AI To Lead To 30% Growth Within The Economy:
Similar Trend To Emerging Markets:
Break-Throughs Within AI:
AI To Lead To 30% Growth Within The Economy:
Since the 1900s, GDP per capita has grown at 2% annually. There is no evidence that this will speed up meaningfully within the near future – if anything, data now suggests a possible slowdown. However, according to recent research, there is a possibility for 30% annual growth of GWP within the century.
Research in recent periods has suggested that, AI will provide and drive explosive growth within the near future. It is possible that significant advances in AI could allow capital to much more effectively substitute for labour. Capital is accumulable, so this may lead to increasing returns to accumulable inputs, and so to super exponential growth.
1) AI robots as a form of labour may allow for this exponential change within society. Within a future in which AI robots can perform any task that a human labour can do for a smaller cost, this will mean that AI robots can perfectly substitute for all human labour.
Total labour = human labour + AI labour.
We can invest output to build more AI robots, therefore increasing the labour supply: more output -> more labour. In other words, labour becomes accumulable again, leading to super exponential growth improvements.
2) AI as a form of capital is a theory too. Namely, the fact that there is currently diminishing returns towards holding more capital, holding the amount of labour fixed. Imagine creating more high-quality laptops and distributing them around the world. Economic output at first would increase as the laptops made people more productive at work. However, eventually additional laptops would make no difference as there’d be no one to use them.
Advances in AI could potentially change this. This is because, by automating cognitive tasks, they could allow capital to substitute more effectively for labour. As a result, there may no longer be diminishing returns to capital accumulation. AI systems could replace both the laptops and the human workers, allowing capital accumulation to drive faster growth.
It is theorised that explosive growth would require AI that substantially accelerates the automation of a wide range of tasks in the production of goods and services. The more rapid the automation, and the wider the range of tasks, the faster growth could become.
Furthermore, both the AI robots perspective and the AI as a form of capital perspective make a similar point: if advanced AI can substitute very effectively for human workers, it could precipitate explosive growth by increasing the returns to accumulable inputs. In many growth models with plausible parameter values this scenario leads to explosive growth.
- A survey of AI practitioners asked them about the probability of developing AI that would enable full automation. On average, they assigned ~30% or ~60% probability to this possibility by 2080.
- To add, Joe Carlsmith estimates the computational power needed to match the human brain. There is reason to state that we may develop human-level AI, as findings show that we’re ~70% likely to do so by 2080.
Whilst common market consensus is pointing towards innovative investing as being flawed and trivial, in reality there is huge potential within certain innovative companies. Importantly to note, the majority of public market companies in which are labelled as innovative are likely fuelled with hype & speculation, however logically there is a small percentage of companies in which truly are innovative.
Similar Trend To Emerging Markets:
Bret pointed towards the similarities between innovation now, and emerging market investments. People could see GDP was going to accrue to the emerging markets, and therefore individuals deployed equity towards these emerging markets in order to benefit from this gain. “We believe the same is true for innovation”, said Bret.
“We believe a strategic allocation to innovation probably will evolve into a sub-asset class, as did the “niche” strategy of the 1980s—emerging markets. In the late 1980s and early 1990s, investors had little, if any, exposure to what has evolved into 13% of the global equity market capitalization and 60% of global gross domestic product (GDP) on a purchasing power parity basis.”
“Initially, many investors resisted exposure to developing markets based on the volatility associated with geopolitical uncertainties, corporate governance, and liquidity. With time, however, they observed low correlations between and among the stock returns of the various developing nations, as well as growth rates that far surpassed those in the developed world. Investors concluded that broad-based exposure to developing markets offered enough diversification to minimize idiosyncratic risks and lower volatility, resulting in higher risk-adjusted return.”
From an allocation perspective, one is better to be in front of this wave, rather than behind it. This is because, ARK believes that innovation market capitalisation will exceed $200T by 2030. In addition, ARK Invest has identified 14 different interesting instances of transformative technologies in which are now converging together, and therefore are reaching a tipping point.
Whilst investing within innovation is risky, there is evident knowledge to assume that the future will be filled with innovative companies producing ground-breaking results.
Break-Throughs Within AI:
William Summerlin is an analyst at ARK who solely focuses and specialises on AI technologies. Will mentions how, he is most excited for the opportunity for foundation models. Will states that the larger AI models are, is often a good leading indicator as to the quality of performance. This is a trend that will continue, says Will. This has been seen across many other language models, and a range of other AI technologies.
What is an AI foundation model?
Foundation models work by training a single huge system on large amounts of general data, then adapting the system to a new problem. This interlinks with the future of AI in which refers to AI being flexible, reusable models in which can be applied to just about any domain or industry task. IBM mention how, “the next wave of AI looks to replace the task-specific models that have dominated the AI landscape to date”. The future is models that are trained on a broad set of unlabelled data that can be used for different tasks, with minimal fine-tuning. These are called foundation models.
Foundation models however are not trivial, and results within the real world have already been seen. For example, the first glimmers of foundation models have been shown within GPT-3, BERT or DALL-E 2. One can input a short prompt, and the system then generates an entire essay, or complex picture based on set parameters – even if it was not trained on how to execute that image or task explicitly.
Looking at the foundation model of GPT-3, which is a powerful language model, this has shown outstanding results in which give Tesla investors hope in regards to the future of FSD. The model is used to create articles, poetry, stories, news reports and dialogue using just a small amount of input text that can be used to produce large amounts of quality copy. GPT-3 has over 175B parameters and can generate any text including guitar tabs, or computer code.
“Others have found that GPT-3 can generate any kind of text, including guitar tabs or computer code. For example, by tweaking GPT-3 so that it produced HTML rather than natural language, web developer Sharif Shameem showed that he could make it create web-page layouts”.
Elon Musk recently mentioned within a tweet that, “2029 feels like a pivotal year. I’d be surprised if we don’t have AGI by then. Hopefully, people on Mars too.”
Limits Within The Economy In Terms Of Growth:
Interestingly, there is plausible reason to state that the economy has innate limits in terms of growth. There are many economic models in which predict explosive growth, however fail to capitalise on the bottlenecks in which may slow growth. For example, regulation of the use of AI systems overall delays the adoption of AI. Humans too, must adapt towards new technological and social innovations, and there is a possibility of fundamental limits in terms of how advanced technology can become.
Interestingly however, there are evident incentives to remove the bottleneck of growth. If there is solely one nation whom are removing the bottlenecks of growth, allowing for less regulation, this therefore will likely lead to other nations removing these bottlenecks too.
Healthy competition between nation states will be vital to unlock the true potential of economic growth.
