娇色导航

Martin Bayer: Traditional database provider, platform provider, cloud provider, AI provider or a bit of everything? What does Oracle actually stand for today?

Thorsten Herrmann: The thrust is clearly to be a platform and cloud provider. Of course, we have to recognize that customers in different industries have different levels of maturity, especially when it comes to the cloud. However, Oracle also has the ambition to enter very deeply into the industrial value chain in certain sectors, such as healthcare, for example through acquisitions or the development of special know-how and special solutions. With our cloud, we logically also want to build the corresponding infrastructures for the topic of AI.

Oracle struggled with the cloud for a long time and got on board comparatively late.

Sure, you could say we’re a bit late to the party. But that also has advantages and not just disadvantages. In my opinion, the second cloud generation differs in key aspects from what was previously known on the market. All other providers have addressed the market with a classic public cloud approach. We call our approach a distributed cloud.

What does that mean?

We want to ensure that customers always get the same range of functions regardless of which form of cloud they consume, whether it is a private cloud behind the firewall in their own data center, the public cloud or the sovereign cloud. There are no differences in the tools used or the interfaces. Customers are therefore not pushed in a certain direction based on the type of cloud.

Depending on the criticality of the applications, customers can remain in their own data center and use the scalability and flexibility of the resource pool there. There is also the option of moving to the public cloud at certain times. And then there are perhaps parts that should run in a sovereign cloud or a highly restrictive cloud—in other words, an air-gap solution where the entire infrastructure is located exclusively in Germany, where the people involved are checked for sabotage protection and where there are no connections to the public internet.

Digital sovereignty is currently the subject of much discussion. How relevant is this topic for Oracle?

We have designed sovereign clouds and restrictive clouds from the very beginning. This is certainly due in part to Oracle’s history, having emerged from a CIA project. We have been familiar with the issue of creating highly secure applications or handling highly classified data from the very beginning.

Oracle launched a special construct here in Germany two years ago. The sovereign cloud is operated by a special German company of its own, which operates quasi separately from Oracle. Does that still exist?

Yes. We know different levels of sovereignty. What we understand by the Sovereign Cloud are data centers in Europe that are only operated by European citizens who have the appropriate clearance. All servers, including those used to manage the infrastructure, are located exclusively in Europe.

If the requirements are stricter, there is also the option of restricting the whole thing to Germany, for example if data is not allowed to leave Germany. Only German citizens who have been checked for sabotage protection operate these data centers here in Germany. A German company is responsible for this.

And then we also offer the possibilities of Air Gap Solutions. This gives customers their very own cloud, so to speak, which is only connected via certified data links, so-called diodes, that only work in one direction.

Ultimately, it’s a question of what level of sovereignty customers want. We can map everything both technologically and organizationally. That is a very significant difference. There are a lot of declarations of intent on the market, but we already have it.

Every cloud hyperscaler currently claims to be able to operate sovereign data centers and cloud infrastructures here in Germany.

The devil is in the details. I would always recommend this: Look at the fine print.

Many data protection experts say that as soon as a US provider is behind it or involved, it’s actually a no-go from a data protection point of view. That also applies to Oracle.

This is also an emotional discussion, which we are of course also confronted with—especially when I talk to political decision-makers. However, I believe that in many discussions we still do not differentiate clearly between what is actually feasible in technological and organizational terms and what constitutes the emotional component in the global political landscape?

Now, I can’t change the global political landscape—and that’s not my ambition. But I can, of course, try to create a factual basis. And the small print of many providers states, for example, that management servers may not be located in Germany. That’s a hard fact. But if I claim that my cloud runs in Germany, then everything really has to run in Germany. Not only must the data not leave the country, there must also be no access from outside.

There can only be access by persons certified in accordance with German law, and Oracle has checked this in a procedure agreed with the BSI. This is not easy, but it is available here today.

The blocking of the e-mail account of the chief prosecutor at the International Criminal Court in The Hague by Microsoft recently caused quite a stir. The background to this was a sanction imposed by US President Donald Trump. As a US company, Oracle could also be forced to press a kind of kill switch. How do you assess this risk?

I wouldn’t necessarily want to comment on the political context right now. We are able to operate cloud infrastructures in many countries without having any relationship with the parent company—even without an Internet connection. If you are then asked to let data flow out … data from a data center that is not connected to the Internet cannot flow out so easily. So against the background I just described, I would definitely see our distributed cloud approach as an advantage.

It’s also not the job of technology companies to play on the political landscape, but to create technological opportunities. At the end of the day, I see myself first and foremost as a European and as a German citizen. We are operating here within the German legal framework and we are sticking to it. Realistically speaking, everything else is to some extent beyond our control. If we get into a situation like this, then I believe our countries have very fundamental problems. This needs to be discussed on a different level.

At the moment, more and more companies in Germany are looking for alternative solutions—I would mention Schwarz Digits and STACKIT as clouds or the open source solutions from Nextcloud.

