DeepSeek's R1: A Potential "Sputnik Moment" for AI Reasoning?

Is DeepSeek the true new AI reasoning frontier?

The overall opinion in the AI community varies from amazement to disbelief about how the DeepSeek team achieved reasoning-level capabilities and a performance that arguably surpasses?OpenAI o1 models in key benchmarks. No matter which side of the camp you are on, it’s indisputable that a shift happened this month when DeepSeek introduced their R1 model and a striking new way to train AI models.

The famous tech legend, Marc Andreessen, has called this event the ‘AI Sputnik moment’ In my view (living in Singapore), it’s like that moment before a tropical storm rolls in. There’s an electric tension in the air, and you know everything is about to change. DeepSeek’s paper, DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning, didn’t just fine-tune the current AI playbook. It practically rewrote it, showing how large-scale AI can develop reasoning skills by learning through trial and error, rather than strict instruction.

In the past few days, I tried to understand in a bit more depth why this is a significant milestone and how this could be transferrable to other areas of applied AI, including the impact on energy and resources sectors. I have also taken some time here and there to play with DeepSeek R1 via the Web UI, mobile app and a distilled R1 Qwen model that I downloaded for offline experimentation. So, with some initial caveats that I don’t claim to know the ‘ins and outs’ of how exactly each step of the R1 paper was executed, I will try to outline here at least some of the key points I’ve gathered and found relevant for industries regarding DeepSeek’s breakthrough.

The second caveat is that I am taking the paper and first-hand experience with the R1 model at face value, that is, I am not getting into speculations on whether DeepSeek is lying or not about their use of NVIDIA GPUs as well as not discussing the emerging controversies of possible violation of OpenAI's terms of use and questions on IP rights.

One area in which I'd exercise extreme caution is the legal interpretation of DeepSeek R1's T&Cs. It's not an area I have spent time on, thus I will defer this topic to our qualified Responsible and Legal AI folks to opine. At just a glance, for the online version of the model, be careful and read the usage terms and how DeepSeek can and might use your inputs and outputs, even after you choose to delete your account. Check usage and intellectual property ownership too. Assess those clauses and their associated risks. Consider carefully the implications to you and your business before you jump on it for more than harmless exploratory purposes.

I’ll also add a final caveat: reinforcement learning (RL) isn’t some silver bullet. It’s emerging as a cornerstone that can be combined with other proven methods, for example, supervised learning for predictive analytics, time-series forecasting for demand and trend prediction, and unsupervised learning for clustering and anomaly detection. That way, you tackle different types of problems. For instance, lost profit opportunity decisions in an oil refinery may require a reinforcement learning solution that augments first principles models with historical and live data to drive entire process optimization opportunities. Usually, you can’t effectively deploy AI to a refinery (or any operational context) without combining physics-driven and data-driven math in hybrid optimization models. Here, the new DeepSeek proposed approach could unlock new levels of intelligence in applied artificial intelligence systems by letting hybrid physics-driven and data-driven RL models autonomously develop advanced problem-solving strategies in more traditional engineering environments within the energy and resources industries.

Learning without a manual. Isn’t it how we learn?

Most advanced AI models, in particular, large language models (LLMs) (e.g. OpenAI, Meta, Anthropic) and specialized machine learning models, rely on supervised fine-tuning (SFT) to adapt to specific tasks or domains. SFT involves training a pre-trained model on labelled datasets to refine its performance on a particular use case. While SFT is widely used, it is often combined with other techniques, such as unsupervised pre-training, reinforcement learning (e.g., RLHF for aligning models with human preferences), and transfer learning, to create models that balance generalization and task-specific accuracy. A good analogy would be handing someone a lengthy manual and hoping they follow it to the letter, or as Matt McDonagh elegantly put it, “Imagine teaching a child to ride a bike. You could give them a detailed manual (SFT), but they'll likely learn better by trying it themselves (RL), falling, getting up, and gradually improving”.

DeepSeek went in a different direction. Their R1 model was trained primarily through reinforcement learning, which means it learned by doing and improved each time it got feedback. That approach led to moments where the AI recognized a flawed strategy midway and fixed it on its own.

If we try to transfer this concept to business realities, think of industries used to sudden demand spikes or harsh environments like power and utilities, renewables, oil and gas, water utilities, mining and chemicals. An AI that can adapt in near real-time is a big deal. It’s cheaper, too, because you’re not forced to create an endless stream of labelled examples upfront. Another advantage is transparency. During training, the model is designed to reveal how it arrives at an answer, showing its chain-of-thought. This is a clever way to augment root cause analysis (RCA), FMEA and other widely used problem-solving engineering techniques in asset-intensive industries. Additionally, this transparency can help build confidence when you’re operating in strict regulatory spaces or heavy-asset facilities. More importantly, it removes the “black box” perception of AI models and encourages faster adoption within business workflows.

