DeepSeek

There is a LOT of talk this morning about DeepSeek, and how it is shaking up the AI industry. This has huge ramifications not just in the AI market, but downstream in applications where they are using AI, like the growing e-Discovery market. Without getting too far into it, here are the five immediate things that I see regarding DeepSeek:

1. Security issues galore. Aside from the fact it is a Chinese created product (and we just went through and are still going through the TikTok security issues/sale/divestiture), it's open source meaning developers can use it for their own underlying AI functions in their own tools. Developers can also include things that make use of it less secure, like save your information for use in future modeling or even analyze the questions you ask of it. There is also an input/output problem where DeepSeek continues to learn and evolve based on what users collectively put into it. These items alone should give lawyers pause for using it in a legal setting currently. I would not trust it at all for use in e-Discovery yet.

2. Reverse engineering. If the Chinese government is to be believed, it costs substantially less to create and uses considerably less power than the standard AIs being created by Silicon Valley. If this is true (I have my doubts for several reasons) then the market just got turned on it's head. You can bet that Meta, OpenAI, Nvidia and others are reverse engineering this product to see how they can simulate the same power use and costs. It will be no time at all before the same results are integrated into the proprietary AIs currently available, and we see reductions in the costs to use THEIR products. Competition is a great thing sometimes.

3. From what I've read this morning, the outputs are about as good as Open AI's current 4o-mini. That's good but not great, but exceptional for the anticipated costs and most use cases. This level of competition could lower the costs of e-Discovery substantially further once the results are assimilated into more secure models. That means really cheap AI reviews, there is genuinely no way human reviewers will be able to compete in cost and quality. We're getting to the point where the costs of a human review offshored to INDIA will be more expensive than AI review.

4. What humans will be able to do is run and engineer AI searches/prompts. As AI review becomes the industry norm, what you'll see is Review Managers becoming savvier at prompt engineering, and a much smaller set of reviewers reviewing samples of the results. Validations and testing are going to be crucial, and there will always be a need for reviewers on privilege and sensitive materials (PII, PHI) reviews. With costs coming down substantially, the ability to run multiple versions of prompts across larger sets just became much more feasible as well.

5. Platforms with AI models already integrated (Relativity's aiR, ediscovery AI, Reveal, etc.) are out way ahead of everyone else in this. Revenue models for the industry are going to change dramatically to per doc pricing and flat fees over hourly billing. That changes the law firm dynamic more than most people think. Firms that are tech savvy at reducing the overall hosting and data costs are going to benefit in huge ways. That means they cull better using Early Case Assessment (the old Clearwell approach) and move over to review databases only that which absolutely needs review. Any way to reduce hosting and review costs are going to be net benefits and areas to maintain revenue.

Get ready. It's going to be a wild ride.