Generative Artificial Intelligence

AI: Doomsday or Promising Future?

Generative AI1 advances have raised concerns about negative impacts. Prominent figures, including Elon Musk and Steve Wozniak, call for a pause in AI development. Over 1,100 signatories support an open letter for halting AI systems stronger than GPT-4. The letter emphasises planning and management. It argues that powerful AI should be developed only when its effects are positive and risk management.

Italy has recently banned ChatGPT based on privacy concerns. However, countries that block or severely restrict the usage of these new technologies will likely fall behind and become losers. It is not likely that any international treaties will be introduced within a reasonable time. The Genie is already out of the bottle!

Generative AI: Doomsday

Different impressive AI solutions have been developed over the last few years. Generative AI carries several risks, including misuse, misrepresentation, bias, lack of accountability, intellectual property infringement, and economic disruption. The rise of AI systems could expose 300 million full-time jobs to automation, with two-thirds of U.S. occupations being exposed to some degree of automation. These concerns and the doomsday AI predictions have led to calls for caution and restraint. Some people, including some of the developers of these language models, believe we are close to developing a general artificial intelligence. However, there are many reasons to question that. Language models excel at language, a crucial step forward. This skill is essential for translating between human and computer languages. It can bridge other algorithms and systems. However, instead of being impressed by ChatGPT, we might worry about our language’s predictability.

Do a few large companies control Generative AI?

Some voices have raised concerns that this technology is in the hands of a few large American companies running in large American data centres. At first, that may sound like a valid concern. However, it is essential to realise that OpenAI was a relatively small, albeit well-financed, startup. The cost of renting more than 10,000 GPUs to train the GPT-3,5 model over more than a month costs $4.6 million. That is a lot of money, but not beyond what quite a few companies worldwide could do.

Today, anyone can rent cloud-based resources. Training data is also freely available. The Common Crawl is a monthly snapshot of the internet and a free dataset that anyone can download. Of course, if you want to download a dataset of 400 TiB, you may need to buy a few hard disks first! While training a model is costly, it can be used on much simpler resources. Hence, the race to catch up with OpenAI and Google would be tighter than most people realise.

Open-Source Models

I would predict that the landscape will look very different within a year. There are already a few promising open-source models. Stanford University developed a much smaller language model called Alpaca. It is so tiny that they distribute it as open-source software you can train and run on your PC. According to their comparison, their model is almost as good as GPT-4. Other options include KoalaGPT4All, and Vicuna. However, none of these licenses allows the user to use them commercially. Today DataBricks released an open-source model, Dolly 2.0, which can be used for any purpose and which you can train on any data you want. It can be downloaded here. In a leaked document written by a Google Engineer, open-source AI models will ultimately win the race against the larger companies thanks to faster development cycles and much better performance2.

Generative AI: Promising future

Recognising that the economy will likely adapt and grow with technological advancements is essential. AI presents many opportunities for growth and development. Goldman Sachs Research states generative AI could significantly impact the global economy. It may drive a 7% increase in global GDP (almost $7 trillion) and boost productivity growth by 1.5 percentage points over a decade.

Advancements in natural language processing enable generative AI to create content indistinguishable from human-created output. This breaks down communication barriers between humans and machines. Goldman Sachs economists Joseph Briggs and Devesh Kodnani argue most jobs and industries are only partially exposed to automation. AI is more likely to complement, rather than substitute, human labour.

Historically, jobs displaced by automation have been offset by creating new jobs. The majority of long-run job growth is accounted for by introducing new vocations due to technological breakthroughs. According to research by economist David Autor, 60% of today’s workers work in jobs that did not exist in 1940. Technology-driven job development has accounted for more than 85% of employment growth during the previous 80 years.

AI will not replace you. A person using AI will3.

Far-reaching consequences

Artificial intelligence advancements are predicted to have far-reaching consequences for global corporate software, healthcare, and financial services. Generative AI has the potential to improve corporate workflows, automate ordinary operations, and create a new generation of commercial applications. It is already increasing the productivity of knowledge workers, hastening medicine research, and boosting software code production.

Compared to the $685 billion global software business, the total addressable market for generative AI software is expected to be $150 billion. Companies in various industries will gain as more generative AI technologies are created and incorporated into current software packages and technological platforms. Uses span from increasing office efficiency and sales to building design and manufacturing parts, improving patient diagnosis in healthcare settings, and detecting cyber fraud.


Rather than halting development entirely, a responsible approach to AI development should focus on addressing the risks and creating sensible policies. Organisations can ensure accountable design and training, establish guidelines and protocols, educate stakeholders, implement safeguards, and monitor and evaluate the performance of generative AI systems. The positive global outlook on AI can still drive policy-making and innovation by managing these risks.

As history has shown, creating new jobs and industries often offsets job displacement caused by automation. Instead of fearing AI-induced unemployment, we should focus on the potential opportunities for new job creation and economic growth that AI can offer. This will require a balanced and sensible approach to AI’s role in the future economy and job market.

At Gislen Software, we are building competence in AI, including Generative AI. Please get in touch with us for more information on how we can help you with AI solutions or any other kind of software development!

1 Generative AI is the common name for miscellaneous AI-technologies based on deep learning algorithms that can produce text, code and visual media by identifying patterns. They have been trained on very large training data (therefore, they are often called Large Language Models). GPT-3 is, for example, trained on about  45 TB of text data.

2 The comment on the leaked document was added on 6/5. 

3 The quote is used across the Internet. Who said it first is not clear.

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