Why you should care about this

1. Low(er) cost of scaling

DeepSeek claims its RL-first approach reduced overall investments compared to other leading-edge AI frameworks, including those that rely on massive, labelled datasets or specialized infrastructure. This contrast suggests a quicker, more cost-effective path to advanced AI capabilities than following the current paradigms of other frontier large foundation models or multi-stage fine-tuning pipelines.

Of course, it’s not just about cutting training costs. Most companies are not training large language models, they are integrating those via cloud services or on-premises deployments. However, suppose you’re in an industry like oil and gas, power generation, or petrochemicals, where margins can be tight. In that case, this efficiency is worth exploring because of the ability to move from pilots to scaled enterprise solutions at a fraction of the infrastructure cost required if you were to train and build reinforcement learning models to tackle business and operational problems. Besides, the fact that a powerful model like R1 is open-sourced, new possibilities of self-hosting and developing edge solutions for industries that require extreme precision sounds great.

Not so fast though, there are at least a few steps in between.

The next question still is whether you have the robust digital backbone to support it, however more affordable the new AI world may look like

Scaling AI without a strong digital core can be like building a skyscraper on soft soil. Within this digital core, you need a resilient data infrastructure with clean, integrated, well-governed (proprietary, synthetic and third-party) data to feed your AI models and handle the real-world complexity that comes when scaling it. Without that, any initial cost advantage DeepSeek’s approach offers might evaporate as soon as you push it into full operational use.

2. Smaller models are also powerful

DeepSeek’s final “distillation” step was all about making advanced AI more widely accessible, even if you don’t have a monster server cluster at your disposal. Typically, large language and other complex AI models require powerful hardware (think specialized GPUs or high-performance computing (HPC) environments) to run effectively (unless you’re leveraging LLM-as-a-Service via widely available APIs, which works well for most customers with less restricted and low-risk use cases). However, by distilling the intelligence from DeepSeek-R1 into smaller models like Qwen or Llama, the team managed to shrink the compute requirements and maintain most of the advanced reasoning capabilities. That’s another breakthrough.

This is a big deal for organizations operating on tighter budgets or in highly regulated settings where cloud usage can be complicated due to sovereignty, political choices or other enforced restrictions. Rather than having to outsource all AI functions or commit to enormous infrastructure investments, these distilled models can be deployed in-house on more modest hardware. For mid-tier operations, like a renewable plant that wants to run AI-driven optimization locally, or a utility company that needs AI behind a firewall for compliance reasons, this opens up the possibility of leveraging cutting-edge AI without the usual high costs. It effectively democratizes the technology, letting smaller players and specialized teams tap into cutting-edge AI models without having to build (and maintain) a specialized AI division or break the bank on cloud bills.

In my experience, you want to blend RL with other predictive techniques to fully realise AI value. Let’s say you have a time-series model forecasting demand for your power grid. It might provide a baseline you trust, while RL takes that forecast and decides how best to distribute loads or plan maintenance outages as it develops new problem-solving strategies. Similar to what we discussed above on hybrid physics-driven and data-driven models for oil refineries, this layered approach avoids over-reliance on a single method and gives you more robust outcomes, with higher accuracy overall.

3. Autonomous learning and self-evolution (a bit scary, actually)

In their paper, DeepSeek’s team describes how the DeepSeek-R1 model occasionally had “aha moments” that felt almost human. In these moments, the model spotted its own flawed reasoning mid-task and pivoted to a better approach. This is a stark departure from typical AI systems that follow a rigid script of pre-labelled examples. DeepSeek trained its base model, DeepSeek-R1-Zero, using reinforcement learning from day one (according to the paper, there was no initial supervised fine-tuning), so the AI was free to explore how it reasoned through problems.

Instead of mindlessly moving forward whenever it encountered a tricky question, the model began to self-check. It would reflect on its “chain-of-thought” and sometimes backtrack to re-evaluate its initial assumptions. In other words, it learned to learn. By incentivizing reasoning directly, DeepSeek enabled the model to develop behaviours like self-verification and reflection, leading to unexpected, sophisticated problem-solving strategies. This self-evolution process underscores the possibility of organically “growing” general intelligence through trial-and-error reinforcement, rather than just feeding the model a fixed set of right answers. Even for me who is part of this industry, this feels slightly sci-fi still.

