This year, volatile market forces, governmental policies, and societal demands will converge to shape the pace and direction of the energy transition. We expect global energy prices to remain volatile this year, influenced by factors such as geopolitical tensions, the upcoming elections, market speculation, and fragile supply chains that are not secure enough to deliver on our energy needs.

Looking ahead to the key forces set to shape energy demand, we expect to see the following advances, and challenges, in 2024.

Sovereignty considerations will be at the forefront of the energy transition

The energy transition’s pace hinges on this year’s crucial global elections, involving nearly half the world’s population. The outcome of these elections will decide how governments worldwide implement policies and incentives to speed up the move to cleaner energy alternatives. Though we’ll have to watch and wait for the political outcome, what we do expect to continue is the US making progress in clean electricity through its Inflation Reduction Act (IRA) and China’s carbon emissions declining this year, driven by a substantial increase in clean energy investments.

We also anticipate a stronger link between renewables and energy sovereignty. Our World Energy Markets Observatory (WEMO) 2023-24 found that more countries are transitioning to in-country renewable sources as a way to protect energy supply against geopolitical uncertainties. The energy transition will continue to be a way to safeguard supply against geopolitical threats this year. Whatever the outcome of elections, we expect governments and regulators to lead market interventions, such as demand response and flexibility schemes, which are crucial for ensuring the integration of intermittent renewable energy sources and maintaining grid stability. We also expect to see more governments reassessing investments in larger, long-term assets like nuclear.

SMRs have a simplified design based on existing and proven light water reactor technology (Gen III+ and predecessors), along with a mature and robust fuel supply chain. This reduces the overall licensing and build risk. While SMRs enjoy a well-established technological basis, other technologies such as advanced (Generation IV) reactors (Rs) will also see increased interest in 2024 as demonstration projects and the development of the fuel supply chain progress.  

Nevertheless, many nuclear projects will face financial setbacks and will encounter regulatory challenges, as we have seen in the US. This is not unexpected as the industry emerges and will require innovative approaches to minimize risk to ensure project feasibility.  To be sure, there will be winners and losers, but fortune will favor the well-prepared.

The year of consolidation

As governments around the world gear up for elections, the private sector will also be looking closely at their priorities. The oil and gas industry will continue a period of consolidation as companies streamline their operations and focus on assets that align with their transition plans.

The role of gas in a net-zero emissions future is also subject to debate, and the private and public sector alike will need to carefully consider the environmental and economic implications of its continued use. We expect to see this consolidation accelerate in 2024 as companies seek to optimize their portfolios and adapt to the changing energy landscape.

The potential of generative AI will become reality

Last but not least, 2023 was indisputably the year of AI, with the technology having a profound impact on the world’s businesses. AI has more than 50 different uses in the energy system, and the market for the technology in the sector could be worth up to USD 13 billion.

AI is already having a huge impact on the growth of smart grids and smart meters, so we anticipate an AI boom in the running of components within energy systems over the coming year, especially relating to customer service.

Overall, as we continue to navigate the energy transition in 2024, the global landscape is undergoing profound shifts, presenting both challenges and opportunities. We are expecting to see further synergies between digital and sustainable innovation, taking advantage of the potential of technologies to accelerate the energy transition for the better. The continued integration of AI, alongside advancements in renewable energy and nuclear technologies, positions 2024 as a pivotal year in our collective journey towards a sustainable and resilient future within the energy sector.

In the previous blog, we covered the key ingredients of the vehicle user experience. Here, we’ll discuss how to redesign the vehicle for that user experience.

Step 1: Plan new use cases and business models

Change how you think about the car. It’s no longer a machine to get people from A to B, it’s now an environment where people spend time and benefit from experiences.

The vehicle that rolls off of the production line can be thought of now as a Minimal Value Product – a product which can increase its value through software additions across its life cycle. For automotive companies, correctly implementing this business model is partly a financial engineering challenge – which will also require a change in mindset for both vendors and consumers.

Step 2: Build the in-car tech to enable these new use cases

The in-car software and hardware architecture to support these new services will fall into three categories:

• The software and processing power to run information and entertainment displays, as well as repurpose a glut of ‘big data’ into meaningful information. This will likely include processing data from onboard sensors, and from external devices.
• The communication technology to allow the vehicle to exchange information with external devices, as well as selecting the right protocols (Wi-Fi, cellular modems, etc.) to deliver this.
• An ecosystem that allows the car to safely access third-party apps, including processes for app certification.

