Beyond the ChatGPT effect, how is generative AI revolutionizing industries? Examples and implications

What happened

Generative AI has been making headlines for the past few months thanks to the hype surrounding ChatGPT. Following our latest thematic event (ChatGPT & Co: What are those AIs banging at my door?) and a series of client meetings, we recently published a note that deep-dived into the technology, its limits, why it is not sentient, and its immediate and potential applications. 

To continue on the same path, we are presenting here how the latest evolution of the AI technology will impact and transform our investment themes.

Impact on our Investment Case

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Generative AI already impacting the theme 

By design, generative AI is already having a substantial impact on the AI & Robotics theme, which was launched precisely to address the ecosystem of players enabling this type of technology. Training large AI models requires a significant amount of computing power, primarily provided by GPUs. NVIDIA (the current top exposure of the theme) has a de facto monopoly in the Machine Learning segment, thanks to its combination of innovation and proprietary application programming interface (API), which has become an industry standard. This computing power needs to be easily accessible, hence the recourse to cloud-based solutions. As a result, spending into data center infrastructure continues unabated. A derived impact is also felt on the semiconductor manufacturing supply chain, as such chips require cutting-edge technology. AI models also require large amounts of data to be accurate, hence the need for data management systems, which are also present in our investment universe and portfolio. 

A shift bound to accelerate

ChatGPT has brought AI into the mainstream and put the technology on everyone's radar. Its simplicity and concrete applications will drive its widespread adoption and accelerate the shift towards its ubiquity, faster than anticipated. The productivity gains of Generative AI will be substantial, automating cumbersome processes and allowing users to focus on real added value. This will create a strong tailwind for automation systems such as those present in the Robotic Process Automation sector (e.g., UiPath Inc). However, companies may find AI systems too complex to deploy, creating opportunities for "enablers". These facilitators will witness significant growth if they execute correctly.

An AI for an AI

With AI experiencing its "iPhone moment," it's difficult to comprehend how much the theme will change in the future, given that it is enabling this revolution. One exciting possibility is that AI may soon create AI on an automated basis, thanks to the code-generation capabilities of Generative AI. This would enable robots to become truly collaborative, by allowing their programming to be updated and fine-tuned through simple speech instructions, much like 3D printing. The potential for growth in this theme is enormous, and it is sure to become a must-have in the years to come.

New technologies meet

Blockchain technology may play a major role in the development and adoption of generative AI. Training AI algorithms relies on vast amounts of data, with concerns arising about the integrity and privacy of this data. Blockchain offers the ability to secure data and improve its integrity through transparency, immutability, and auditability. This is essential for generating reliable content, which is critical for the adoption of generative AI. 

Blockchain-based digital content

The relationship between AI and blockchain is not limited to the model training. The content created by generative AI applications can also rely on blockchain technology. Actually, the development of a web 3.0 where users regain control over their data will rely on both AI and blockchain.

Are you using AI to create digital content like a photo or a video? A non-fungible token (NFT) can be created at the same time. The abundance of personalized digital content will grow exponentially, even if questions over the commercialization rights of such NFTs still have to be answered.

This can apply to any type of contents. The gaming industry is also on the verge of changes in user experience. It will be very easy for gamers to create avatars and characters with AI, which can then be tokenized on the blockchain. The development of digital twins will be facilitated.

Developing blockchain applications

We have argued since our strategy launch that the infrastructure related to blockchain technology is being quickly developed. As a result, we are about to experience the next phase of the cycle, related to the mass development of applications. Generative AI is likely to accelerate this phase, as the programming, debugging and audit of applications will (1) require less coding knowledge, and (2) be faster.

In parallel to the development of dApps and smart contracts, we can expect a surge in the number of tokens and digital assets available for investors. Competition between token projects will increase, which could lead to a similar boom as the initial coin offering of 2017 – for the better or the worse. Tokenomics, the study of crypto supply and demand, still have good days ahead, even if we can expect an automated review and valuation of the crypto assets thanks to AI-based algorithms (like for traditional assets). 

AI is pushing the boundaries of personalized, precise and efficient care

Imagine a world where medical devices are not just tools, but personal assistants that seamlessly integrate into our lives, predicting and preventing health issues before they even arise. This vision might not be so far away after all, thanks to the transformative power of AI. By analyzing vast amounts of data and identifying patterns and correlations, AI is enabling medical devices to deliver care that is truly personalized to each individual's needs, in an accurate and efficient way. From prosthetics that learn and adapt to their user's movements, to AI-powered conversations that provide mental health support, this technology is impacting every facet of the medical device industry.