To put it provocatively, how much open source is developed outside of Germany and how much is really developed within Germany? That’s not meant to be polemical. But the discussions surrounding open source would also benefit from a little more objectivity. Incidentally, a large part of our cloud is based on open source.

If a data center operator had to remove all technology from its data center that contained American chips, switches, routers or Chinese components, there would be almost nothing left in it. So where does sovereignty begin and where does it end?

I think it’s too short-sighted to say: this is a German provider and therefore automatically sovereign, and this is an American provider and therefore automatically not sovereign. You have to look at the technological concepts, the operating concepts and the organizational measures. Something like this always goes hand-in-hand with the regulating authorities, with the customers and their requirements, and then logically with the providers who make the technological capabilities available.

Oracle is expanding its own cloud data center infrastructure and has also been increasingly looking to cooperate with other providers such as Microsoft, AWS and Google for a few years now. Is the focus shifting?

We are currently not managing to build fast enough to . This is a challenge that all providers are facing. We already have 51 public cloud regions in 26 countries worldwide and are planning to open more. This is progressing very quickly for us.

A second general challenge that we are all facing in the cloud sector is the issue of energy. How do I get the power into the data centers and how do I get the heat out? We all know the problems that exist at hubs like Amsterdam, where fast-charging stations for cars are now being shut down because you have to decide who gets the electricity.

At the same time, however, we will continue to push ahead with partnerships because we see a need for multicloud. In our opinion, the goal cannot be to build every cloud like a castle. This does not correspond to the IT reality of our customers. They used to operate a wide variety of servers, infrastructures and architectures in their data centers, different storage systems, network components, software stacks. Why should they have to commit to a single cloud today?

We need to get closer to what technology should actually do: Namely, to provide answers as to what the customer gets out of it and to work out the contribution to the customer’s benefit. I think we all need to focus much more on that than always just saying, “It’s great for us if everything runs on our own infrastructure.” But that doesn’t correspond to the reality out there. You also have to serve the heterogeneity of the landscapes out there.

What does that look like in concrete terms?

For example, the Oracle database … very stable and with interesting AI features to combine unstructured and structured data. However, certain parts of the customer’s IT infrastructure may run on a Google, AWS or Azure cloud. Why shouldn’t the customer actually have the option of consuming the database there too?

How exactly does this work now? Oracle databases in particular are sometimes very closely interlinked with the hardware. I’m thinking of your Exadata machines. Are they located at Google’s data center?

Exactly.

Closely coupled with the Google Cloud infrastructure.

Completely coupled. We know Exadata as an on-prem version of this database and there is also the cloud version, i.e. the managed version. The whole thing is a cloud service that we operate for the customer. Our aim is to keep all these migrations simple anyway. For example, we also have very low egress costs, which are normally incurred if you want to switch from one cloud to another.

Almost all providers have now abolished these egress fees.

Yes, they are being reduced.

Would it also be conceivable, for example, for AWS to set up its data center machines in the Oracle data centers and operate them there for its customers?

Not an uncharming thought, but that would be purely speculative. This would definitely not be decided in Germany, but would clearly be decided by our central engineering teams. Various aspects would have to be assessed, for example security, etc.

In general, there has been quite a cultural change at Oracle in recent years—a move towards openness. When I think back to how Larry Ellison used to berate AWS and Microsoft just a few years ago and today they’re cooperating.

And vice versa, of course. I can still remember from my Microsoft days when we tried to replace Oracle database environments with Postgres SQL. That wasn’t so easy. This shows that the level of customer satisfaction with our database is high, particularly in terms of stability and innovation, especially now in the 23ai environment.

When I made my trips to get to know the customers, there were certainly some discussions, but almost nobody confronted me with a discussion about instabilities, technological shortcomings or the like. That’s why this combination is so important: now I can also use the database stack that I have learned to value as a customer in the cloud and, above all, in the cloud of my choice. In other words, real added value. Everyone welcomes that equally.

However, I currently have the feeling that many people are trying to draw up boundaries again and give preference to their own stack along the lines of: “Dear customer, if you are already in my cloud and in my stack anyway, then why not use my tools, my features and my services instead of something else external?” But regulation demands more openness from cloud providers.

Exactly. Regulation is one aspect. But the other issue of modularity also plays an important role. We have always had this in the application environment. In the on-premises world, we have heard users complain about monolithic blocks versus modular systems and best-of-breed. The issue doesn’t disappear when I’m suddenly in the cloud. Of course, every provider wants you to use as much of your own stack as possible.

But having the option of creating these possibilities via standard interfaces, standard data exchange and certain standard formats and thus supporting change should be the case.

We keep hearing from many users that we are now in the cloud and are finding that the whole thing doesn’t work so well from an economic perspective if everything is only in one place. Here, too, we are to some extent the challenger in the market with a completely different price-performance ratio, which is also due to the architecture. With us, you can consume a fully-fledged cloud, i.e. its full functionality, in just four racks. With our competitors, you need many times that amount to get a fully-fledged cloud. And the smaller solutions are always a subset of the functions.

