Mobile internet technology has transformed global communication and ushered in a new connected era. Location-based services like Uber, Gojek, and Grab have revolutionized transportation and food delivery, and user-generated content platforms like TikTok have redefined media consumption. With the advent of 5G connectivity, Superapps are further integrating multiple online functions into a single, easy-to-use platform.
However, while the mobile Internet has greatly solved connectivity problems, it has not adequately addressed productivity. The ability to process, make sense of, and generate insightful output from the vast amounts of information available remains largely down to human responsibility. This is where generative AI, or “Gen AI” comes into play.
Emergence and Possibility of Generative AI
Gen AI employs machine learning algorithms to learn from vast data pools and generate new content based on this learning. These capabilities enable Gen AI to not only analyze, but also create, innovate, and support decision-making, potentially revolutionizing productivity.
Two key aspects that Gen AI will change are cognitive capabilities (developing intelligence) and the cost of productivity (how intelligence is produced).
Currently, AI’s cognitive ability is about 30-50% of human ability. With Gen AI, this could reach the 10% percentile. In the future, the possibility of developing superintelligence that violates one percent of human capabilities becomes more obvious.
From a productivity cost standpoint, creating intelligence today requires significant resources: food, about 12 years of education, and work experience. The move to Gen AI has radically changed this equation, requiring primarily power, GPUs and data, all of which are increasingly democratized. This change makes productivity exponentially cheaper.
However, not all Gen AI is equal. With different architectures, training, and input data quality, different AI systems exhibit a wide range of capabilities and potential output qualities. The key understanding here is that an AI is only as good as its training data, implying that unbiased and accurate data is needed to prevent flawed output.
Gen AI and the domino effect of “market creation”
The surge in launch and usage of Gen AI apps for ChatGPT and other media formats has set off a domino effect of “market creation”, indicating potential demand for similar AI applications. Releases of the OpenAI API and other of his LLMs have made the development of such intelligence more cost-effective from an app builder’s perspective.
This led to an explosion of “copycat” apps, many of which failed due to lack of security and reliability. This is fueling demand for finer-tuned, secure, reliable, and accurate narrow AI, especially for business use cases and unique data-driven models.
In response to this growing demand, the current focus is on developing the data value chain. We are seeing a shift in budget allocation as more funds are allocated to investments in AI strategies and data value chain solutions.
Next week, we’ll discuss specifically what data value chain developments can do to drive generative AI use cases and, at scale, shape how the future enterprise (not just generative AI enterprises) will be built. Please look forward to it.
Despite its immense potential, Gen AI is not a panacea for all productivity inefficiencies. While Gen AI can automate a wide variety of tasks, it is important to remember that it cannot completely replace human creativity, empathy, and critical thinking, or “humanity.” As we embed Gen AI more deeply into our lives, we will always keep in mind its limitations and potential pitfalls, and will continue to strive to harness the benefits of AI and preserve the irreplaceable value of human ingenuity. You have to strike a balance between
Paulo Joquinho Writer and content producer for a technology company, and book co-author Navigating ASEAN Innovation. He is currently the editor of Insignia Business Review, the official publication of Insignia Ventures Partners and a senior content strategist at a venture capital firm that joined right out of college. During his college years, he had multiple job opportunities in content and marketing for Asian startups. These include his internship as an associate at G3 Partners, a Seoul-based marketing agency for tech startups, co-working his space and business, his tech community at ASPACE Philippines, his community This includes running his engagements, working as an intern at his FlySpaces workspace and his marketplace. He graduated in 2019 with a Bachelor of Arts degree in Industrial Engineering from the University of Manila, Ateneo de his.
This article is the first in a series on AI and was originally published in Insignia Business Review.
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