Generative AI can be used to generate synthetic data for machine learning algorithms that researchers and companies can use to test and refine these devices without risking the health of patients, significantly accelerating the pace of innovation. Deep learning algorithms can leverage the power of generative AI to improve the performance of diagnostics, remote monitoring and prevention. Generative models will also improve the interaction with patients or healthcare providers through speech or text, helping streamline the healthcare process. Companies that have to deal with large amounts of data to generate valuable medical insights are likely to benefit the most from these emerging technologies.

From reactive to proactive

Supervised learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been a game-changer in the field of diagnostics. With their unparalleled ability to analyze vast amounts of data and identify patterns and relationships, it has transformed the areas of liquid biopsy, genetic testing, and medical imaging, among other areas. In liquid biopsy, AI already allows to detect genetic mutations and predict treatment response. In genetic testing, it helps interpret complex genomic data, enabling earlier diagnosis and targeted treatment. In medical imaging, it supports the detection of abnormalities that might be missed by human interpretation, facilitating earlier and more accurate diagnoses.

Generative AI is taking this field to another level, by filling in gaps or generating additional data points it can improve diagnostic accuracy. This is particularly valuable in cases where patient data is limited or incomplete, allowing healthcare providers to make more informed decisions and deliver better patient outcomes. Zebra Medical (acquired by NanoX) and PathAI both use generative AI in their medical imaging.

Bringing a new level of sophistication

By learning from data and making predictions based on patterns and insights that may not be immediately obvious to human designers, generative AI has the potential to bring a new level of sophistication and personalization to prosthetic devices and other medical implants. By processing data from sensors and the patient's body, generative AI can optimize prosthetic devices to match patients' unique needs and preferences, providing a more natural experience. It can also improve the accuracy and reliability of medical implants, detect potential issues, and enable remote monitoring for better coordination of care. As an example, Stryker is a medical device company that uses generative AI to design and develop implants for joint replacements. 

Meet the new medical assistant

Another exciting possibility of AI is its potential to transform care management solutions and telemedicine by providing real-time analysis of patient data and automating certain processes, such as handling appointments and summarizing medical records. This could make healthcare more efficient, alleviating some of the burden on medical staff. One real-world example is Microsoft's Nuance Communications, which plans to integrated OpenAI's GPT-4 natural language processing software into its medical platform. This would improve the platform's ability to capture complex medical terminology more effectively, summarizing clinical notes for integration into electronic medical records.

Biotech is one of the longest standing generative AI fields. 

Biotech is one of the longest-standing fields in generative AI. One of the first applications of generative AI was reported in 1996 during a protein structure challenge, using a neural network to predict protein secondary structure. Scientists have been tackling this problem from all angles for one reason: all life activity is based on protein activity, which is defined by their structure. Mastering protein structure is tantamount to mastering life as a biological process. While ChatGPT has been on everyone's lips since November 2022, Google achieved the "T" in GPT in July 2021 with a general solution to the protein structure prediction problem. In two years, the two papers published became top 0.1% cited articles. From food to clothing, biofuels, plastics, and health, all industries will be revolutionized. Health is probably going to be the first.

Drug research in the starting block

Pharma R&D ROI has plummeted over the last 30 years, with a cost of $2bn per drug, 12 years of R&D, and a 90% failure rate. However, more biomedical data has been produced in the last year than in the previous 300 years, and generative AI is the only way to process this vast amount of data. Over the last seven years, more than 25 Generative Adversarial Networks (GANs) have been presented for drug discovery, and generative AI in biopharma is expected to reach $9 billion by 2027 (47% 5-year CAGR). By 2030, all molecules in clinics could be AI-designed or optimized.

Clinical trials protocols will be boosted

Patient selection, dosage, and protocol can be reasons for clinical trial failures, not just the drug itself. Even water given above the kidney’s filtration capacity (0.8-1.0 L/hour) can quickly be fatal. NLP-based AI allows efficient data extraction from unstructured sources that are often difficult to exploit, such as clinician notes, patient entries, social media data, etc. The retrieved data can be used as input into ML algorithms to make predictions on patient response and dosage, thus limiting clinical trial failures. While big pharma can absorb failures to some extent, smaller biotechs can often go bankrupt after a single late-stage failure.

