The Battle of Generative AI: Money Talks in the Competition of Large Language Models

The world of generative AI is witnessing a fierce battle among large language models and generative AI platforms in terms of applications and functions. However, the real battle is far more grounded: funding. According to forecasts by think tank Bloomberg Intelligence, the global GenAI market will reach US$1.3 trillion by 2032. As GenAI relies on large models for its generative capability, the battle of GenAI is, in fact, a battle between large models.

Funding is key in the battle of large language models

The most widely used among these is Large Language Models (LLMs). Funds are coming in to put a stake on the future of LLMs. To date, Microsoft has invested more than US$13 billion on OpenAI; Amazon upped its investments in OpenAI rival Anthropic by US$2.75 billion; Meta announced in its first quarter earnings report that it plans to spend billions more on AI; and Google announced in April that it will invest US$3 billion in building a data centre.

“Companies like Amazon, Meta, Microsoft, and Google have all raised their capital spending plans on AI substantially, a trend that will likely carry into the years ahead.” - Tan De Jun, research and portfolio manager at FSMOne.com

Outside of the tech giants, the birth of ChatGPT has also caused a wave of ventures into AI. According to data from CrunchBase, AI start-up companies around the world have raised nearly US$50 billion in 2023.

AI startups are on the rise

But just one year on, the market is very different. Faced with the high cost of computing power, the question of profitability, and the withdrawal of investment, worries are mounting: will the LLM competition become a game for the biggest players only?

Burning through money is almost ingrained in the DNA of LLMs. The performance of the model is largely dependent on the number of parameters; the more parameters there are, the higher the computing cost.

“If you don’t have US$200 to 300 million, it’s very hard to start.” - Yang You, presidential young professor at the School of Computing at National University of Singapore (NUS)

Burning through money is almost ingrained in the DNA of LLMs. The performance of the model is largely dependent on the number of parameters; the more parameters there are, the higher the computing cost.

Computing power is a major cost factor

However, the cost of electricity is a mere fraction of the cost of processing chips. Lu Jianfeng, chairman and co-founder of Singapore-based AI company WIZ.AI, said, “[Mark] Zuckerberg said Meta requires 600,000 H100 (NVIDIA H100 Tensor Core GPU) of computing power, for OpenAI this could be 1 million pieces. Outside of these top companies, it’s very hard for anyone else to start from nothing to produce a general-use large model.”

High costs naturally bring about questions of profitability. Figures show that the economic benefits generated by large models are currently far below investments.

Profitability is a major concern

According to a report by Grand View Research, the global LLM market reached US$4.35 billion in 2023. In contrast, data from CrunchBase showed that AI start-ups worldwide raised a total of nearly US$50 billion in financing last year, of which some US$18 billion went to just three US companies: OpenAI, Anthropic, and Inflection AI.

“With the high cost of computing power, with existing architecture and hardware technology, even if companies started charging users, general-use LLMs such as ChatGPT would still be vastly unprofitable.” - Yang You

What they are betting on is the longer-term future - artificial general intelligence (AGI), which is AI that will truly reach or even surpass human-level intelligence.

The ultimate goal is artificial general intelligence

Clearly, short-term profit is not what the tech titans are after. What they are betting on is the longer-term future - artificial general intelligence (AGI), which is AI that will truly reach or even surpass human-level intelligence.

Mark Zuckerberg has also said that AGI is the company’s “long-term vision”.

“If you think AGI is in, the more GPUs you have to buy.” - Yann LeCun, chief AI scientist at Meta

While the tech giants have a full armoury, most start-ups do not have the luxury of overlooking commercial challenges, especially as financing becomes increasingly difficult after the market frenzy.

Startups face significant challenges

However, he feels that AI companies targeting businesses and other verticals have it better: they are seeing more opportunities and it has not become an environment where the winner takes it all.

“If a large model is trained for specific scenarios, with a volume and cost that’s only 1% of OpenAI, you’ll see profits very quickly… For example, using a large model to accelerate R&D in pharmaceutical drugs, or helping oil companies to find oil.” - Yang You

With computing resources as a limiting factor, LLMs that specialise in certain sectors or have specific capabilities are turning out to be the only viable path for the majority of small companies. Most of such language models are used in enterprise services.

Specialized LLMs are the way forward

PatSnap, a Singaporean tech intelligence unicorn, is one such example. PatSnap co-founder Guan Dian said that compared with the general-use large models of OpenAI and Anthropic, PatSnap’s LLM goes deeper and is more precise in the patents sector.

“For the tech giants, vertical application markets are smaller, so they won’t come in to compete with you, and you will have the space to grow.” - Guan Dian

WIZ.AI is based on open-source models, and the company has incorporated Indonesian languages and trained the first Indonesian LLM in Southeast Asia for use in customer service. Lu Jianfeng said small companies are not looking to rise to the zenith of technology; instead, they are looking at increasing efficiencies.

“It’s more putting new wine into old bottles, and the new wine tastes a lot better too.” - Lu Jianfeng

The sensitivity of smaller companies to computing costs is also a pain point in the implementation of LLMs. Lu said the larger the model, the higher the cost of inference. If a scenario requires only basic knowledge, an overly large model would not be economic, so these factors need to be taken into consideration in actual operations.

Computing costs are a major concern

… if you have a model that is best suited to the languages and industries of Southeast Asia that can offer value across different sectors and industries, it will also attract high-end talent, like Falcon, which was launched in the Middle East.

The US is leading while Europe and China play catch up in the competitive field of AI. Both at the very top and at the level of start-ups, the US is the clear leader. Among the top ten AI companies in 2023 in terms of capital raised, eight were from the US, and the other two from Europe.

The AI competition is heating up

Geopolitics is also affecting this war. Back in 2022, the US government started to ban Nvidia from exporting its high-end A100 and H100 chips to China, hampering China’s competitiveness in the field of AI.

“Especially for countries like China, who are emphasising autonomy and control, they will definitely develop an independent large model, otherwise the risk is just too great.” - Yang You

He said small countries like Singapore will also need to develop their own LLMs with tens of billions of parameters.

“It’s not about competing with OpenAI; if you have a model that is best suited to the languages and industries of Southeast Asia that can offer value across different sectors and industries, it will also attract high-end talent, like Falcon, which was launched in the Middle East.” - Yang You

In 2023, the United Arab Emirates launched its own LLM with 40 billion parameters which included a chatbot, customer service functions, virtual assistant, translation, content generation, emotion analysis and other uses. Falcon showed outstanding performance, even outperforming OpenAI and Google’s models in some aspects.

Falcon is a leading LLM

Southeast Asian LLM launched by Singapore last year

Last year, Singapore launched Sea-Lion, an LLM tailored for Southeast Asia, with two versions based on 3 billion and 7 billion parameters respectively. Last December, when Singapore announced its AI Strategy 2.0, it included an investment of $70 million over two years to build an LLM, based on Sea-Lion, with 30 billion to 50 billion parameters.

“Singapore should raise its computing capability, as it is a limiting factor in creating synergies.” - Yang You

In addition, Singaporean LLM start-ups are smaller, and small start-ups are known to achieve technological breakthroughs more efficiently than research institutions. At present, start-up companies in the field are concentrated in the US, China, and Europe, so Singapore should also look into developing the sector.

Singapore’s AI strategy is focused on developing its computing capability