WHERE TO LOOK FOR SAAS GROWTH POTENTIAL

#1?WHERE TO LOOK FOR SAAS GROWTH POTENTIAL?

Unlocking the full growth potential of your SaaS business requires a detailed exploration of key areas that often hold the clues to success. This article delves deeper into what makes a SaaS company thrive. It explores the top 10 questions every SaaS founder should ask. These questions are vital for identifying untapped growth opportunities. Through this exploration, founders can better position their companies for sustained success.?

#2?WHY CEOS ARE SO OPTIMISTIC ABOUT THE ECONOMY?

It seems like there’s been a massive waiting game with the economy for the past six months. Many investors, analysts and companies have been sure that inflation is slowing down and interest rate cuts are around the corner. The optimism has sent markets and company valuations sky high. Actual data has been deflating that optimism and making America realize that interest rate cuts may truly be farther down the road. ?

#3?AI DATACENTER ENERGY DILEMMA

The boom in demand for AI clusters has led to a surge in focus on datacenter capacity, with extreme stress on electricity grids, generation capacity. The AI buildouts are heavily limited by the lack of datacenter capacity, especially with regard to training as GPUs need to be generally co-located for high-speed chip to chip networking. The deployment of inference is heavily limited by aggregate capacity in various regions as well as better models coming to market.

#4?SO ARE WE ALL WORKING FROM HOME NOW?

In the corporate tug-of-war over remote work, CEOs like Andy Jassy and Elon Musk are the old-school gym teachers insisting everyone get back on the field, despite the bleachers being perfectly fine. They argue that remote work is akin to slacking off, yet studies and employee sentiments suggest otherwise, highlighting that flexibility might just be the secret sauce to productivity and satisfaction.

#5?CONSIDERATIONS FOR INVESTING IN AI STARTUPS

The early days post-ChatGPT were marred by uncertainty—where to invest was as clear as mud. VCs, typically guided by data-driven theses, found themselves in uncharted waters. Without a compass, they sought refuge in the foundational tools of AI: developers of LLMs, their ancillaries like vector databases, open-source LLM repositories and the hardware backbone of AI chips. This scramble for “shovels and picks” in the AI gold rush was just the beginning.

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