The interesting part that I can immediately see is that reinforcement learning models can pivot quickly when conditions change, once they have “evolved their intelligence and reasoning” well enough. If we apply this approach to the energy sector, for example, we could mitigate oil price fluctuations or sudden changes in wind and solar power generation that can easily throw off static algorithms, helping both the commercial and trading functions optimize for the market demands but also increase our ability to integrate renewables into our grids more seamlessly and with less curtailment risk. An RL-driven model that learns from new data every moment could help respond with more precision to operational and market events. Don’t forget, that bringing this reality to life is possible but needs the underlying data pipelines to be real-time and highly accurate. There’s no workaround for that.

Here are just some additional use case ideas that DeepSeek R1 itself helped me come up with:

●????? Power and Utilities: An RL-enhanced model might analyze consumption forecasts from existing time-series methods, and then dynamically adjust electricity flows across different regions to minimize blackouts.

●????? Renewables: Imagine an AI that optimizes wind turbine angles in response to shifting weather patterns, checking its own logic and improving its strategy after each gust.

●????? Oil and Gas: An RL system could fine-tune upstream production rates or calibrate refining processes on the fly, building on anomaly detection models that spot potential breakdowns early.

●????? Mining and Chemical Sectors: From predicting rock fracturing to balancing chemical reactions in large-scale processing, an RL-based AI could refine operations by learning from sensor data in real-time and adjusting parameters accordingly.

The point is, that R1’s approach can amplify other models you already rely on. A good AI solution doesn’t throw away what works. It integrates the best parts.

Fundamentals are even more important now

As I previously said, there is no silver bullet. Whatever you do with AI, your best bet is to tackle both: reinventing your business workflow with AI (supported by use cases) and rewiring your digital core.

There’s a reason 96% of companies recognise AI’s potential, but only 9% have fully deployed it. Complexity, cost, and risk multiply when you move from small proofs-of-concept to enterprise-scale rollouts. To tackle this problem, Accenture introduced a solution called the “AI Refinery,” which helps organizations turn fragmented AI pilots into scalable value-led enterprise solutions.

The AI Refinery emphasizes four key pillars: Agents, Knowledge, Models, and a Backbone.

● Agents connect the dots by autonomously acting on insights, though you can maintain human oversight for critical decisions and interact back and forth seamlessly.

● Knowledge means vectorizing or indexing your corporate data so models can quickly extract the right information. Think of it as rewiring your data so machines can read it.

● Models are refined and optimized with your data, ensuring they’re relevant to your specific operations. I call them ‘engines of intelligence’.

●?Backbone manages the entire AI ecosystem, keeping an eye on cost, performance, and compliance. This is where the right combination of modern AI infrastructure takes place.

To make these four pillars work, you must treat your data like a real asset (would you throw gold bullion away?). In the era of generative AI, a strong data foundation isn’t just about centralizing what you already have, it’s also about tapping new data sources, bridging data gaps, and navigating inherent cyber risk.

Last year, Accenture launched a series of thought leadership papers on this. In my view, the six essentials for data readiness are crucial to succeed and accelerate AI journeys from PoCs to AI that is embedded in the organization for tangible value creation:

1.?Proprietary Data Is Your Secret Advantage

• LLMs become game-changing when paired with unique operational or customer data your competitors don’t have.

2.?Unstructured Data Unlocks Context

• Text, images, audio, and video can reveal deeper insights that structured data alone can miss.

3. Synthetic Data Fills Gaps

• When real data is limited or sensitive, generating realistic stand-ins accelerates training and cuts risk.

4. Connected Data Fuels Collaboration

• Breaking down silos allows cross-functional insights and reinvents end-to-end business processes.

5. Generative AI Raises the Stakes

• More data usage means higher exposure to cyber threats, privacy concerns, and quality issues. Data governance is a must.

6.???? Generative AI as a Data Booster

• LLMs can help document, catalogue, and modernize data faster, letting you leapfrog legacy systems if done well. We are seeing clients turbocharge their AI progress by adopting this duality of Generative AI as a synthesizer and data preparation accelerator whilst delivering treated data to Generative AI engines for agentic solutions, chatbots or other generative applications.

None of the sophisticated AI stacks matters if your data is continually at low quality, unmanaged or scattered across siloed systems. I will be pedantic, as you try RL, agents and advanced generative AI (which I highly recommend you do), you need to focus on unifying your data by covering the six essentials above. That might also mean building a more robust, cloud-based architecture, integrating sensors across your sites for better data collection, and standing up federated governance practices so you can trust the data being fed into your models. In other words, you need a digital core that can flex as business needs evolve without creating over-reliance on underlying frontier models.