Step 3: Verify

All of the above needs rigorous testing and verification. Mostly, this will involve running new services in a simulated vehicle environment to check they function as intended.

Some services may need road testing. But for ones that don’t touch vehicle controls, or risk causing distractions, it is usually fine to conduct ‘real-world testing’, by launching beta versions, gathering user feedback, and using this data to continually improve products.

Step 4: Create a software-driven culture to outpace the competition

The move to digital services is forcing traditional automotive companies to rethink how they build and launch services. The digital culture of rapidly iterating digital products must reconcile itself with the more traditional, measured and safety-conscious automotive approach.

Step 5: Evolve your supplier ecosystem

Carmakers will need to expand their list of suppliers. Gone are the days when a Tier 1 knew everyone and could handle everything. This brave new world will encompass specialist providers in telco, silicon, software development and XR, as well as emerging technologies, like the metaverse.

Competition in the automotive industry is intensifying and brands are competing on more fronts than at any time in history. Of course, price, performance, brand, and residual values continue to be important. But as the industry gravitates toward electrification and software-defined vehicles, customers are looking at what else their vehicles can do for them. How well do they integrate with their lives and their digital ecosystems? Can and will the car evolve over time to add more value to daily life? And, for manufacturers, how do you build supply chain resilience and competitiveness to address these evolving demands, while ensuring availability and affordability? 

Automakers – especially at the luxury and premium end of the market – are also intensifying their focus on providing assisted and autonomous driving capabilities and new ways to add value with digital experiences, inside and outside the vehicle. In the face of increased competition, the speed with which automakers are able to innovate and the extent to which they can engage and satisfy their customers in new ways will be crucial to future success or failure.

Autonomous mobility at the crossroads

For years, tech and innovation events like CES have been dominated by autonomous vehicles of all shapes and sizes. The technology is always impressive … at the shows. But, in the real world, progress has been slower than expected. For every success, it seems like there’s been at least one story of a scaled-back or canceled investment, an unfulfilled promise, or a serious safety scare.

The pursuit of autonomous mobility is a double-edged sword. The cost of adding sensors for 20+ detection zones around the car is significant. And the volumes of data, the sophistication of algorithms, and the amount of computing power required to develop, test, and validate systems are eye-watering. And yet, the ability to offer customers safe and stress-free ways to travel; to give back quality time while getting from A to B, is a once-in-a-lifetime opportunity to build trust and open the door to a whole new world of services and revenue streams. It’s no wonder the pursuit of the various certification levels is so intense and why so many companies are taking different routes – from in-house development with tech partners to major alliances with tier-1 suppliers, and even acquisitions. Some companies are making more progress than others, but the race is still wide open.

The in-car experience is evolving

The transition to electric and the pursuit of autonomous-driving capabilities have major implications for the automotive customer experience, especially the in-car digital experience. With electric vehicles, we know that recharging away from home will involve idle time. And – though it may still be a way off – autonomous mobility will allow us to focus less on driving the car and leave us more time to do other things. Today, our first thought might be to reach for our smartphones or tablets, but this is a lost opportunity for vehicle manufacturers.

And so the question becomes: How can your car keep you entertained and engaged while it charges or self-drives?

The answers are emerging in the form of expansive screens, adaptive interfaces, the addition of extra screens for passengers, an increasing emphasis on in-car gaming, content consumption, subscription services, and almost unlimited ways to pass the time productively, recreationally, or relaxingly in a vehicle.

And then there’s the potential to have an AI-powered assistant, or companion, that connects all the different services and is capable of providing pretty much any information you need about your journey, your agenda, upcoming commitments, highlights from your inbox or social media feed, and much more.

All of these features represent potential points of differentiation, and many of them are revenue-generating opportunities (e.g. subscription-based services). Beyond direct revenue and new levels of customer intimacy, in-car digital interactions also create opportunities to generate new data and insights, which can (with the right levels of consent and anonymity, of course) be used to shape new products and services – inside and outside the vehicle – and new monetization opportunities.

