According to CNBC, San Francisco-based AI startup Writer launched a large-scale AI model on Wednesday, competing with enterprise products from companies like OpenAI and Anthropic. Notably, Writer spent approximately $700,000 to train its latest model, including data and GPUs, while its competitors' startups have spent millions of dollars to build their own models.
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Writer uses synthetic data (data created by AI) to reduce costs. It aims to simulate real-world information typically input into models without compromising privacy and is becoming a more popular training method. Companies like Amazon, Meta, and Microsoft have started using synthetic data to train their models.
However, some experts warn that synthetic data should be used with caution as it may degrade model performance and exacerbate existing biases. Waseem Alshikh, co-founder and CTO of Writer, stated that Writer has been working on synthetic data pipelines for years, emphasizing that they do not train models on false or hallucinated data, but rather on real factual data, which is then converted into clearer, cleaner synthetic data for model training.
Writer's generative AI allows corporate clients to use its large language model (LLM) to generate human-like text for anything from LinkedIn posts and job descriptions to mission statements, analyze and summarize data or text, and build custom AI applications for market analysis. The company has over 250 corporate clients, including Accenture, Uber, Salesforce, L'Oréal, and Vanguard, who use the technology in areas such as support, IT, operations, sales, and marketing.
The generative AI market is expected to reach $1 trillion in revenue within a decade. According to PitchBook, as of 2024, investors have poured $26.8 billion into 498 generative AI deals, with companies in the sector raising $25.9 billion in 2023, a more than 200% increase from 2022.