Will AI and digital technologies exacerbate or lessen the impending polycrisis of 2024? This is the primary question underlying these predictions. In 2024 AI will continue to dominate the tech landscape. It will be less turbulent than last year when AI went through 4 seasons, from the winter of AI excitement to an autumn of clarity. AI technology will grow steadily, with more clarity and some disillusionment. We will also return to digital basics in 2024. For instance, we frequently forget that access to the internet is not a given. It is comparable to water or electricity services. When it breaks, we become aware of it. We experience our digital reality, including access to the cutting-edge ChatGPT, through the decades-old Internet protocol – TCP/IP. Thus, in addition to AI, we will discuss traditional digital issues like infrastructure and standardisation, cybersecurity, content governance, and the digital economy in our 2024 predictions.
Jovan and Diplo Team
Artificial Intelligence | Geopolitics | Governance | Diplomacy | Security | Human Rights | Economy | Standards | Encryption | Identity | Content | Inclusion
In 2024, we can anticipate steady growth, some disillusionment, and more clarity in the AI field.
AI technology will continue to grow both deeper, through powerful foundational models, and wider, through more connections to the rest of the digital ecosystem (e.g. the internet of things, virtual reality, digital infrastructures). In parallel, smaller AI and open source models will gain momentum as a more transparent, adaptable and environmentally friendly approach to AI.
In 2024, Google will try to challenge ChatGPT’s dominance. It remains to be seen whether the AI space will be bipolar, with OpenAI/Microsoft and Google, or multipolar, with more actors joining at the top. In addition to Apple, Amazon, and other companies, the most interesting challenge for duopoly comes from Meta, which has been supporting the open-source LLaMA model.
Large Multimodal Models (LMMs) will gain in relevance following the shift from text towards integrating video, images, and sound in AI foundation models.
After realising the strategic relevance of AI-codified knowledge, companies, communities and countries will intensify the development of bottom-up AI based on in-house solutions. AI models will be localised through the use of data pruning, adaptive computing, and models for retrieval augmented generation (RAG). Bottom-up AI will become a new AI dynamic in 2024.
Small Language Models (SLMs) are already a thing, but cost-efficiency and sustainability considerations will accelerate this trend as AI has a big carbon footprint due to the use of a lot of energy for processing data and the development of foundational models.
After surviving an attack from the ‘extinction risk’ camp in 2023, open-source AI will continue to develop fast in the new year. Open-source AI tends to be smaller but more adaptable and customisable. LLaMA is 10 times smaller than OpenAI Large Language Model (LLM). If LLaMA or other smaller and open-source models (BLOOM and OPT) can achieve similar results to GPT-4, it can be a major shift in the AI landscape with more inclusion for organisations and individuals with much fewer resources.
Following a lot of hype in 2023 about AI replacing everything, including humans, we can anticipate disillusionment in 2024. The application of AI will be put to the test in the workplace, in the classroom, in the entertainment industry, and in other spheres of human endeavour (see: the Gartner hype disillusionment slope).
Businesses will face the reality that AI per se won’t bring exponential growth in productivity. AI may help businesses if they start doing their jobs differently. AI is not a silver bullet for businesses, but a catalyst of changes. In addition, users’ tolerance for AI hallucinations in the early days of ChatGPT will decrease as expectations will rise.
There will be more clarity in discussions about governing AI and dealing with associated risks. Existing risks (e.g., jobs, misinformation, biases) will receive more attention than existential risks (human survival). Exclusion risks based on a few AI monopoly companies will become more relevant. AI governance will become more specific and concrete, addressing computational power, data and knowledge, algorithms, and AI applications.
AI risks will dominate the governance and regulatory debate. The main shift will be from a heavy focus on extinction and long-term risks towards existing risks that AI poses to society, from misinformation to education and the future of jobs. In addition, governments will focus more on exclusion risks triggered by the AI monopolies of the major tech companies.
The end of 2023, with the adoption of the EU AI Act and the publishing of the Interim Report by the UN Secretary-General’s High-level Advisory Body on AI, signalled more clarity in dealing with AI risks. The previous heavy focus on extinction risk is balanced with existing risks and midterm risks, such as monopolies of AI platforms.