Speed and satisfaction – why they matter more than ever

You could argue that the evolutions I’ve explored above are technology trends, much like many others. However, these trends are different in that if you can achieve the combination of safe autonomous or highly assisted mobility and engage customers with compelling in-car experiences, you can gain a level of trust, and access – and even companionship – that is unprecedented in the history of OEM-customer relationships. This brings with it the opportunity to develop deeper, longer, and more lucrative relationships.

But the race for the hearts and minds of customers is intense, with a raft of new players (many from China) to compete against, new demographics, and rapidly evolving customer expectations. In this climate of increased competition, it is imperative that automotive companies intensify their innovation efforts in a bid to deliver the integrated and connected customer experience that will soon be taken for granted. And if your brand isn’t able to provide it, you can assume that another one will. 

Balancing the need for speed and satisfaction with a ‘zero compromise’ approach to safety and security

Against this backdrop of ultra-intense competition and a relentless focus on innovation, OEMs must remain vigilant and understand that speed to market can never take priority over safety and security.

Assisted and autonomous mobility can offer comfortable, convenient, and stress-free travel. But they also mean taking a significant degree of responsibility for the safety of vehicle occupants. In short, ADAS and autonomous driving systems cannot fail. Failures will result in more than a few lost sales – they could lead to loss of life, high-profile court cases, and a complete loss of confidence in your brand.

And though it’s less likely to be a life-or-death matter, automotive brands need to be vigilant about ensuring the cybersecurity of their vehicles and data ecosystems. Digital assistance or companionship, subscriptions, services, integrated payment solutions and ecosystem services (e.g. via wearable health devices, smartphones, etc.) will typically require some degree of data sharing. This opens the door for personalization and seamlessly convenient experiences, but it’s not without its risks. No brand wants to be the next one to appear in a high-profile data leak story and risk losing the hard-earned trust of its customers.

Software is the key to safe, secure, and satisfying experiences

So what’s the key to accelerating innovation cycles and customer satisfaction without compromising on safety and data security?

The answer lies in your software strategy. After all, software is at the heart of assisted and autonomous driving systems, it drives immersive and engaging digital experiences through infotainment systems and more, and it can be the key to ensuring the security of personal data and the identification and elimination of sophisticated cybersecurity threats. The right software strategy and architecture (i.e. a simplified one) can also provide you with greater flexibility during times of supply chain instability, meaning you can maintain product availability while your competition potentially suffers. As many of us learned during the pandemic, simply making sure your cars are available to potential buyers can be the biggest advantage of all.

But the stakes are too high with software and the task of transforming into a software company is too big to go it alone. Here are three ways automotive companies can get their transformation right.

1. Partner up to boost software capabilities

Software-driven transformation is a broad and deep-reaching process, which can encompass upskilling your existing team, building new capabilities, and finding the right balance between maintaining your existing digital products and developing new ones. This is a huge undertaking, and so it makes sense to partner up with automotive software specialists and engineers who can share and instill industry best practices, build dedicated software factories for you, or support you in maintaining existing products or developing new ones.

2. Use cloud, virtualization, and AI to achieve more

Cloud and AI can be used to process and analyze the high volumes of data produced during autonomous driving system development and testing, to virtualize ECUs, and to support data spaces and service ecosystems. These technologies, combined with the suite of automotive-specific accelerators being built by hyperscalers today, can supercharge your innovation and product development cycles, enabling you to get to market faster with new products and services, while keeping your – and your customers’ – valuable data secure. 

3. Look for external inspiration

Automotive companies can’t be everything to everybody. It’s difficult (impossible?) to develop an infotainment UX that rivals that of smartphone makers like Apple and Google if it’s not your core business. Likewise, you won’t suddenly create ‘killer’ content and entertainment options if you’re just starting out. Instead, partner up with startups and niche players in differentiating domains and focus on the bigger picture.

The road ahead is filled with complexity and exciting developments. And yet, for all the focus on new technology, there are still large groups of customers who care little for new tech, and who continue to value practicality, build quality, and affordability above all else. How organizations address these oft-divergent customer desires within their product portfolio will be a challenge for many ‘traditional’ OEMs.