Rare disease research in particular will benefit

Ironically, AI powered by real-world data is two and a half times better than a simple epidemiological study for the detection of rare diseases due to a lower bias to discard unexpected symptoms. By reducing the average 6-year diagnostic delay for rare diseases, clinical trials could enroll more patients and provide much better care, saving the healthcare system vast amounts of unnecessary treatments. Rare disease research, in particular, will benefit greatly from Generative AI, as it can analyze vast amounts of data to identify patterns and relationships that would be difficult to detect otherwise.

Securing the Cyberspace

The cybersecurity sector is another area being significantly disrupted by generative AI. Apart from its ability to detect and respond to cyber threats just like to other security threats in real-time, AI can analyze network traffic, detect suspicious activity, and recognize impostors trying to hack into a system. Generative AI could also be used to improve the accuracy of cyber threat intelligence. By analyzing large amounts of data, generative AI could identify patterns and trends that would be difficult or impossible for humans to detect, ensuring that cybersecurity experts need to intervene only for the highest level attacks where human intervention is absolutely necessary.

The technology can also be used to develop more sophisticated authentication and identity verification systems. For example, facial recognition and other biometric authentication systems powered by generative AI can be more difficult to spoof than traditional authentication methods, which have already showed on multiple occasions their weak points, e.g., hackers could imitate voice and speech patterns by obtaining a short video of a victim speaking.

Access to data

Fintech relies on advanced technology to offer more personalized financial services – or at least, this has been the promise of the industry. Generative AI and next-level chatbots are about to make this a reality and have an impact on all steps of the manufacturing of a financial service product.

As opposed to other industries, financial organizations have an edge: they have access to tons of data to train models. While many incumbents have publicly announced banning ChatGPT, we expect the same organizations to develop their own models. The privacy limitations on financial data can be overruled by the use of synthetic data that preserves statistical quality. Private companies such as Hazy offer such services to major incumbents.

Personalized financial services

The analysis of spend patterns and life stage will allow financial organizations to auto-generate loan offers or investment suggestions. Product comparison can be automated, as well as the answers to clients’ questions regarding an offering. Everyone will have access to a digital personal banker. Combined with next-gen chatbots, robo-advisors could finally provide some tailored engagement with clients and provide more authentic, quasi-human-like, interactions.

The clients will not be the only ones impacted by generative AI. Labor-intensive processes will be significantly optimized within financial organizations. AI algorithms will reduce the administrative burden by verifying documents, validating information, or drafting presentations and investment proposals. Productivity will increase, even for tasks that have no tolerance for fault. For such tasks, generative AI will assist humans, improving people’s efficiency and accuracy.

Reinventing financial analysis and accounting

The number of bookkeepers and accounting clerks has been declining since the development of spreadsheets and accounting software in the early 1980s. Future generative AI models will have the potential to accelerate the recording of transactions. Accountants, which are extracting information from financial data, will also see their role and tasks evolve.

Dedicated AI-based models to perform financial analysis and identify investment opportunities will also appear. We expect our own investment process to be adjusted to such new tools. Surfing on the hype wave, Bloomberg already announced the development of a BloombergGPT. But these changes do not only apply to financial professionals. Even people with no financial knowledge will be able to know if a balance sheet seems solid. As a consequence, we could see some sort of standardization in balance sheet of listed companies that will want to pass the automated test. Forensic financial analysis will have to adapt to this new paradigm.

You do not know what to buy ? Ask for recommendations ! 

E-commerce platforms have been heavy users of artificial intelligence for years. For instance, the success of Amazon has a lot to do with its product recommendation algorithm that enhances the user experience from product discovery to checkout. Generative AI will bring the product recommendation process to the next level, given the possibility to analyze a greater amount of data to learn about customers’ preferences, purchase history, and behavior. This should lead to increased customer satisfaction and higher conversion rates, which will imply more volume processed and more revenue for payment providers. Chatbots will evolve towards real virtual assistants that can provide customer support and personalized product recommendations. Klarna, the leading buy now pay later company, understood this potential as it was among the first companies to propose a ChatGPT plugin that facilitates product comparison and provides direct links to recommended products.

Improving payment experience

Payment processors will have an important role to ensure that a recommended product purchase can be concluded in a seamless way. Similarly to the analysis of purchase history, algorithms will analyze transaction history to recommend personalized payment options. Concretely, depending on the type of goods purchased, the amount spent, the day of the month, and past behavior, platforms will suggest the most relevant payment option (bank transfer, debit card, credit solution like credit cards or installment payments, etc.). 

Patterns in customer behavior will provide insights to businesses, allowing them to optimize their payment processes. Dynamic pricing and yield management (i.e., variable pricing strategy to influence consumer behavior to maximize revenue) are entering a new era. 