But you can’t get very far with four racks, can you?

Of course, four racks don’t have the same compute power and storage capacity as 100, but there’s no lack of functionality. That’s why we can also offer particularly attractive conditions and place this infrastructure and platforms in market segments where people were previously asking: “Well, this is actually too expensive, and can we even afford it?”

But that is basically the crucial question. Do I focus on a technical migration to the cloud and simply move my IT from my data center to the cloud, which ultimately doesn’t bring any particular added value? Basically, you’re giving away all the opportunities for modernization and transformation.

Hardly anyone does pure lift and shift these days. Many are moving into new application development with the cloud, or at least into a certain degree of cloudification, containerization, etc. Then there are perhaps a few topics that are no longer strategic in terms of the time horizon, but which will be needed for the next two or three years. Of course, the effort involved should be kept to a minimum, so encapsulate it or keep it on-premises. Or if you really want to empty the data center, then simply move it over to the cloud and continue to operate it there.

Good, but that still has a very technical focus. I would go one step further in the direction of process and organizational modernization. You mentioned it: Oracle wants to focus on certain industries. Are you also going into a real business consultancy that you offer yourself or is this done via partners?

Well, in those areas where we have sufficient know-how ourselves—and I would, first and foremost, mention the healthcare sector with Oracle Health, formerly Cerner—we have built up a lot of capacity in the retail environment or in the hotel industry. Otherwise, we pursue the approach of strategic partnerships, i.e. addressing these topics together with the relevant consulting firms.

I would not see it as an obvious goal to build up corresponding capacities ourselves. There are excellent industry-specific specialist consultants and the large consulting firms such as PwC, Deloitte, Accenture etc., with whom we naturally talk intensively about partnerships and then also address certain industries. That is why we have also organized ourselves internally by industry in order to improve this connectivity.

Oracle cooperates with various providers of large language models (LLMs). Their own bots and AI agent technology then build on this. How do AI agents from different platforms understand each other and how do they exchange information?

PwC is building interesting platforms for this. At the end of the day, you need the right platforms, because there are LLM developers and there are also many customers who want to train their LLMs enriched with their enterprise data, and this requires powerful infrastructures.

We operate the entire stack in the AI environment. We started integrating AI into our own applications very early on, both in the industry-specific applications and in the Fusion Stack. We have probably overtaken SAP worldwide in the ERP environment. This is also due to the fact that a lot of agentic AI is already integrated into our applications, whether it’s human capital management (HCM) systems or supply chain topics.

We also offer managed platforms with various LLMs, whether it’s Cohere or Meta or others. And we also offer entire infrastructures, from very small, very modular systems that can be easily integrated into your own operations, through to superclusters that can also be used to train LLMs.

You are sitting on a veritable treasure trove of data with your database technology, which is used by customers.

Well, our customers are sitting on a treasure trove of data.

Other providers get customers’ consent and use their data to train AI features. Their argument is that this also benefits you because it makes the AI more intelligent.

We explicitly don’t train models with customer data. We are very strict about this and say that the data belongs exclusively to the customers and we help the customers, sometimes together with partners, on special projects. But always in the context [that] the customer is in control, the customer decides.

That will not change. If only because, as a database provider, we receive a great deal of trust from our customers. This is where the most critical data, which often contains the central value chain, is stored. There are large car manufacturers where not a single car would roll off the production line if these databases were to come to a standstill. This is the heart of a company. And this data belongs to the customer.

So you provide the basic technology. But if the customer says, “I would like to enrich and train this or that AI model with my business data….”

They can do this via 23ai, for example. We would also make all the cloud features available, and we would logically also work together with the customer in the project groups. We would make our engineers available, and we would contribute the expertise of our partner companies. Everything that is necessary for the customer to achieve an optimal result. But we would never use this data or metadata to train our own agents.

Here in Germany in particular, we often talk about topics such as the data economy and data spaces, some of which are also being pushed by politicians—especially in view of the new possibilities offered by AI. Industries should set up data rooms and share data in order to generate new ideas. But it’s not really getting off the ground.

Take a look at predictive maintenance models. Does machine manufacturer A share its data with machine manufacturer B? Is it really in their interest? Do you really want a generally trained model to exploit the advantage that you may have found in the algorithm based on vibration analysis or number of revolutions, whatever, and thus have longer running times and less wear. Is that what you really want?

Basically, I think it’s always interesting to improve the quality of data, but you have to look at the specific cases and see how the interests are distributed. It would be quite conceivable to create a platform for a certain subset of toolmakers or machine builders who all want to complete a specific use case. It is then conceivable to run this simulation in a controlled environment with a controlled user group and their consent.

But not necessarily to create an agent that can then be resold to all sorts of other companies. Then we quickly get into the discussion: Who is actually driving how much innovation? What about IP theft? Another area that needs to be closely monitored. Not everything that’s feasible is always desirable or permissible.

This article originally appeared on . It was translated into English using DeepL and edited for clarity.

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