The article discusses a survey of investors and experts in the field of machine learning (ML) and artificial intelligence (AI). The survey highlights several key points:
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Demand for Compute Capacity: The current surge in demand for ML has caused a phase shift in the demand for compute capacity. However, concerns about technical barriers such as GPU capacity are expected to be resolved with the velocity of chip innovation and entry of new market players.
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Customer Readiness: Despite macroeconomic challenges, organizations recognize the immense power of AI and ML to supercharge their businesses. The commercial interest and customer-readiness for ML solutions has remained resilient.
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Execution Side Barriers: The most significant barriers in the way of success stem from the execution side, particularly around founders bringing academic theories or research into production as scalable and practical offerings.
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Investor Advice: To grow their business and attract investors, ML startup founders should be concise with their message around technical and product differentiation, communicate their business value proposition in a straightforward manner, and ensure potential partners are not just inspired by their vision but excited to deploy their ML innovation as soon as possible.
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Advancements: The survey suggests that many startups have not yet passed the threshold for technological or productization de-risking and will likely require significant specialized R&D resources and talent to execute on and scale their vision.