Cadenza Chronicle

Since its commercial beginnings in the early 2000s, cloud computing has been responsible for the proliferation of mobile apps and a $200 billion industry we have come to know as Software-as-a-Service (SaaS).

Now, “the cloud” may be about to drive its biggest innovation yet…artificial intelligence. But are the two applications compatible or a dangerous combination?

A Virtual Power Couple

Besides raw computing power, there is another important service the cloud provides – data storage.

Artificial Intelligence simply would not be possible without vast amounts of training data, so in this respect, the cloud and AI combo go hand-in-glove and are a virtual power couple.

The cloud is helping AI proliferate at the enterprise level by enabling all kinds of organizations from Fortune 500 companies to small non-profits have direct access to it, while AI in turn has the clear capability of enhancing the cloud in numerous ways.

One example of this cloud and AI combo is Together AI.

Cloud-Based Generative AI

Let’s say you’re in a paperwork-heavy industry such as finance or law, where research and reading eats up a lot of your time.

Among many other capabilities, Together provides the underlying infrastructure that can speed up this process by enabling enterprise customers to quickly summarize entire documents, highlighting only the most important parts, all from the comfort of your cloud environment.

Better still, Together’s AI models are open source, meaning more customization and privacy than one-size-fits-all solutions.

It also means ownership of custom applications, which is an often overlooked, but all-important benefit.

Given such features, its no surprise Cadenza (hey that’s us) was an early backer of the company, which has since gone on to partner with the likes of Nvidia and also recently secured a new $106 million funding round led by Salesforce Ventures at a $1.25 billion valuation.

Relationship Growing Pains

The cloud and AI simply would not be possible without the AI infrastructure that is rapidly growing around us.

From data storage and processing to compute resources, and machine learning frameworks, AI infrastructure is the backbone of it all.

The big cloud providers such as Amazon, Microsoft, Google, and Oracle have already invested billions into their cloud infrastructures.

For example, Meta, who is playing catchup on the AI front, recently announced that their anticipated capital expenditure on AI infrastructure is expected to reach $35-$40 billion in fiscal year 2024.

Amazon has already spent nearly half of this sum or $14 billion on AI just in the first quarter of this year and Google has said that it plans to spend around $12 billion or more each quarter this year on AI, most of which will be allocated towards new data centers.

Such large-scale investments couldn’t have come at a better time.

Consider this, 54% of companies used generative AI in their business by November 2023, just one year after ChatGPT was released to the masses according to PwC.

Prior to this, the figure was only 35% in 2022 and closer to 30% the year before that.

This is a 20% uptick in data usage in a very short period of time and the use of AI is only continuing to grow, putting a strain on cloud resources that weren’t originally built for such demands.

The CEO of Accenture confirmed as much in a Financial Times interview, saying there are the two primary challenges holding back AI deployment at scale:

• Data capabilities
• Safety controls

It appears as though this will continue to be the case, at least over the next several years.

The reasons for this vary, but most would say its due to spending constraints at the organizational level, as a result of limited growth prospects in their respective business, and the always dreaded, higher input costs.

But even throwing heaps of cash at the problem of data and power storage, as some large firms are, won’t magically solve it overnight. It will take some time for our physical infrastructure to catch up with our accelerating technological capabilities.

There is also the ages-old truth that well-fed animals move slower and the most well-fed animals in the entire business kingdom are large enterprises.

They will always react to innovation and gradually adopt it over time, rather than lead it. It is no different this time around when it comes to plugging AI into the cloud.

Happily Ever After

The cloud and AI combo is symbiotic and there will come a time when developers, CTOs, and others will wonder how they ever lived before it.

Such a time may still be a way off, but there is a clear path to getting there.

It starts with platforms like Together AI, which offer easy-to-deploy, cost-efficient AI tools in the cloud, helping flatten the adoption curve for enterprises.

After this, the ball keeps rolling with AI cloud security, which is an emerging growth area that cybersecurity platforms like Jericho Security are stepping up to serve.

Last, but not least, the next generation of energy efficient data centers, featuring on-demand connectivity and optimized hardware to handle the demands of Large-Language Models (LLMs) that power AI, will allow cloud environments to scale.

The next five years will see major advancements made to AI applications, making them and the cloud and AI combo a permanent fixture in enterprise environments around the world.

If you found this informative, check out other articles like The Tokenization of Real World Assets, or The Case for Decentralized Social Media

If you would like more information on our thesis surrounding The Cloud And AI or other transformative technologies, please email info@cadenza.vc