The Next Frontier for 2-Sided Marketplaces: How Fintech Will Unlock Enormous Value

This technology has become increasingly prevalent, often stepping in to perform roles traditionally held by customer support agents. As a result, it has brought about substantial reductions in operational costs for businesses. In various sectors such as sales, marketing, and customer service, AI/ML models are being utilized not as replacements for human employees but as complementary tools.

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Web Conference: Machine Language, Artificial Intelligence and Usage Data

After its G20 presidency and the Chandrayaan-3 lunar mission, India is taking on a more ambitious global role, including in science and technology. New Delhi’s AI strategy will have ripple effects throughout the developing world and its relationship with developed economies will be critical to India’s prospects for achieving its potential in the twenty-first century. South Korea, Japan, and Taiwan are home to some of the world’s most important semiconductor design and manufacturing companies, as well as semiconductor manufacturing equipment makers. They are also located in critical geographies for global supply chains along the South China Sea and East China Sea.

We see immediate opportunities that companies within cloud computing, cybersecurity, digital media, and digital commerce could leverage to accelerate disruption of legacy businesses while boosting their own top and bottom lines. The rise of generative agency would affect model companies – both large and small – and incumbent internet companies in consequential ways. For model companies, scale would quickly become a distinct moat, and developers would likely seek to build sub-models, plugins and tooling for ecosystems with the most users. A positive loop of more users would lead to more sub-models and plugins, which would in turn lead to better functionality. Data would become an even greater differentiator, and agent models would seek to farm-out tasks across interoperable sub-models. At the same time, models with high quality, specialized, proprietary data may have more ability to generate usage and economic leverage vs. competing sub-models.

Policy implications

A short list of these includes handwriting recognition, speech recognition, and image and object recognition. Many of these tools have been used in smartphones and numerous business and consumer applications. Consider Google Translate, which employs deep learning and is used by more than one billion people; it can already handle more than 100 languages, a number that AI researchers aim to soon expand to more than 1,000. For example, AlphaFold, an AI system developed by Google’s AI lab, DeepMind, has been able to predict the protein structures of all 200 million proteins known to science.

While all of this is obstructed by the exigency that is the allocation or auctioning of satellite spectrum, 2024 is the year when this entire space is expected to open up. Space is already the next frontier, and next year, satellite-based Internet services are set to connect remote areas, while also improving connectivity in mainstream circles. AI, in no uncertain terms, was the biggest conversation driver across the industry through 2023 and it is expected to remain so next year as well. Going ahead in 2024, it is expected to become more ubiquitous, all-encompassing, affordable, proliferated, and perhaps even more controversial.

We also assume that the entrepreneur faces an extra fixed cost, F, to learn ML, and if it takes such a cost, then it will reach the same ML knowledge level as that of the incumbent. To capture this aspect in our model, we assume that the entrepreneurial firm can access a share of the incumbent’s operational data to improve its products and increase customers’ willingness to pay. Our analysis shows that policymakers should consider how these operational data policies affect not only the amount but also the quality of entrepreneurship. In particular, while policies that make operational data generally available stimulate the amount of entrepreneurship, this growth could come from entrepreneurs who take on too little risk. These findings suggest that entrepreneurship policies that reduce the cost of becoming an entrepreneur with access to ML technology, such as the support of open-source AI initiatives, might complement policies regarding access to incumbents’ operational data. First, policies will need to be developed to ensure that AI complements rather than replaces human labor.

The usual suspects from three segments including the relational world (for example, Oracle, SQL Server and various dialects of MySQL and PostgreSQL), nonrelational databases (for example, MongoDB, Elastic, Redis) and the hyperscalers. OpenAI, Anthropic, Cohere Inc. or otherwise, 2024 will likely be marked as the start of a Cambrian explosion of fit-for-purpose, and more compact, foundation models or FMs. Suffice it to say that there was a pretty high mortality rate, which is the Darwinian order of things. According to the Organization for Economic Cooperation and Development, over the past decade, AI has been the fastest-growing sector for venture funding during that period, as shown in the chart, which extends only through 2020. A related fun fact from the OECD is that actual AI venture funding grew by 28 times over that period.

From innovation to impact:

The question of who owns the data generated and processed by AI systems remains unresolved. Deepfakes and misinformation created with Generative AI models can influence the masses and manipulate public opinion. Moreover, Deepfakes can incite armed conflicts, presenting a distinctive menace to both foreign and domestic national security. People can perceive the output of Generative AI models as objective truth, overlooking the potential for inaccuracies, such as hallucinations.

• But the concept of a super engineer being able to produce 5-times or 10-times content likely doesn’t come into view maybe for another year or two, just depending on who you speak to but that gives you a sense of scale.
• The result is a short list of indications that have a better probability of success in clinical trials because they can be more accurately matched to appropriate patient groups.
• Sam Altman, the CEO of OpenAI, recently explained that while gen AI today is good at doing “parts” of jobs, it’s not very good at all at doing “whole” jobs.
• However, workers will need support in learning new skills, and some will change occupations.

Read more about The Economic Potential of Generative Next Frontier For Business Innovation here.