Just when the AI market seemed on a steady course, DeepSeek entered like a kid setting off fireworks indoors. Its low-cost AI model, bypassing NVIDIA frameworks, led to a historic $600 billion slump for the US tech giant. And the industry’s crown prince, OpenAI, has started a war on two fronts – the first is legal, challenging DeepSeek on model training, and the second is competitive, combatting its sudden rival with the launch of 03-mini.
With much of Silicon Valley now in a tailspin, Big Tech is still scratching its head about how a much more efficient AI tool could take the world by surprise. Yet, while for many the full capabilities of this new Open Source platform are still being understood, it speaks to an age old truth in business – competition can come from anywhere, and ultimately this is the latest example of a fresh innovator pushing competitors to be more efficient with their emerging tech investments.
A dive into DeepSeek
For curious onlookers, what’s DeepSeek really doing that is so gamechanging? The company is marketing itself in direct contest with OpenAI – “rivalling OpenAI’s Model o1”. DeepSeek’s R1 LLM is priced at the fraction of the cost of vendor alternatives – one of its key draws. Another kicker is that its foundation is based on reinforcement learning rather than labeled data. For AI, this is revolutionary. Labeled data provides a target for the model to predict – it’s like training wheels for AI: time-intensive to set up but helping keep models on the right path. DeepSeek runs without this, causing it to be slower upfront, but faster and more scalable in the long run, avoiding data tagging bottlenecks. Like ChatGPT in many aspects, DeepSeek can excel in mathematical and computational tasks, and through Open Source availability its ‘weights’ have been disclosed to the public, a huge win for the open-source community, in comparison to black box products on the market.
All these disparate elements added up means DeepSeek provides attractive scope for largescale automation for users, with its free availability and capacity to create chatbots rivalling other models. But it’s not all green flags. Concerns about data protection and information freedom are an issue, as data is housed in China under their own non-EU regulations. This is why it’s vital that organizations and individuals alike should carefully consider if the business process and regime it sits in are acceptable, compared to current requirements for data privacy, protection, and creative and political expression.
However, throwing data caution to the wind, consumers have flocked to the app in their millions, demonstrating that there will likely soon come a time where users are split between an ‘everyday’ AI they can play or interact with comparatively simply, and more expensive, advanced AI, coexisting for different, likely public sector, research, and industry use cases. Thus, for providers, the best competition strategy is to innovate, improve UX and functionality and find the right niche or market to dominate.
What’s the deal with ‘agentic AI’?
LLMs and GenAI are of course just one avenue of AI innovation. A hot new buzzword in Big Tech is ‘agentic AI’. This will be revolutionary for reimagining workflows and may soon create holistic AI ecosystems that autonomously manage and optimize processes in concert, with little human oversight in some use cases. Agentic AI is tipped by Gartner to revolutionize AI’s potential, with scope for it to be featured in a third (33%) of enterprise software applications by 2028, up from a minimal 1% today.
While more complex AI workflows are highly anticipated, basic prompt-and-response applications will become available off-the-shelf and, for now, this will serve many public users more than adequately. In our State of AI in Sales report, we found that nearly half (47%) of current AI users have no immediate plans to further integrate AI into their workflows. In fact, AI usage is barely moving past a basic level for many organizations. But those exploring exciting, nuanced applications will find more options with agentic AI.
This different type of AI service is less likely to hallucinate as it’s not the same kind of AI as GenAI, but will come with its own pros and cons to manage in terms of effectiveness, ability to execute what’s asked of it, and the levels of human oversight required to ensure reliability and accountability for the actions it takes.
A step towards that ‘Star Trek’ future
A world of supercomputers and starships – with the pace of change in emerging tech, it seems like sci-fi isn’t that far off from reality. But is that accurate? We are a long way from technology being leveraged for the sake of flash – what’s practical is what’s functional. Finding the right level of technology to solve the needs of the user is critical. For environmental and cost reasons, people don’t get in a Ferrari to go pick up groceries or tear up a whole field for just one bowl of cornflakes. Right now, a lot of the effort, expense, and resources of AI ‘behind the hood’ is hidden from the public, but society needs to become more knowledgeable about this to make better decisions, and to help direct industry to innovate where it will have the most impact.
Yet, it’s plain to see that, all other issues put to one side, the likelihood of more ubiquitous and embedded AI just took a step towards reality. And, given that DeepSeek comes from outside the US, perhaps AI creation may come from a greater variety of cultures, potentially reshaping global AI offerings and diversifying centers of expertise and the ways in which different users are catered for.
One of the most notable things that struck me in the vision that Star Trek laid out for a possible utopian future was the level of trust that users had in their computing. They appeared to have cracked issues of data privacy and security such that they didn’t hesitate to use their AI for work and leisure, for reasons great or small. That hinges on trust. For current AI users, there must be trade-offs around trust and economics if they want to balance being good data stewards of their own or customer data, and both the monetary and external costs of their tech use.
What’s next – AI 2.0?
There is a logical progression in AI innovation. Users want support with small tasks, see value, and increase their expectation of what AI can deliver. If AI can sort emails, can it review them? If AI can find a fault, can it fix it? Hence the growing interest in agentic AI as a new milestone to reach.
Arguably, there are tensions between innovation and trust, between economics and excitement, standard and frameworks and features. Over the next few years, the industry will need this to shake out more clearly if individual suppliers are to gauge their markets well and afford to keep innovating. Additionally, partnerships, ecosystems, and APIs – generally, working together to provide greater customer value – will need very clear international standards and for secure and trustworthy interoperability.
This will be key as, barring a massive leap forward in AGI (artificial general intelligence), perhaps led by quantum computing, there’s it not looking likely that there will be ‘one system to rule them all’, as in an all-encompassing AI that can accurately perform all the tasks a person or organisation might want. But siloed AIs, like any siloed software solution, aren’t likely to create that Star Trek-like world society apparently envisions.
Consumers and organizations must vote with their wallets, consciences, and needs in mind. But suppliers must really look ahead to a longer-term play if they are to answer these major challenges and support the society that they cannot just supply, but hopefully make better, with AI.
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