Fraud detection

Detecting and preventing payment fraud is essential, especially in the rapidly growing e-commerce sector. According to Juniper Research, global losses due to online payment fraud reached $41bn in 2022, and this amount is expected to increase by 15-20% in 2023. Generative AI algorithms can play a critical role in identifying and preventing payment fraud by analyzing patterns in payment data to identify suspicious transactions. These algorithms can help reduce various types of fraud, including credit card fraud, account takeover, and identity theft.

Revolutionizing the security industry

Security and space sectors will be significantly impacted by Generative AI thanks to its ability to create new data based on existing patterns or data sets.

As we have mentioned before, one of the most significant benefits of this technology is its ability to analyze large amounts of data, including video footage and sensor data, in real time, thus automating security operations and decreasing the workload of existing staff. This can help security personnel to detect potential threats or incidents faster and respond more quickly. For example, generative AI could be used to analyze CCTV footage, detect unusual behavior, and generate responses to routine security incidents, reducing the time and effort required to resolve them.

Generative AI can also be used to create simulations to train security personnel in various scenarios. This can help them to be better prepared for emergencies and to respond more effectively to real-world situations. Additionally, generative AI can be used to develop predictive models to anticipate potential security threats and prevent them before they occur.

Propelling the space industry forward

Generative AI also has the potential to transform the space sector by improving the efficiency of space missions and reducing costs. For example, generative AI could be used to optimize the trajectory of spacecraft, reducing the time and resources required to reach their destination. It could also be used to simulate spacecraft behavior in different environments, allowing engineers to identify potential problems before they occur. Generative AI could also be used to improve the safety of space missions. For example, it could be used to detect and prevent collisions between spacecraft or to predict space weather conditions that could impact the mission.

The Dark Side of Generative AI

While generative AI can be beneficial, it also raises concerns about privacy and civil liberties. There is a risk that the technology could be used to profile individuals based on their behavior or other characteristics. Another concern is the potential for generative AI to create "deepfakes" or other forms of fake content, which could be used to spread misinformation or manipulate public opinion. This could have major implications for national security, particularly if used to influence elections or other important political events. At the same time, the solution to such an issue may lie in using exactly the same technology: generative AI is already able to detect and recognize AI-generated content.

Saving energy

AI has been a hot topic in the cleantech sector for quite some time. Its applications range from optimizing energy management, predicting consumption patterns, balancing supply and demand, to developing predictive maintenance for renewable assets.

By processing data from multiple sources, such as smart meters, weather forecasts, and historical energy consumption patterns, AI-based algorithms can help predicting energy demand, identifying peak periods and hence allow energy providers to adjust supply accordingly. This can help to reduce energy wastage, save costs, and improve system efficiency. On the demand side, AI-powered systems can automatically adjust temperature, lighting, and ventilation based on occupancy patterns and weather conditions.

In the realm of wind energy, AI can be used for predictive maintenance and condition monitoring of wind turbines. By analyzing sensor data, such as wind speed, turbine rotation speed, vibration, oil temperature, and more, AI can detect patterns and identify potential issues before they damage the turbine. This results in reduced downtime, lower maintenance costs, and increased overall efficiency of wind power generation.

Materializing innovation

The recent surge in generative AI has the potential to elevate AI applications to new heights. Generative AI specializes in creating novel designs, patterns, and solutions, which can be used in the development and optimization of cleantech solutions.

One such example is the discovery of advanced materials for clean energy applications. Generative AI can accelerate material discovery by simulating countless iterations of material properties, eventually identifying optimal candidates for use in energy-efficient construction materials, or next-generation batteries. Additionally, generative AI can be employed to envision new sustainable designs for buildings, featuring innovative integration of solar panels, carbon-absorbing walls, passive cooling, and more. It can also play a role in carbon capture and utilization technologies (CCUS) by optimizing chemical reactions and processes to enable more efficient methods of capturing and converting CO2 into useful products.

Our Takeaway

Generative AI is a technology that is having an impact on different industries and creating opportunities for innovation and growth. The examples presented in the document illustrate the potential benefits of this technology and the possibilities it presents.

Moreover, generative AI is influencing the competitive landscape, creating new markets, and encouraging innovation. As such, this technology has the potential to contribute to economic growth and create new jobs.

It is important to acknowledge that there are challenges and risks associated with the use of generative AI, such as the need for regulation and ethical considerations. However, with responsible and sustainable use, generative AI can be a valuable tool for businesses and society, unlocking possibilities and opportunities for the future.