AI Risk Coverage in 2023 Prediction of AI Risk Coverage in 2024
Existing risks (short-term) from AI developments include loss of jobs, protection of data and intellectual property, loss of human agency, mass generation of fake texts, videos, and sounds, misuse of AI in education processes, and new cybersecurity threats. We are familiar with most of these risks, and while existing regulatory tools can often be used to address them, more concerted efforts are needed in this regard.
Exclusion risks (medium-term) could be triggered by the centralisation of AI knowledge in the hands of a few powerful players. Ultimately, their monopolies could create a risk of exclusion: citizens, communities, and countries worldwide will be limited in their ability to use and benefit from common knowledge. Such monopolies could lead to a dystopian future for humanity. Legally speaking, risks of such AI monopolies can be reduced via antitrust regulation and protection of data and intellectual property associated with the knowledge used for the development of AI models.
Extinction risks (long-term) are based on the possibility of AI evolving from servant to master, jeopardising humanity’s very survival. After very intensive doomsday media coverage throughout 2023, these threats haunt the collective psyche and dominate the global narrative with analogies to nuclear armageddon, pandemics, or climate cataclysms.
The dominance of extinction risks in the media has influenced policy-making. For example, the Bletchley Declaration adopted during the London AI Safety Summit heavily focuses on extinction risks while mentioning existing AI risks in passing and making no reference to exclusion risks.
The AI governance debate ahead of us will require: (a) addressing all risks comprehensively, and (b) whenever it is required to prioritise them, that decisions be made in transparent and informed ways.
Dealing with risks is nothing new for humanity, even if AI risks are new. In the environment and climate fields, there is a whole spectrum of regulatory tools and approaches, such as using precautionary principles, scenario building, and regulatory sandboxes. The key is that AI risks require transparent trade-offs and constant revisits based on technological developments and society’s responses.
The main question is access to powerful hardware that processes the AI models. In the race for computational power, two key players—the USA and China—try to limit each others’ access to semiconductors that can be used in AI. The key actor is Nvidia, which manufactures graphical processing units (GPU) critical for running AI models.
With the support of advanced economies, the USA has an advantage over China in semiconductors, which they try to preserve by limiting access to these technologies via sanctions and other restriction mechanisms.
There are two sets of challenges: firstly, copyright holders worldwide are questioning in courts the use of their creative work in the development of AI; secondly, facing a limited data volume of internet content and in an attempt to respond to privacy and intellectual property concerns, companies are using synthetic data generated by AI. This recursive loop triggers ‘AI inbreeding’ and an inevitable degeneration of the quality and relevance of AI models.
Copyright: In 2023, the question of protecting the data and intellectual property used by AI platforms started gaining momentum. The EU AI Act requires more transparency about data used for AI models. Writers, musicians, and the photography industry started court cases against OpenAI for use of their intellectual property for training AI models. The New York Times’ lawsuit against OpenAI for copyright infringement is one recent example. Other companies, however, are watching and learning valuable lessons: Apple decided to conclude a $50m multi-year deal with the Times to license material for its AI models.
In 2024, new ideas will flourish. For example, some lawyers argue that, analogous with copyright, a ‘learnright’ should be established to govern how AI uses content for training. Various tagging and watermark systems are discussed for distinguishing human-made from AI-made artefacts.
There are also arguments against strict copyright protection and the need to allow data scrapping in order to facilitate progress of AI.
Synthetic data: Most existing models are based on internet content, considered as the ‘ground truth’ for AI development. This initial input into AI models is already depleted. In 2024, there will be two major problems with acquiring data. Facing limited available data, AI companies started using synthetic data generated by AI. According to Stanford University researchers there is a 68% increase in synthetic data generated by AI on Reddit.
In 2024, discussion on transparency, evaluation, and explainability will need to operationalised as it will be required by the EU AI Act and other legal instruments in the field of AI. AI governance will also focus on the relevance of ‘weights’ in developing AI models: how to highlight the relevance of some input data and knowledge in generating AI responses.
