Artificial Intelligence (AI) startups are some of the most susceptible to hype and magical thinking. As a result, the reality of what modern AI can actually achieve becomes convoluted.
It’s an undeniable fact that the pandemic has changed a lot about everyday life. The real question now, however, is how lasting will some of these changes be?
Everyone is up in arms about the April jobs report. The US added 266,000 jobs during the month, but it was a fraction of what was expected and did nothing to change the unemployment rate.
At the same time, we had a viral TikTok video of a McDonald’s in Texas apologizing for being short staffed because “No one wants to work anymore.”
Conservatives are quite loudly implicating the cause of this (and other similar anecdotes from small businesses)are caused by supplements to unemployment benefits and other pandemic aid discouraging work.
So which is it? …well, probably both.
The sheer scale of the healthcare system is mesmerizing. In the US alone, $11,072 per capita spending (in 2019) and at around 320 million in population means that we’re looking at $3 trillion in annual spending.
And even though the US is the largest spender, spending per capita has been rising across the entire world, driving healthcare to make up a larger and larger share of total global GDP.
But, as experienced founders know, the sheer size of a market alone doesn’t matter that much. What matters is how attainable it is for your specific business and business model.
For founders in the healthcare sector, this means avoiding the number one faux pax; forgetting the “business” part of the healthcare system.
At Creative Ventures, we often hear from our peers, “You seem really fearless about technical risk.” Ironically, this could not be further from the truth. We explicitly do not invest in technologies at Creative Ventures (even though we are a deep tech fund).
“What’s your data moat?” It’s one of the most common questions to AI-related startups. It’s also an increasingly irrelevant question. Thanks to better representations and algorithms it’s now possible to do a lot more with a lot less data. Outside of a limited number of fairly specialized applications, data moats are becoming less and less secure.