What we can say with confidence is that mobility experiences of the future – whether they’re autonomous or human-driven – must be satisfying, safe, and secure. Automotive companies must be quick to give their customers what they want. Check out our perspective on software in automotive to learn more. 

Understanding the impact of the revolution

Capgemini defines generative AI as a technology with the capability to learn and reapply the properties and patterns of data for a wide range of applications, from creating text, images, and videos in different styles to generating tailored content. It enables machines to perform creative tasks previously thought exclusive to humans.

Capgemini Research Institute reports nearly all (96%) executives say generative AI is a hot topic of discussion in their boardrooms. Across organizations in every sector, digital and business leaders are talking about how generative AI might be applied to a series of use cases from customer engagement to sales processes and onto operational activities.

It’s important to recognize that generative AI is more than just chatter. Gartner® published that in a recent Gartner, Inc. poll of more than 2,500 executive leaders, 45% reported that the publicity of ChatGPT has prompted them to increase artificial intelligence (AI) investments. Seventy percent of executives said that their organization is in investigation and exploration mode with generative AI, while 19% are in pilot or production mode.[1]

The rate of investigation is uppermost at high-tech companies. Where OpenAI led with its work on ChatGPT, other vendors are now following quickly in their footsteps. Capgemini Research Institute reports 86% of organizations are either working on generative AI pilots or have already enabled functionality.

Attend any technology conference today and you’ll hear a barrage of AI-related product launches. Fearful of falling behind their competitors, technology vendors are fighting to grab a piece of the generative AI action. The technology market is being flooded with standalone tools and AI-enabled additions to existing systems and services.

Panning for nuggets amongst this AI gold rush is an intractable challenge. At work, curious staff are beginning to use generative AI in their everyday activities and executives are looking at ways to harness that momentum.

While some of these tools could lead to huge boosts in productivity, it’s crucial to understand how these tools exploit data and how they might operate as part of an integrated technology stack. Today, we believe this deeper awareness is lacking. The result is a series of major hurdles that are associated with publicly available generative AI models:

• Too disjointed – They don’t address the needs for risk, privacy, and business controls.
• Too universal – They don’t understand business knowledge and cultural context.
• Too uncontrollable – They don’t have mechanisms to control the quality of outputs.
• Too risky – They don’t prevent third parties from reading and learning data.
• Too immature – They don’t have a built-in and enterprise-scale technology stack.

Developing an awareness of what’s next and what’s possible

At Capgemini, we recognize the potential benefits of generative AI are undeniable, yet so are the potential risks that come from unregulated deployment. Business leaders can’t afford to let the technology be implemented without due cause and consideration. In this fast-moving area of innovation, it’s vital to establish what’s coming next and what might be possible.

Capgemini’s dedicated Generative AI Lab is working to develop this important insight. Our group-wide effort aims to understand developments and advances in AI. With the rapid rise of generative AI, we expect the pace of change to quicken further. We anticipate both impactful breakthroughs in capabilities and unforeseen challenges and applications.

The Lab orchestrates our efforts to make sense of this revolution. We develop thought leadership, research, and internal readiness in this emerging area, allowing the wider group to develop a strong sense of how generative AI will affect all businesses today, tomorrow, and long into the future. The Lab’s work concentrates on two key horizons:

1. Internally, we provide a lighthouse effect on what’s coming next in generative AI. We develop an awareness of the key capabilities that are required, providing an early warning to our group of any major changes that are emerging.
2. Externally, we present industry-leading thought leadership on the opportunities and challenges from advances in generative AI. We undertake research and development alongside partners and academics, establishing practical responses.

Our Lab is staffed by a dedicated team of Capgemini AI experts from around the world. While the rush to implement generative AI is a recent trend, the technology itself has been a long time in gestation. During this period, Capgemini has worked with clients on AI across multiple sectors, including life sciences, consumer products and retail, and financial services.

We’ve helped a life science company re-sequence DNA and we’ve supported banks as they’ve used generative AI to translate old software into modern languages. We’ve worked on documentation for highly complex engineering products, and we’ve partnered with an insurance firm as its uses natural language to provide accurate answers to non-technical staff.

The experts in our Generative AI Lab will draw on these experiences and develop internal knowledge and external responses as professionals continue to explore emerging technology.

