AI is the buzzword of the moment. New and existing companies across industries, it would seem, are joining the race to incorporate AI into their business propositions. Or at least, they’re claiming to. This frenzy to hop on the AI bandwagon has introduced a new concern to the market called techwashing¹, or AI-washing. This marketing practice of overinflating or misrepresenting the capabilities of AI has become more popular recently. One study² found that 40% of European companies that self-identify as “AI startups” actually used little to no AI. 

AI-washing is attractive for startups because the use of AI attracts venture capital investors; AI startups raised more than $50 billion¹ last year. AI companies also attract investors looking to score a large, short-term payout in a nascent industry. But techwashing, especially of a technology as complex and advanced as AI, makes it difficult for consumers and investors to distinguish between legitimate AI and marketing ploys. That’s why we believe a better investment strategy for those interested in the AI market is to invest in the companies that make AI possible instead of betting on companies claiming to be “powered by AI.”

Many companies falsely claiming to use AI³ are actually referring merely to the building blocks⁴ of AI such as algorithms and machine learning. In simplified terms, AI is truly AI³ if it can handle unstructured data, but AI nevertheless relies on data in some form to operate. Companies genuinely using AI will employ data analysts, data storage providers, data security systems. Data usage of the sophistication and scale needed for AI also likely involves cloud computing, which is a commonly outsourced service. For companies to successfully run AI, they need other outsourced products like effective platforms with significant computing power and connectivity made possible by sufficient hardware and software systems. 

Instead of investing in companies that claim to be AI-powered, it may prove more fruitful to invest in the technologies that make AI possible. Many such companies are included in the TrueShares Technology, AI & Deep Learning ETF (LRNZ). These are companies focused on computing power, data storage and security, algorithms, and platforms – all of the building blocks necessary for AI to succeed.

For data, Snowflake is currently the only publicly-traded company that can provide the quality and quantity of data that AI (and non-AI) companies need to power their algorithms. With their cloud computing services, companies are able to organize high-quality, relevant data and exchange data with others.

Similarly, Nvidia provides the high-performance computing (HPC) that is essential in allowing for data processing and the running of complex models at high speeds. Their semiconductors and other hardware and software offerings are embedded in a variety of industries from gaming and automotive to robotics, finance, and biotech. 

Shrodinger is yet another example of an LRNZ holding that provides a widely applicable platform for companies looking to incorporate AI in their business propositions. Shrodinger developed a predictive chemical simulation software that companies in the pharmaceutical, biotechnology, and materials design sectors are leveraging to make discoveries more rapidly and at lower costs than they were able to before. 

An investment in Snowflake, Nvidia, Shrodinger, and the other 20 to 30 LRNZ ETF holdings could serve as an investment in the backbone of AI, a rapidly growing industry still in relatively uncharted territory. We believe investing in AI’s building blocks could provide a more reliable opportunity for secular growth compared to investing in the AI-powered (or AI-washing) companies themselves.

1. https://www.cio.com/article/475061/how-the-cio-can-be-a-bulwark-against-misleading-ai-claims.html
2. https://www.stateofai2019.com/chapter-7-europes-ai-startups/
3. https://cybernews.com/tech/ai-washing-the-new-greenwashing/
4. https://www.gov.uk/government/publications/building-blocks-for-ai-and-autonomy-a-biscuit-book/core-elements-of-ai 

*Substantial industry growth does not guarantee positive investment returns and may lead to significant volatility. 

The TrueShares AI & Deep Learning ETF (AI ETF) is also subject to the following risks: Artificial Intelligence, Machine Learning and Deep Learning Investment Risk – the extent of such technologies’ versatility has not yet been fully explored. There is no guarantee that these products or services will be successful and the securities of such companies, especially smaller, start-up companies, are typically more volatile than those of companies that do not rely heavily on technology. Foreign Securities Risk -The Fund invests in foreign securities which involves certain risks such as currency volatility, political and social instability and reduced market liquidity. Growth Investing Risk – The risk of investing in growth stocks that may be more volatile than other stocks because they are more sensitive to investor perceptions of the issuing company’s growth potential. IPO Risk – The Fund may invest in companies that have recently completed an initial public offering that are unseasoned equities lacking a trading history, a track record of reporting to investors, and widely available research coverage. IPOs are thus often subject to extreme price volatility and speculative trading. New Issuer Risk – Investments in shares of new issuers involve greater risks than investments in shares of companies that have traded publicly on an exchange for extended periods of time. Non-Diversification Risk – The Fund is non-diversified which means it may be invested in a limited number of issuers and susceptible to any economic, political and regulatory events than a more diversified fund.