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Tips for AI startups to scale the business for the US market

According to Precedence Research, the AI market may exceed $538 billion in 2023, with a compound annual growth rate (CAGR) of 19% from 2023 to 2032. The U.S. artificial intelligence market size accounted for $103.7 billion in 2022 and is estimated to reach around $594 billion by 2032.

With the opportunities that this and other market forecasts may promise, there is a tendency for the excitement to wane, as the data for Q3 shows. The number of generative AI deals fell to 101 rounds in Q3, a 29% decline from Q2, according to Pitchbook analysis.

While venture investments are losing enthusiasm in general, generative AI remains an attractive field. For example, in August, Israel-based AI startup AI21 Labs raised $155 million in a Series C funding round, which saw participation from technology heavyweights Alphabet’s Google and Nvidia. Another example is a Dutch AI startup for healthcare interactions, Corti, which completed a $60M Series B round in September.

For AI startups, the US market has a lot to offer, from talent pool to capital access. For example, 26% of all capital invested in American startups this year has gone to artificial intelligence-related companies, according to Crunchbase calculations. The trend is likely to continue, say analysts, as more and more companies use AI as a backbone for their technological solutions.

Many investors agree that the American venture capital infrastructure creates blue-sky possibilities that are enticing for many companies. In this article, we dwell upon a few distinctive features of the American market.

Access to capital

One of the most distinguished characteristics of the American startup market is easy access to venture capital at every stage of the company’s development. According to Crunchbase data, the average Series A was up to $22.7 million in 2021, while Series B reached $50.2 million. To compare, the average Series A in Europe was $14.2 million and the average Series B there was below $48 million.

Recently, several corporations announced their commitment to invest in AI startups. For example, IBM is launching a $500 million enterprise AI venture fund to invest in startups across stages. Once a startup, Dropbox launched a $50 million venture fund focused on AI startups earlier this year.

“US funds invest in case your company succeeds, whereas in Europe, they invest because your company succeeded,” writes the co-founder of Front, Mathilde Collin. “I was shocked to discover that VCs in the US are chasing founders as much as founders are chasing them,” she adds. According to Collin, even meeting with an investor in Europe seems more challenging than in the USA. The decision time is shorter in the US, too during the meetings with the investors.

Significant talent pool

As startups choose to scale, the headcount starts to grow fast. The American artificial intelligence market offers a range of talented professionals like no other does. One-third of all talent is concentrated in the top three primary markets of the US: San Francisco Bay Area, New York and Seattle. According to CBRE, the San Francisco Bay Area has over 630% more talent than the secondary market average, i.e., Dallas, Chicago and Atlanta.

The migration processes further strengthen this dominant position of the US market as a primary source of the talent pool. According to Anu Bradford of the European Legal Studies Center at Columbia Law School, “While 60% of the world’s top AI researchers work in US institutions, 29% of those individuals had received their undergraduate degree in China, 20% in the US and 18% in Europe. This suggests that the world’s leading AI researchers are migrating to the US to study and work, rather than the other way around”.

It is no surprise that a recent study also shows US candidates are aware of their market value, says the Index Ventures research, and they are “generally more confident and better at selling themselves.”

Risk aversion

When it comes to the attitude to risk taking, there is nobody who knows it better than American startup founders. While the famous mantra in Silicon Valley was “fail often, fail fast,” failing was considered shameful in Europe and other parts of the world. “Data shows that founders have a better chance of getting it “right” the second or third time — if they have the guts to come back fighting,” writes Niklas Zennström, co-founder of Skype, in the opinion piece in Financial Times.

Lack of risk aversion implies that venture investors are more willing to make riskier investments, making raising funds more accessible. A lack of “risk culture” is one of the reasons why European startups have lower success rates than their Indian and American counterparts, argues McKinsey & Company in their research. But with a 14% success rate (as opposed to 20% in the US and 19% in India), “European companies don’t fail more often than US companies”. They do not advance to the next round of funding or push too hard and keep the conservative approach to growth and development.

Venture Ecosystem

When it comes to scaling startups, no one can do it alone. To thrive a startup needs an ecosystem that includes accelerators and incubators, venture capitalists and a community of like-minded founders. According to the Global Startup Ecosystem ranking compiled by Startup Genome, North America remains the leading region globally regarding the number of tech startup ecosystems. The ranking looks into the performance of the startups, funding opportunities, market reach, connectedness, talent and experience, and knowledge base (i.e., research and patent activity). For example, the leader of the ranking, Silicon Valley was where most notable AI startups appeared, including OpenAI, which is undoubtedly Silicon Valley’s most visible AI success story, or the early demo of ChatGPT.

Scaling European startups on the American market requires extensive research and hiring an expert or consultancy with a proven track record and expertise. From access to funds and pool talent to understanding the geography of the market and legal issues — these are the characteristics that should be considered.