The following article is taken from ARK Invest, and their research into AI Software: Productivity Gains Could Propel The AI Software Market To $14 Trillion By 2030 – ARK Invest (ark-invest.com)
Within bold, is the additional commentary from Christian Darnton.
WILL SUMMERLIN: As companies generate profound productivity gains from artificial intelligence, we estimate that software spend per year could grow at a 42% compound annual rate from just under $1 trillion today to $14 trillion in 2030.[1] According to our estimates for 2030, propelled by AI software, total IT spend could increase 20% at an annual rate and surpass $20 trillion, which is four times the ~$5 trillion consensus estimate, as shown below.
According to our research, during the next eight years AI software could boost the productivity of the average knowledge worker by nearly 140%, adding approximately $50,000 in value per worker, or $56 trillion globally, as shown below.[2] We expect the value of a knowledge worker empowered with AI to increase ~15% at an annual rate, compared to the 2.7% consensus expectation for annualized wage growth through 2030.
This is whole proposition of Palantir. Namely, the fact that there is a symbiosis between software and human. Thus, this results in workers increasing their productivity drastically, despite not being technical. Palantir empowers non-technical workers.
Alex Karp said this about Palantir:
” I believe that Silicon Valley is creating innovation without jobs, and it’s really hurting our world. And what’s very special about Foundry is that it creates innovation with jobs. So there’s factory workers – there are 1500 people at Chrysler using our product, 1500 people who are technical but are not PHDs in Computer Science or Math.”
“And that’s actually been the secret of PG, our government product. It wasn’t about making money. Sure, we’re a business, we like making money, it was about how can we make sure a special operator comes home or comes home with legs and that really is motivating.
And so, on the commercial side, making sure that workers can use the product. And once the worker starts using the product, they’re like really valuable. Because the worker on the frontline can start begin to do something that here to forth no worker can do, actually a computer alone can’t do it. Because it’s weight – it’s a factorial problem. Computers are not capable of actually doing this yet, maybe they will be in ten years, but by then the worker will be so good that maybe they don’t get – they’re irreplaceable.“
Anticipating more than a doubling in productivity, companies are likely to purchase AI software at a 75% discount to the boost in worker productivity. In other words, for every $1 gain in productivity, they probably will be willing to dedicate $0.25 to software licensing.
Important to note, we do not believe AI will lower the overall demand for human labor. Instead, productivity and GDP gains should enhance the health of labor markets over time.
Today, Open AI’s Codex coding tool uses neural networks to autocomplete tasks and can complete more than 30% of coding problems associated with simple tasks like creating website forms in minutes instead of hours. Given current rates of improvement, tools like Codex are likely to take over specific tasks in nearly every job category from accounting to engineering.
Another recent example is DALL·E 2, which produces strikingly creative images from text prompts. The Twitter account @Dalle2Pics documents many examples of DALL·E-generated images such as the one below, its response to the following prompt: “Early designs of the iPhone by Leonardo da Vinci.” We plan to publish additional research related to DALL·E 2 and its impact on productivity in the near future.
Palantir just announced that they were integrating the OpenAI solution into their product. This is part of a future initiative to shape the way low-code/no-code is experienced. As stated within our previous articles, “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.” 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.
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.
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”. This new partnership, whilst many overlooked, just shows the innovative inventions occurring at Palantir. Eventually through the use of these OpenAI language models, in Foundry, users with NO CODING EXPERIENCE can SPEAK to AI resulting in code being written. Or, users can WRITE IN NATURAL LANGUAGE, and this can be translated to CODE.
We have estimated the value of the productivity gains based on the “automatability”––or productivity multiplier––of various labor tasks relative to the wages associated with several job categories, discounted to account for lower international wages, detailed in the US Bureau of Labor Statistics, as shown below.
McKinsey’s mid-point scenario suggests that AI could automate 15% of labor tasks by 2030. According to our research, however, the displacement could be much higher for two reasons: technological advancement and the adoption curve.
The current slope of AI advancement is steep because innovations in hardware, training methods, and neural network architecture are compounding to accelerate progress beyond Moore’s Law. OpenAI, DeepMind, and other organizations have demonstrated that AI models should be able to achieve near human-level proficiency in many narrow knowledge worker tasks. Our research also suggests that AI training costs are dropping at a rate of ~60% per year, potentially breaking down barriers to many exciting large model projects.[3] In our view, higher allocations of human and financial capital to AI projects will continue to accelerate the rate of innovation, as shown below.
Agriculture has been an important case study in automation. The tractor, for example, transformed agriculture from a human- and horse-powered industry into a mechanized industry during the past hundred years. If agriculture is any indication, other industries in the process of automation are likely to enjoy increased productivity and margins, both of which should boost their returns on invested capital over time. The tractor is a good proxy for the level of automation in the agriculture industry.
As shown below, the adoption of the tractor followed the traditional S-curve. While displacing horses and mules, it hit an inflection point in the 20-25% range in 1940 and within 30 years leveled off at roughly 80% in 1969.
Thanks to the tractor, farm productivity increased dramatically. Between 1950 and 2015 total farm output nearly tripled while employment dropped by more than two thirds, as shown below.
Accelerating the adoption of AI software offers a clear return on investment, particularly because of the acute shortage of specialized knowledge workers. An AI tool that doubles the productivity of engineers is a compelling value proposition for software companies struggling with recruitment. Failure to adopt this technology is likely to put many companies in harm’s way.[4]
Technological advances and accelerated adoption call into question the consensus forecast for technology spending. According to Gartner, global IT spend as a percent of GDP was ~4-5% from 2012 to 2021. Consensus estimates for future IT spend relative to GDP are the same. In our view, the productivity potential associated with AI will upend that forecast.
If AI software companies were to generate revenue at a $14 trillion annual rate and a cash flow yield of roughly 30%, AI software could create more than $80 trillion in enterprise value, more than six times the $13 trillion in enterprise value created by the Internet since 1997, as shown below.
