We are on the slope of Gartner’s hype cycle, a period of disillusionment that often follows the peak of inflated expectations in generative AI. Market players who bet on AI may find themselves sliding down the slope. However, there is a silver lining for investors and startups focused on impactful and profitable AI applications. By harnessing and deploying quality data, the lifeblood of today’s knowledge economy, they can stay on the slope and thrive. Those who continue playing with AI toys without considering profitability will likely be at the bottom.
Investors are waiting for a second wave of AI startups focused on specific tasks and original solutions not being topped on foundation models of tech behemoths. Many startups that raised funds in a couple of previous years will fail because they cannot productize AI without first narrowly defining the problem and optimizing solutions. About half of AI startups built their products using OpenAI/GPT 4. While they accelerate AI deployment in pitch decks and fundraising strategies, using this open-source software is not unique. The uniqueness is in building proprietary training data sets and efficient business models.
Newcomers to the AI startup scene promise profound innovations. They will be poised to revolutionize the market with new or modified business models that offer significant advancements or unique value propositions. The most astute startup founders will avoid direct competition with established incumbents or offer solutions that mirror those already in the market. Instead, they will leverage the disruptive power of AI to create profitable applications that set them apart.
How can startups and VCs stay on the slope and not slide down?
By employing innovative business models that bring significant advancements in profitability to differentiate themselves in the market, they can stop thinking that AI is a profitable innovation. They can also eliminate unnecessary mystification of AI—AI is a set of algorithms. Both people and algorithms have their strengths and weaknesses. By combining heuristics, intuition, and generative AI in a hybrid data-driven approach, startups can cut costs and increase revenues. It is an old and proven path to profitability.
On the startup side: Despite the initial challenges of lower gross margins and ongoing costs, AI startups promise significant returns on investment. While their economic profile may start low, AI technology’s continuous advancement and maturation can lead to substantial growth and long-term profitability. Investors seeking profitable and impactful applications in the VC industry should not overlook this potential for high returns. One more advantage to employing AI: startups have a chance to provide reliable data on their profitability.
On the VC side: the current decision-making process is manual, inefficient, non-inclusive, subjective, and biased. Early-stage investors have to make funding decisions using substantially incomplete data about startups. Processing significant volumes of unstructured and often unreliable data manually, investors search through irrelevant data sets and cannot recognize some good business opportunities, losing them. Using AI for sourcing and screening will provide VCs with information-processing methods and insight-generating tools.
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Read #PROFITomix for more information on how generative AI can help build and fund #profitable #ventures.