Conclusion: Rewards without the risks

The rapid rise of generative AI brings excitement and concern in equal measure. However, three-quarters (74%) of executives believe the benefits of generative AI outweigh the risks, according to the Capgemini Research Institute. Our Generative AI Lab has been created to identify pathways to a brighter, AI-enabled future. The Lab will work to clear the obstacles and build the bridges that will help us all reach this destination successfully.

[1] Gartner Press Release, Gartner Poll Finds 45% of Executives Say ChatGPT Has Prompted an Increase in AI Investment, May 3, 2023. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

What is Large Language Models (LLM)?

Artificial intelligence (AI) is a technological field that aims to give human intelligence capabilities to machines. A generative AI is an artificial intelligence that can generate content, such as text or images. Within generative AIs, foundation models are recent developments often described as the fundamental building blocks behind such applications as DALL-E or Midjourney. In the case of text-generating AI, these are referred to as Large Language Models (LLMs), of which the Generative Pre-trained Transformer (GPT) is one example made popular by ChatGPT. More complete definitions of these concepts are given in Figure 1 below.

The technological history of the ChatGPT LLM

In 2017, a team of researchers created a new type of model within Natural Language Processing (NLP) called Transformer. It achieved spectacular performance for sequential-data tasks, such as text or temporal data. By using a specific technology called ‘attention mechanism’, published in 2015, the Transformer model pushed the limits of previous models, particularly the length of texts processed and/or generated. 

In 2018, OpenAI created a model inspired by Transformer architecture (the decoder stack in particular). The main reason for this was that Transformer, with its properties of masked attention, excels in text generation. The result was the first Generative Pre-trained Transformer. The same year saw the release of BERT, a Google NLP model, which was also inspired by Transformers. Together, BERT and GPT launched the era of LLMs.  

Improving the performance of its model over BERT LLM variants, OpenAI released GPT-2 in 2019 and GPT-3 in 2020. These two models benefited from an important breakthrough: meta-learning models. Meta-learning is a paradigm of Machine Learning (ML) in which the model “learns how to learn.” For example, the model can respond to tasks other than those for which it has been trained.  

OpenAI’s aim is for GPT Large Language Models to be able to perform any NLP task with only an instruction and possibly a few examples. There would be no need for a specific database to train them for each task. OpenAI has succeeded in making meta-learning a strength, thanks to increasingly large architectures and databases massively retrieved from the Internet.  

To take its technology further, OpenAI moved beyond NLP by adapting its models for images. In 2021 and 2022, OpenAI published DALL-E 1 and DALL-E 2, two text-to-image generators.¹⁰ These generators enabled OpenAI to make GPT-4 a multi-modal model, one that can understand several types of data.  

Next, OpenAI released InstructGPT (GPT 3.5), which was designed to better meet user demands and mitigate risk. This was the version OpenAI launched in late 2022. But in March 2023, OpenAI released an even more powerful and secure version: the premium GPT-4. Unlike preceding versions, GPT-3.5 and GPT-4 gained strong commercial interest. OpenAI has now adopted a closed source ethos – no longer revealing how the models work – and become a lucrative company (it was originally a non-profit association). Looking to the future, we can expect OpenAI to try to push the idea of a prompt for all tasks and all types of data even further. 

Why is everyone talking about Large language models?

Only those currently living under a rock will not have heard something about ChatGPT in recent months. The fact that it made half the business world ecstatic and the other half anxious should tell you how popular it has become. But let’s take a closer look at the reasons why. 

OpenAI’s two remarkable feats­­

With the development of meta-learning, OpenAI created an ultra-versatile model capable of providing accurate responses to all kinds of requests – even those it has never encountered before. In fact, GPT-4 achieves better results on specific tasks than specialized models. 

In addition to the technological leaps, OpenAI has developed democratization. By deploying its technology in the form of an accessible chatbot (ChatGPT) with a simple interface, OpenAI has made it possible for everyone to utilize this powerful language model’s capabilities. This public access also enables OpenAI to collect more data and feedback used by the model.

Rapid adoption  

The rapid adoption of GPT technology via the ChatGPT LLM has been unprecedented. Never has an internet platform or technology been adopted so rapidly (see Figure 2). ChatGPT now boasts 200 million users and two billion visits per month.  

