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Smarter AI Thanks to Specialized Human Trainers: A Behind-the-Scenes Look

In the past, training AI models like ChatGPT and its competitors involved vast teams of low-cost workers helping machines learn simple distinctions, such as identifying whether an image was of a car or a carrot. However, as AI models have become more advanced, the need for highly specialized human trainers has grown exponentially. Experts in various fields, from history to medicine, are now integral to shaping the next generation of AI.

Cohere co-founder Ivan Zhang explained that a year ago, undergraduates were enough to teach AI general improvements. But today, licensed professionals such as physicians and financial analysts are needed to ensure the models perform effectively in specialized environments. Cohere, a key rival of OpenAI, works with Invisible Tech, a startup that supplies thousands of trainers to help AI models reduce errors, known in the industry as “hallucinations.”

Invisible Tech, founded in 2015, initially focused on workflow automation but pivoted to AI training after OpenAI approached them in 2022. This partnership came about because OpenAI’s early ChatGPT models were prone to generating incorrect information, and they needed advanced human feedback to address this issue.

Invisible’s founder, Francis Pedraza, highlighted the company’s role in providing specialized human trainers to most of the big players in the generative AI (GenAI) space, including OpenAI, Cohere, and AI21. Invisible’s network now consists of 5,000 experts worldwide, many of whom hold advanced degrees. Depending on the complexity of the task, Invisible pays its trainers up to $40 per hour, with other companies in the space offering even higher rates for niche expertise.

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The issue of AI “hallucinations” has become a significant challenge for the industry. These occur when AI models generate factually inaccurate information, undermining their reliability, especially for businesses. To address this, companies like OpenAI run constant experiments to improve AI performance, relying heavily on human trainers with deep domain expertise to refine the models.

Invisible’s role extends beyond just providing human labor; they also help manage the increasingly large and complex tasks required to train AI. As Pedraza put it, OpenAI has some of the world’s best computer scientists, but they may not be experts in fields like Swedish history or biology. That’s where specialized trainers come in, filling knowledge gaps and making the models more versatile.

Other companies in this space, such as Scale AI, which was last valued at $14 billion, also supply training data and have ventured into providing AI trainers. However, Invisible remains a key player, having been profitable since 2021 despite only raising $8 million in primary capital. Pedraza noted that Invisible’s unique ownership structure allows them to maintain 70% control within the team, with a secondary market valuation of around half a billion dollars.

The rise of specialized trainers marks a significant shift from earlier days of AI training, which mostly involved low-paid, less-qualified workers from African and Asian countries. Today, demand for trainers with deep knowledge across a range of subjects and languages is creating a well-paid niche, opening doors for experts in various fields to contribute to AI development without needing coding skills.

As AI companies continue to push the boundaries of their technology, the market for specialized trainers is growing. Zhang, from Cohere, mentioned his inbox is flooded with inquiries from new firms offering similar services, underscoring the increasing importance of human expertise in the evolution of AI.

China’s AI Giants Step Up to Challenge U.S. in AI Development

While global attention has largely been on U.S. companies like OpenAI, Google, and Meta, China is making significant strides in the artificial intelligence (AI) race. Chinese tech giants, including Baidu, Alibaba, Tencent, Huawei, and ByteDance, have developed powerful generative AI models over the past 18 months. These companies aim to position China as a global leader in AI, adding a new dimension to the technology competition between China and the U.S.

Generative AI, which powers applications like ChatGPT and Google’s Gemini, can generate text, images, and videos based on user prompts. Below is a closer look at the key players and their AI models from China.

Baidu: ERNIE Baidu, a major Chinese internet company, was one of the first to launch generative AI tools. Its flagship model, Ernie Bot, is a chatbot designed to compete with OpenAI’s ChatGPT. With 300 million users, Ernie 4.0 claims capabilities comparable to GPT-4, offering understanding and reasoning abilities. Baidu is also commercializing its AI through its cloud computing division.

Alibaba: Tongyi Qianwen Alibaba launched its foundational AI models, known as Tongyi Qianwen, or Qwen, last year. The company has developed different versions for various tasks, such as content creation and solving mathematical problems. Some Qwen models are open-sourced, allowing developers to access them with certain restrictions. By May, over 90,000 enterprise users were using Qwen models.

Tencent: Hunyuan Tencent’s AI model, Hunyuan, focuses on Chinese language processing and advanced logical reasoning. Accessible via Tencent’s cloud services, Hunyuan is designed to support industries from gaming to social media and e-commerce. Tencent has also integrated Hunyuan into WeChat, China’s largest messaging platform, through its AI assistant, Yuanbao.

Huawei: Pangu Huawei has taken a unique approach by creating AI models tailored to specific industries like government, finance, and meteorology. Its Pangu AI models, available through Huawei’s cloud services, support generative features such as virtual human avatars and code generation. One standout model, the Pangu Meteorology Model, can predict the trajectory of typhoons with remarkable speed, significantly reducing prediction time.

ByteDance: Doubao ByteDance, the company behind TikTok, entered the AI race later than its competitors with its Doubao model. Doubao stands out by being more affordable and offers capabilities such as voice generation and code generation for developers, making it accessible to a broader range of users.

China’s AI advancements reflect the country’s growing ambition to rival U.S. companies in this critical technology sector.

 

AI Craze Distorting VC Market as Tech Giants Invest Billions

The venture capital market is grappling with distortion as tech giants like Microsoft, Amazon, Alphabet, and Nvidia pour billions into artificial intelligence (AI) startups, reshaping traditional investment dynamics. Unlike previous tech booms, where VCs were central players, the current AI frenzy is driven by these major tech companies investing heavily in capital-intensive firms such as OpenAI, Anthropic, Scale AI, and CoreWeave.

This shift in funding dynamics means that the usual pressures for startups to go public are less pronounced. Many of these AI firms are not yet profitable, which typically deters public market investors. Instead, tech giants are providing significant incentives, including cloud credits and business partnerships, further skewing the market.

Melissa Incera of S&P Global Market Intelligence notes that AI startups are attracting substantial investment interest despite having more funds than they can use. Venture capital exits are scarce, with U.S. VC exit values on track for $98 billion this year—an 86% drop from 2021. The number of venture-backed IPOs is expected to hit its lowest since 2016, underscoring the challenging exit environment for VCs.

In 2024, investors have already injected $26.8 billion into 498 generative AI deals, following a trend from 2023 when generative AI companies raised $25.9 billion, marking a more than 200% increase from 2022. This surge reflects a dramatic shift, with AI accounting for 27% of total fundraising this year, up from 12% in 2023. AI funding rounds have also grown 140% larger on average compared to the previous year.

Despite this influx of capital, venture capitalists are facing difficulties due to the current market conditions. The Federal Reserve’s interest rate hikes have pushed investors toward safer, yield-generating assets, making it hard for VCs to attract new funds without delivering returns. Traditional VCs are mostly investing in application-level AI startups rather than the high-capital infrastructure firms.

Notable AI companies like Cerebras, a semiconductor firm, are approaching an IPO, but most high-profile AI startups remain private. These companies, such as Anthropic and Cohere, have secured significant funding at inflated valuations, leaving VCs struggling to promise exits under current conditions.

The secondary market offers some liquidity through share sales, but IPOs remain the primary route for VCs to realize returns. As AI firms continue to grow privately, there is less incentive for them to go public, given the favorable terms they receive from large tech investors.

While the enterprise potential of generative AI remains high, with expectations of eventual significant returns, the current market conditions make it challenging for VCs to secure exits and attract new investments.