the field has been accelerating ever since.⁶ 

The idea for the topological qubit needed to make the theory of quantum computing into reality was born in 1997. Microsoft has been working on it since 2000 and, in early 2025, finally figured it out. That breakthrough came just two months after Google created a quantum chip that performed a calculation in five minutes that the best supercomputers need 10 septillion years to complete.⁷

The Harvard Quantum Initiative also made a recent breakthrough at the end of 2025 in fault tolerance, or the ability of a quantum computer to reduce errors.⁸ This breakthrough has accelerated the timeline of quantum computing up by as much as 10 years. Many researchers in the field say quantum computing is way ahead of where they thought it would be less than a decade ago.⁸

Cracking the Code

Just over a year ago, when “vibecoding” entered our lexicon, industry experts thought the technology wouldn’t replace workers any time soon. Yet earlier this year, Claude Code forced them to eat their words. Seemingly overnight, many experts thought Claude Code would be the “death of SaaS” everywhere — within days of the non-coder-friendly Cowork plugin going live, software stocks collectively lost over $300 billion in market cap.⁹

Investing in the Unimaginable?

Back in the 1940s, $30 billion (in 2026 dollars) was invested in the Manhattan Project to develop atomic weapons.¹⁰ $298 billion was invested in the Apollo Program to land humans on the moon.¹⁰ From 2013 to 2024, $1.6 trillion was invested in AI.¹⁰ Global investment in AI is projected to reach $2.5 trillion in 2026 alone.¹¹ 

McKinsey estimates that $5.2 trillion will need to be invested in data centers by 2030 to meet demand. But that’s the imaginable demand of today, not the unimaginable demand of future scientific breakthroughs. The question for investors is this: Are you positioned for the unimaginable?

TrueShares Technology, AI & Deep Learning ETF (LRNZ) seeks to provide thematic exposure to a concentrated portfolio of 20-30 technology companies that are significantly involved in the application of advanced levels of AI within their businesses. We believe these companies possess innovative AI and Deep Learning solutions that represent a distinct competitive advantage in a particular industry. LRNZ is led by a portfolio manager with a deep understanding of the technology sector and an ability to conduct extensive qualitative fundamental research.

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Investments in companies involved in artificial intelligence and other emerging technologies may experience significant volatility, and technological leadership can change rapidly. There is no assurance that companies benefiting from current trends will continue to do so. Investing involves risk, including possible loss of principal. Technology and AI-focused companies may experience greater volatility than the broader market.

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1. https://hai.stanford.edu/ai-index/2026-ai-index-report/economy 
2. https://news.sky.com/story/ai-is-developing-so-fast-it-is-becoming-hard-to-measure-experts-say-13512068  
3. https://www.businessinsider.com/deepmind-alphafold-ai-origin-story-impact-on-biotech-2023-2 
4. https://deepmind.google/blog/alphafold-five-years-of-impact/ 
5. https://www.economist.com/science-and-technology/2026/01/05/an-ai-revolution-in-drugmaking-is-under-way 
6. https://www.nytimes.com/2024/12/09/technology/google-quantum-computing.html 
7. https://www.nytimes.com/2025/02/19/technology/microsoft-quantum-computing-topological-qubit.html
8. https://thequantuminsider.com/2026/05/04/harvard-researchers-quantum-computing-advancing-faster-than-expected/ 
9. https://www.youtube.com/watch?v=JGm_v3YFMes&t=93s 
10. https://www.reuters.com/graphics/USA-ECONOMY/AI-INVESTMENT/gkvlqbgxkpb/
11. https://www.aljazeera.com/news/2026/2/19/visualising-ai-spending-how-does-it-compare-with-historys-mega-projects 

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*Vibecoding: The shift from writing code line-by-line to directing AI assistants to write the code instead.