Architectural flexibility is essential to capture the highest value opportunities with AI. As new frontier models emerge and breakthroughs shift the conversation, you need an adaptable “switchboard-like” AI architecture that allows your business to plug and play different models without overhauling the entire stack. This architectural agility ensures that you can quickly swap in new capabilities or revert to trusted ones as required. I can’t stress this enough. It’s an essential strategy when tomorrow’s developments may render today’s best model obsolete overnight. Don’t create dependency on foundation models, they are not your competitive advantage. Your data, your IP (models) and your applied solutions are.

A quick primer on why I think AI agents might be the next big thing

One of the more intriguing angles here is the possibility of AI Agents that can do more than just recommend a course of action. Especially now with high-grade reasoning capabilities, they can initiate tasks, allocate resources, or request additional data. You could, for instance, have an AI Agent that senses a drop in renewable energy output and automatically adjusts a backup power plant’s capacity. Although that level of autonomy sounds futuristic, it’s closer than we think if your digital backbone is solid. I’d still advise caution. Keeping people in (or on) the loop for high-stakes decisions, such as adjusting a power plant production output or shutting down a processing unit in a chemical plant, is essential to managing risk. Bear in mind that AI Agents demand high-quality data to be effective. Six data essentials, anyone?

Another example I was recently working on. Imagine an inquiry from a freight broker in Malaysia. They send a WhatsApp message with the new route, cargo volume, and a few special discount rules. The Communications Agent spots the incoming message. It passes the text and attachments to the Extractor Agent, which scans the PDF for base rates, laycan dates, or Incoterms. Next, the Reason Agent checks for missing details—like the currency or standard disclaimers. If something is unclear, the Negotiation Agent can ask follow-up questions through the same WhatsApp thread. Meanwhile, an Analysis Agent compares the offer with historical deals, factoring in cost risk if the voyage runs over schedule. Finally, a Connector Agent updates your enterprise systems with the negotiated terms. At each step, specialized agents handle exactly what they’re good at.

In my experience, if you’re exploring agentic AI systems, start with a targeted pilot that solves a real pain point, such as contract negotiation in shipping or capacity management in utilities. Measure the impact thoroughly. Show how much time is saved, how many errors are eliminated, and how your staff can now focus on bigger priorities. Then scale it. Because once you’ve built that digital core and proven the approach, it’s much easier to add new agents or push into new domains.

Back to DeepSeek with a few final thoughts...

As we have covered, the DeepSeek team introduced a novel approach that lets AI grow more sophisticated by learning from what it does, rather than relying solely on curated training sets. Yet, it’s important to remember that R1 isn’t the ultimate panacea. In my view, R1 itself (or other frontier LLMs) and applied RL can only thrive in an ecosystem where your foundational data is properly managed and other proven predictive models (e.g. physics-driven, time-series, anomaly detection etc) are part of the mix.

For companies in renewables, oil and gas, power and utilities, water, chemicals, or mining, the real promise lies in combining RL with a modern AI backbone and carefully selected Agents. The fundamentals still matter. You’ll want a robust digital infrastructure, a clear data governance model, and a willingness to experiment with layering different AI methods for maximum impact. It’s a journey, not a quick fix.

The most plausible reason that only a few enterprises have fully capitalized on AI is that they haven’t built these fundamental elements. If you take the time to develop your digital core, unify your data, and add the right level of automation, then DeepSeek’s style of reinforcement learning could help you unlock measurable efficiency gains and operational agility. That, to me, is where the real excitement lies. The breakthroughs are here, but it’s the groundwork beneath them that will determine whether you see sustainable, game-changing results.

Finally, we must continually remind ourselves that no large language model is entirely free from errors, biases, or external controls.

In my usage of DeepSeek for about a week, I’ve encountered significant censorship on perceived sensitive topics, regardless of how harmless and transparent my prompt would be on items such as the history of Taiwan or Tiananmen Square events, presumably reflecting the model’s inherent constraints, macro politics scenario and the influence of its training environment. This is a reminder that these breakthroughs (however impressive!) should be thoroughly vetted, tested, and adapted before being applied to production-grade tasks.

In summary, including the RL approach popularized now by DeepSeek in your AI strategy can be impactful. However, adopting R1 models for intelligence applications and business workflows requires rigour. Investing first in a Responsible AI Framework, including legal, cyber, data, and governance, among other key disciplines, is essential to balance your risk/benefit equation.

Every organization needs to weigh these considerations carefully, especially in regulated industries or when the stakes are high. I’d advise extreme caution as you navigate frontier model selections unless you’ve listened to me earlier and have developed your robust RAI Framework, data infrastructure and switchboard architecture. Then, you’ve got much less to stress about and can focus on the impressive value that AI can create for you and your business.

I hope you have fun keeping up with the AI industry. I’m almost out of breath.