2023 will go down in history as the start of a new era in the way we view and use AI. We compiled 23 Things We Learned About AI in Southeast Asia this 2023.

Image generated by Midjourney, with the prompt: futuristic robot and human reading a history book about humanity in a library for robots, realistic photograph

23 Things We Learned About AI in Southeast Asia this 2023

2023 will go down in history as the start of a new era in the way we view and use AI. We compiled 23 Things We Learned About AI in Southeast Asia this 2023.

2023 will be written in the history books as the year the world entered a new era in the way we view and use AI.

While AI adoption is still nascent in many markets and industries, it has come to the forefront of social interactions and business transformation.

AI’s step change has also played a huge part in our content this past year, from thought pieces on the potential of Gen AI across industries to podcasts with CEOs on the reality of AI adoption in Southeast Asia. See all of this content in our AI Notes page.

We compiled 23 learnings from this exploration below.

23 Things We Learned About AI in Southeast Asia this 2023

1. Not all Gen AI is equal. The Gen AI stack is still quite fragmented, as we write in this article. Even for content creation, you have multiple apps with different purposes all charging separate rates. Those that do serve similar purposes will then vary in which aspects their models perform better. Just as we have seen in other areas of digital transformation, this fragmentation will eventually pave the way for solutions with more end-to-end functionality.

2. Gen AI’s impact on the data value chain impacts all companies. Beyond the use cases for large language models and other generative models across industries, the momentum sparked by gen AI for enterprise sets up competitive advantages for companies that are able to better leverage and fine tune data sets. This is regardless if they use gen AI models or discriminative AI models, as we write in this article.

3. There’s still a massive gap for talent to meet commercialisation needs. In Southeast Asia, AI (not just gen AI) has the potential to add US$1T to the region’s GDP by 2030. But for this to materialize, there needs to be a greater pool of talent. AI talent can span an entire stack of skills, from prompt engineering to machine learning. And this talent pool also needs to be actively building for monetizable use cases, as we write in this article.

4. You can’t just plug in Gen AI into anything, at least not at first. Given the current constraints of models and products, Gen AI may not always the best, or at least the first, step in addressing certain issues or increasing certain productivities, as we write in this article. Process automation may first be necessary or already suffice to improve cost efficiencies on data workflows for example, and once that’s in place can the potential of LLMs be unlocked.

5. Investing in context development is key to ensuring LLM integrations are efficient for specific use cases and narrow scopes, like a simple website chatbot, as we write here.

6. Compared to say social media or the sharing economy, regulation has been quick to accelerate or form in response to generative AI, as we write in this article. But regulation remains ambiguous with respect to data privacy and tracing liabilities due to AI’s “memory” black boxes.

Read more about #1 to 6 here.

7. Because of autonomous AI agents’ abilities to execute extremely complex tasks and problem solve, this could unlock many use cases previously unrealizable by LLMs alone (e.g., one-to-one tuition in edtech), as we write here.

8. AgentOps is instrumental to the foundation of Autonomous AI Agents. These are often components (models, databases, and tools/plugins) that combine to form ‘frameworks’ templatized and distributed through marketplaces such as HuggingFace or Github. AgentOps hence primarily focuses on the productization of the AI engineer tech stack, as we write here.

9. OpenAI has catalyzed “gold mine” formation for AI startups, but accuracy remains a key technological investment risk.

10. Increased ability for companies to learn with context (i.e., have data flywheels) sets up competitive dimension along which companies have access to the best infrastructure, as seen in industries ranging from automotive with Carro, to property with Pinhome. This ‘data advantage’ is key for data-driven product-led growth and Distribution.

11. When it comes to sales growth, gen AI can bridge the gap between scale and customization with self-serve products.

12. GPU component of AI startups adds additional layer of due diligence and portfolio services for VCs.

Read more about #9 to 12 here.

13. SEA faces a higher bar for model superiority. Companies like WIZ.AI and bluesheets are closing this “model chasm” by fine-tuning their AI models for enterprise-level tasks, which could be a catalyst for mass commercialization.

14. AI transformation is data transformation. This means that the future of AI transformation is not so much providing models but enabling companies to build their own with internal data.

15. AI has a role to play in data governance and fraud detection, with fintechs racing to build proprietary engines to de-risk products like loans.

16. When it comes to wealth management, AI speeds up the gaps of market asymmetry at scale, but market opacity still requires human presence to supplement AI output, especially when it comes to decision making.

17. Existing platforms that have already gained user trust in personal finance and investment are the most likely to pioneer AI-driven finance super-apps.

Read more about #14 to 17 here.

18. Business readiness and data readiness smooth over AI adoption, but expanding from a more nascent market can be valuable long-term.

19. Clear land and expand strategy is necessary for AI transformation in “early adopter” markets.

Numbers 18 to 19 come from this podcast: 

20. As a consumer platform, it’s not enough to just have the right data to build AI applications. There needs to be a way for the market to “agree” with your data.

21. AI has the potential to drive down costs to serve consumers, which does not only benefit the end-consumer but also the platforms with better margins.

22. Even if AI is operating in the background, it can come at the cost of user experiences, especially in rural areas where digital adoption is not as advanced.

23. Externalities like consumer behavior and regulatory changes need to be factored in when deciding to what extent AI should be implemented.

Numbers 20 to 23 come from this podcast: 

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Paulo Joquiño is a writer and content producer for tech companies, and co-author of the book Navigating ASEANnovation. He is currently Editor of Insignia Business Review, the official publication of Insignia Ventures Partners, and senior content strategist for the venture capital firm, where he started right after graduation. As a university student, he took up multiple work opportunities in content and marketing for startups in Asia. These included interning as an associate at G3 Partners, a Seoul-based marketing agency for tech startups, running tech community engagements at coworking space and business community, ASPACE Philippines, and interning at workspace marketplace FlySpaces. He graduated with a BS Management Engineering at Ateneo de Manila University in 2019.