Transparency: There is very little transparency when it comes to AI models; we know little to nothing about, for instance, the data fed into the model or the weights that are given to parameters. Transparency is a precondition for evaluation and, ultimately, accountability in the digital realm. The lack of transparency in AI follows a general trend of reduced transparency in the digital industry and social media platforms. Twitter, Facebook, and Google provide less and less access to their operations.
Evaluation: The EU AI Act and other regulations require the evaluation of AI platforms. However, there are numerous limitations in conducting evaluation, from lack of data to lack of common evaluation methodologies. In the USA, bipartisan efforts in Congress, such as the ‘AI Research, Innovation, and Accountability Act,’ aim to increase transparency and accountability for high-risk AI applications.
The focus is on the practical use of AI via apps and other services. For example, regulations would focus on the implications that the outputs of systems such as ChatGPT have in terms of human rights, security, and consumer protection instead of regulating the algorithms that generate such outputs. Like with traditional digital systems, responsibility for obeying regulation will be placed on companies that develop and operate AI systems, such as OpenAI/Microsoft, Google, Meta, and Amazon.
For a long time, one of the pillars of digital governance has been to regulate the uses and outputs of digital systems instead of regulating how the internet functions technically (from standards to the operation of critical internet resources like internet protocol numbers or the domain name system). This approach is one of the main contributors to the fast growth of the internet. The current calls to shift regulations on the algorithm level (under the bonnet of technology) would be a major departure from the well-tested and established approach of technology regulation, with far-reaching consequences for technological progress.
The digital decoupling between China and the United States will accelerate significantly in 2024 along a number of strategic axes, including semiconductors, satellites, artificial intelligence, data, and submarine cables. India, Brazil, South Africa, Singapore, Turkey, and Gulf states, among others, will try to carve an asymmetric ‘third digital space’ between the two superpowers. In 2024, the push for national sovereignty over data, AI, and technology infrastructure will reshape digital geopolitics.
In 2024, semiconductors will remain the main digital battleground between China and the USA. So far, the USA-led restrictions on the export of chip-making technology have triggered rapid growth in this sector in China. Alibaba, Xiaomi, Baidu, and other Chinese companies invest heavily in the local semiconductor industry. The same tendencies can be observed worldwide as countries try to become self-reliant in the development and production of advanced chips.
The previously integrated network of submarine cables will continue separating between China and the USA. We have already seen, for instance, that the USA banned landing rights for a few submarine cables with links to China. And China started investing in separate cable systems, such as a new cable connecting China with France via Singapore, Pakistan, and Egypt.
Faced with these divisions, countries will focus on digital sustainability by developing alternative digital routes. They will also work more on preserving content locally via internet exchange points (IXPs).
Outer space is another field of accelerated competition between the USA and China, as well as other public and private actors. In the area of internet connectivity alone, low-orbit satellite (mega)constellations by the likes of SpaceX’s Starlink and Amazon’s Project Kuiper are to face competition from China Satellite Network Group’s Gouwang project.
National space agencies and private actors alike are planning or working on deploying (new) communications and navigation networks (e.g. around the Moon), the development of new space stations, and the exploration and exploitation of space resources (e.g. metals, minerals), giving rise to new governance issues.
Against this backdrop, ensuring a fair and responsible allocation and use of spectrum resources and orbital positions, encouraging responsible behaviour in outer space from public and private actors, and ensuring the sustainability of space activities, will be some of the questions that UN bodies and processes—from the Committee on the Peaceful Uses of Outer Space (COPUOS) and the International Telecommunication Union (ITU), to the Summit of the Future—will be addressing in 2024.
The 2023 WTO World Trade Report noted that Sino-American tensions are contributing to pre-existing fragmentation trends in the world economy. This is leading to a search for economic independence (instead of interdependence) in sensitive areas, and to a reorientation of trade flows along geopolitical divides. This finding is particularly concerning due to the central position occupied by the United States and China in the global economy, creating pressure for their trade partners to position themselves along the fault lines.
The relations that the EU nurtures with both sides, particularly when implementing its Economic Security Strategy will be key to counter—or at least to mitigate—fragmentation trends. Simultaneous politico-ideological and value chain fragmentation would mean that divides would become harder to bridge in the future, with negative consequences for markets and technological interoperability.
