that seeks to leave behind a positive impact on the world. In the realm of technology and innovation, the greatest legacy for future generations lies in the advancements made in artificial intelligence (AI) and the decreasing costs associated with it.

that seeks to leave behind a positive impact on the world. In the realm of technology and innovation, the greatest legacy for future generations lies in the advancements made in artificial intelligence (AI) and the decreasing costs associated with it.

One significant development in this field is the acquisition of Mosaic by Databricks. Mosaic's vision to make AI training and finetuning cost-effective aligns perfectly with Databricks' goal of helping companies rapidly adopt machine learning to stay ahead of their competition. The collaboration between these two entities is driving the decreasing costs of AI and enabling more companies to harness its power.

In less than a year, the costs of AI training have decreased by a staggering 10 times. Previously, it would cost around $600,000 to train a stable diffusion model. However, thanks to the advancements brought forth by MosaicML, companies can now achieve the same results for only $50,000. This reduction in training costs can be attributed to two main factors.

Firstly, companies like MosaicML are continuously making algorithmic improvements, optimizing the efficiency and effectiveness of AI training. These advancements allow for faster and more accurate model development, significantly reducing the resources required.

Secondly, the costs of GPU (Graphics Processing Unit), a crucial component in AI training, have decreased by three times in just three years. For example, the price of a Nvidia T4 GPU for one hour was $0.95 in August 2019, whereas today, it is only $0.35 per hour. This reduction in GPU costs plays a significant role in making AI training more affordable and accessible for companies.

The decreasing costs of AI training have far-reaching implications. One notable impact is the drop in inference costs. In just 16 months, the costs of inference have decreased by 10 times, from $0.006 per 1,000 tokens for Curie generations to $0.0005 per 1,000 tokens for Curie quality generations today. This reduction in costs allows companies to deploy AI models at a fraction of the previous expenses, making it more viable for various applications.

For SaaS (Software as a Service) companies, cloud costs typically account for 50% of their COGS (Cost of Goods Sold). With AI features becoming an integral part of their offerings, the total cloud cost may equate to approximately 10% of their revenue. This means that some companies are spending as much on cloud costs as they are on generative AI features. However, it is important to note that AI features are in addition to regular cloud costs.

The decreasing costs of AI training and inference have significant implications for the industry. As the barriers to entry lower, more companies can enter the market as model providers, fostering healthy competition at the model layer. This competition is likely to place pricing pressure on closed-source model providers, making open-source alternatives more appealing for businesses.

In conclusion, the rapidly decreasing costs of AI training and inference are revolutionizing the field of artificial intelligence. Thanks to advancements in algorithms and the decreasing costs of GPUs, companies can now train and deploy AI models at a fraction of the previous expenses. This trend not only enables more companies to adopt AI but also fosters competition and innovation in the industry. As we look to the future, it is clear that the greatest legacy for future generations lies in the advancements made in AI and the democratization of its accessibility.

To navigate this changing landscape, here are three actionable pieces of advice:

1. Embrace open-source alternatives: As the costs of AI training decrease, consider exploring open-source model providers. These alternatives can offer competitive solutions while reducing the dependency on closed-source providers.

2. Continuously monitor GPU costs: Keep a close eye on the costs of GPUs, as they play a significant role in the affordability of AI training. By staying informed about the latest pricing trends, you can optimize your AI infrastructure expenses.

3. Evaluate the ROI of AI features: When integrating AI features into your products or services, carefully assess the return on investment. Determine the value these features bring to your offering and ensure that the costs align with the potential benefits.

By following these actionable steps, businesses can leverage the decreasing costs of AI to drive innovation, stay competitive, and leave a lasting legacy for future generations.