The number of Large Language Models is exploding, with competitors coming from Google (Bard), Meta (Llama), and HuggingFace (HuggingChat, a French open-source version). There is also a surge in new applications. For example, ChatGPT LLMs have been implemented in search engines and Auto-GPT, which latter turns GPT-4 into an autonomous agent. This remarkable progress is stimulating a new wave of research, with LLM publications growing exponentially (Figure 3).  

Opportunities, fantasies, and fears

The new standard established by GPT-4 has broadened the range of possible use cases. As a result, many institutions are looking to exploit them. For example, some hospitals are using them to improve and automate the extraction of medical conditions from patient records.  

On the other hand, these same breakthroughs in performance have given rise to a host of fears: job insecurity, exam cheating, privacy threats, etc. Many recent articles explore this growing anxiety, which now seems justified – Elon Musk and Geoffrey Hinton are just two of the many influential tech figures now raising the alarm, calling it a new ‘code red.’  

However, as is often the case with technological advances, people have trouble distinguishing between real risk and irrational fear (e.g., a world in which humans hide from robots like those in The Terminator). This example explores the creation of a model that rivals or surpasses the human brain. Of course, this is inextricably linked with the formation of consciousness. Here, it is worth noting that this latter fantasy is the ultimate goal of OpenAI, namely AGI (Artificial General Intelligence). 

Whether or not these events will remain fantasies or become realities, GPT-4 and the other Large Language Models’ AI are undoubtedly revolutionizing our society and represent a considerable technological milestone.

What can you do with an LLM?

Essentially, a ChatGPT LLM can:

1. Generate natural language content: Trained specifically for this purpose, this is where they excel. They strive to adhere to the given constraints as accurately as possible.
2. Reformulate content: This involves providing the LLM with a base text and instruction to perform tasks, such as summarizing, translating, substituting terms, or correcting errors.
3. Retrieve content: It is possible to request an LLM to search for and retrieve specific information based on a corpus of data.

How can you use an LLM?      

There are three possible applications of Large Language Models’ AI, summarized in Figure 4. The first one is direct application, where the LLM is only used for the tasks that it can perform. This is, a priori, the use case of a chatbot like ChatGPT, which directly implements GPT-4 technology. While this is one of the most common applications, it is also one of the riskiest. This is because the ChatGPT LLM often acts like a black box and is difficult to evaluate. 

One emerging use of LLMs is the auxiliary application. To limit risks, an LLM is implemented here as an auxiliary tool within a system. For example, in a search engine, an LLM can be used as an interface for presenting the results of a search. This use case was applied to the corpus of IPCC reports.¹⁹ The disadvantage here is that the LLM is far from being fully exploited.  

In the near future, the orchestral application of ChatGPT LLMs will consume much of the research budget for large organizations. In an orchestral application, the LLM is both the interface with the user and the brain of the system in which it is implemented. The LLM therefore understands the task, calls on auxiliary tools in its system (e.g., Wolfram Alpha for mathematical calculations), and then delivers the result. Here, the LLM acts less like a black box, but the risk assessment of such a system will also depend on the auxiliary tools. The best example to date is Auto-GPT.

Focusing on the use case of a Chatbot citing its sources

One specific use case that is emerging among our customers is that of a chatbot citing its sources. This is a response to the inability of Large Language Models’ AI to interpret results (i.e., the inability to understand which sources the LLM has used and why).

To delve into the technical details of the chatbot citing its sources (The relevant pattern – illustrated in Figure 5 – Is called Retrieval Augmented Generation or ‘RAG’), the model takes a user request as input, which the model then transforms into an embedding (i.e., a word or sentence vectorization that captures semantic and syntactic relationships). The model has a corpus of texts already transformed into embeddings. The goal is then to find the embeddings within the corpus that are closest to the query embedding. This usually involves techniques that find the nearest neighbours’ algorithms. Once we have identified the corpus elements that can help with the response, we can pass them to an LLM to synthesize the answer. Alongside the response, we can provide the elements that were used to generate it. The LLM then serves as an interface for presenting the search engine’s results. This ‘RAG’ approach therefore facilitates the decoupling of factual information provided by the sources from the semantic analysis provided by the LLM, leading to better auditability of the results provided by the Chatbot.  

Read more in Auditing ChatGPT – part II