In 2024, countries and regional blocs such as the EU will also push for more self-reliance in terms of digital developments. Initiatives focused on achieving digital, data, AI, or cyber sovereignty are often motivated by a desire to reduce the risks of negative security and economic spillovers from integrated digital networks.
The sovereignty drive takes different forms. Sometimes, it is about control of infrastructure. In other cases, it is about preserving data on national territory. Increasingly, it is about facilitating national AI developments.
Approaches to digital sovereignty will vary, depending on a country’s political and legal systems. Legal approaches include national regulation and court judgments; technical ones vary between data filtering and frowned-upon internet shutdowns.
A focus on digital sovereignty reduces the appetite for global digital governance solutions. In one illustration of this, the recent withdrawal of the USA from the WTO e-commerce negotiations was justified by the need to gain more room for regulating tech companies nationally.
In 2024, AI will accelerate changes in the economy, from restructuring traditional industries to developing new ones built around AI technology. The main policy dynamics will be related to the economic consequences of the digital decoupling (or de-risking) between China and the USA, anti-monopolies in the field of AI, taxation of online industries, and digital trade.
In 2024, EU’s Digital Market Act (DMA) will provide the legal basis for a push for interoperability among social media platforms. New solutions should make it easier for users to switch from platform to platform without losing previous networks of people. They will also prevent usability-driven monopolies of the major social media platforms.
New risks related to AI come from the high concentration of AI solutions, data, and knowledge in the hands of a few companies. Microsoft’s investment in OpenAI triggered an investigation by US and UK anti-monopoly authorities. More such antitrust investigations are likely to be launched in 2024.
June 2024 is the next deadline for concluding the OECD’s negotiations on taxing cross-border digital services (Amount A of Pillar One of OECD negotiations). The main controversy is around a complex formula for reallocating taxes among the major tech companies. The outcome of negotiations will have far-reaching consequences for the digital economy. Many countries have paused their unilateral digital service taxes until the OECD completes the negotiation process.
At the beginning of the year, all eyes will turn to the 13th WTO Ministerial, which will take place from 26 to 29 February in Abu Dhabi. The Joint Statement on e-commerce, a plurilateral negotiating process currently ongoing among 90 WTO members, is likely to achieve a ‘partial delivery’. While preliminary agreement has been reached on several topics, such as Paperless trading, Open government data, Online consumer protection, Cybersecurity, Open Internet access (Net Neutrality), and Personal data protection, the most controversial issues remain unresolved. This is the case of negotiations on data flows, which have suffered a significant setback following the USA’s decision to withdraw from negotiations on this topic, under the justification of preserving domestic policy space on data regulation. The 13th WTO Ministerial is likely to produce an agreement on e-commerce, but significantly less ambitious than initially foreseen.
Digital Economy Agreements (DEAs) will likely continue to hit the headlines. This digital-only type of free trade agreement is being concluded by countries around the world, and notably in the Asia-Pacific. DEAs not only deal with traditional issues, such as customs duties, online consumer protection, and electronic authentication, but also tackle emerging trends and technologies that are not yet considered to be ‘treaty-ready’, such as digital identities and AI, establishing platforms for collaboration and harmonization. As multilateral discussions become increasingly stalled, the role and importance of DEAs in digital governance is likely to grow in 2024.
Social media companies are becoming less and less transparent about their activities and business models. Most of them stopped and reduced access to their data to independent researchers. For example, X (Twitter) stopped free access to the platform’s API. Meta is also restricting access to their services. The EU’s Digital Service Act provides regulatory solutions via provisions to allow researchers to monitor social network platforms. The lack of transparency on social media platforms will be particularly problematic in monitoring online aspects of the forthcoming elections in 2024.
The value of bitcoin more than doubled in 2023 (see graph below). Regulatory frameworks for cryptocurrencies will become more sophisticated, aiming to balance innovation with financial stability and consumer protection. In 2024, more countries will introduce Central Bank Digital Currencies (CBDC).
In 2024, elections in over 70 countries, including India, the United States, the United Kingdom, the European Union, and Mexico, will rely heavily on online content. As election campaigns will be carried via online platforms, it will increase risks for the spread of mis/disinformation via deepfake videos, texts, and sounds.
Simultaneously, AI offers some hope for the detection of fraudulent content. Nonetheless, in 2024, the volume of deepfakes and identity manipulation generated by AI is likely to outnumber its detection capabilities.
In 2024, according to the Economist, 4.2 billion people in 76 countries will hold some sort of national election. Digital platforms and tools will play an important role in campaigns and elections. Their relevance increases given the high importance of these elections for the future of democracy itself.
AI empowers the generation of content, including fake and manipulative content such as the following deepfake of Trump and Fauci posted by Ron DeSantis.
Link: https://www.youtube.com/watch?v=hLuUmNkS21A
Some platforms like TikTok, Discord, and Twitch are developing new tools to handle election disinformation, while others like X and Meta have rolled back their policies. However, experts have already expressed concerns about platforms not having sufficient resources for monitoring complexity of online content during elections.
In Michigan, USA, there is impending legislation to regulate AI in political advertising highlighting the need for transparency and accountability in how AI is leveraged during electoral processes.
The race to create and detect AI content will speed up in 2024. Those who use AI to generate text, video, and sound in the race have a significant advantage. The “AI detecting” camp attempts to catch up in the race using two major approaches: detection and watermarking.
First, AI detection applications and platforms are failing. It became clearer in July when OpenAI discontinued its platform for detecting AI-generated text. The probabilistic core of generative AI makes it difficult to determine whether AI has generated content. In addition, AI foundation models are rapidly improving their ability to mimic human creativity. Thus, early signs of ‘AI creativity’, such as imperfect drawings of human hands, are becoming less useful as AI advances. AI detection can easily lead to false positives (misclassifying human content as AI-generated) or false negatives (failing to identify machine-generated content).
A recent study of 14 AI-detection tools used in universities found that they were neither accurate nor reliable. There are increasing cases of students being wrongfully accused of AI plagiarism. Many universities have stopped using AI plagiarism platforms due to the ethical and legal risks.
The second main approach is to watermark AI-generated content. It has gained traction among regulators, businesses, and researchers. Watermarking is more promising than AI detection, but it is unlikely to be a foolproof solution.
For example, “Tree-Ring” watermarking is built into the process of generating AI images using diffusion models, which start with messy images that are gradually sharpened. The ‘Tree-Ring’ method embeds a watermark during the early noise phase. Watermarks can be detected during the noise phase of image generation by reverse engineering the final image.
The race continues between those attempting to conceal watermarks and those trying to detect them. In parallel, many policymakers advocate for a watermarking approach. In 2023, the US government and a few technology companies made ‘voluntary commitments’ to support watermarking research.
Given the limited reliability of detection and watermarking techniques, the most helpful approach is the age-old one of determining an image’s source via email, URL, and institutional/individual credibility. Because we cannot trust AI systems, we stay with the old approach of trusting (or not) people and institutions who send us content, whether AI or human-generated.
Major tech companies, such as Alphabet, X, Meta, and TikTok, will play an increasingly prominent role in content policy and moderation. They are becoming de facto content regulators, determining what content is allowed and what is removed from their platforms. In 2024, these companies are expected to continue adapting their content moderation policies to address the growing concerns around misinformation, fake news, and violent content.
The implementation of the EU’s Digital Service Act (DSA) will gain momentum in 2024. Analogous to the ‘Brussels effect’ from data governance, DSA is likely to be emulated in other jurisdictions worldwide.
The internet has become an unregulated space where violent ideologies flourish unchecked. The US and EU’s joint statement on cyber resilience reflects a concerted effort to address cyberterrorism and online radicalisation.
Because governments and tech companies lack adequate policies and technical tools for content governance, the arbitrary prohibition of certain content could cause significant social and political unrest. Tensions will spill over into the streets and squares from online spaces. Following the crisis of 2024, more stringent content governance policies will emerge.
The digital space is fragmenting with the development of smaller and segregated online communities of like-minded people. For example, when Trump was banned from Twitter he moved to Truth Social platform which gathers users with similar views.
This trend of fragmentation means a further disintegration of social spaces and ‘online squares’ with far-reaching consequences for social cohesion and political systems.
