Amazon Web Services’ (AWS) 12th annual re:Invent conference was, unsurprisingly, all about generative AI (GenAI). The five-day event showcased all the ways AWS enables this budding technology — which Amazon CEO Andy Jassy claims will add tens of billions of dollars to AWS’ top line — not just through the infrastructure layer AWS is known for, but also through the company’s platform tools and applications. 

AWS Set Out to re:Invent infrastructure over a decade ago and is prepared to do the same with GenAI

Dating back to the dot-com bubble and the early days of amazon.com, Amazon gained an understanding of what it takes to provision infrastructure designed to scale at massive volumes. After Amazon spent years trying to overcome scale challenges associated with bringing third-party merchants to its e-commerce engine, AWS was born.
 
Despite all the competition it has welcomed over the past 10 years, AWS is still largely credited with not only pioneering cloud infrastructure but also making it accessible to anyone. As articulated by AWS CEO Adam Selipsky, this could range from a college student using a laptop in their dorm room to some of the most sophisticated enterprises in the world. But largely owed to the pandemic, we have seen the cloud market shift from a data center outsourcing strategy to a tangential business driver, which means AWS has had to adapt alongside its clients with not just traditional hosting services but also full-stack solutions tied to a specific use case.
 
One of the most compelling customer examples highlighting this approach includes Pfizer. At the height of the COVID-19 pandemic in 2021, Pfizer pledged to expand its cloud footprint from 10% to 80%. Put another way, Pfizer migrated 12,000 applications and 8,000 servers in 42 weeks, which resulted in $47 million in annual savings and the closure of three data centers. This seemingly successful, large-scale transformation has Pfizer now exploring AWS’ GenAI technologies, including Bedrock, to automate manual processes and realize a projected $750 million to $1 billion in annual cost savings.
 
This customer example speaks to the powerful influence AWS’ infrastructure has with clients such as Pfizer — which needed to submit data to the Food and Drug Administration in a matter of days during COVID-19 — that prioritize speed, scale and agility. Holding a significant portion of the cloud infrastructure layer, AWS is looking up the stack to tackle cloud’s next big reinvention: GenAI.
 

A closer look at AWS’ GenAI stack

Selipsky’s overview of the AWS GenAI stack was consistent with the commentary Jassy has provided on Amazon earnings calls over the past couple of quarters. Here is a quick look at AWS’ GenAI capabilities and some of the new innovations:

• Infrastructure: While a great talking point for AWS, we cannot argue the fact that scalable compute serves as the foundation for all things GenAI. For AWS, this includes both custom chips and NVIDIA (Nasdaq: NVDA) GPUs. AWS used re:Invent to launch innovations in both areas, including Amazon Trainium 2 instances (Trn2), which promises a fourfold performance increase over Trn1 for machine learning inference workloads, and NVIDIA DGX Cloud on AWS. The latter is particularly interesting and comes as all other cloud providers have already signed on as hosting partners for NVIDIA’s DGX AI software. As the first company to put GPUs in the cloud, AWS has a unique relationship with NVIDIA, but one that may be growing more contentious as sales teams push AWS’ own chips as part of a cost optimization play designed to maximize customers’ lifetime value. Even so, NVIDIA’s supplier power is significant, and thus the company has a lot of bargaining power with the hyperscalers, which need NVIDIA to supply GPUs to their data centers, and in return, can host DGX and support NVIDIA’s push into the software space.
• Platform tools and “as a Service” models: The middle layer of AWS’ GenAI stack is largely synonymous with Amazon Bedrock, a managed service used by 10,000-plus customers to access and customize foundation models for their GenAI apps. Making sure customers are not beholden to one model provider and can access an array of options through the same API interface is key to AWS’ strategy. It also contrasts with Microsoft’s (Nasdaq: MSFT) approach and helps AWS position itself as an open and flexible alternative. New models supported via Bedrock include Anthropic’s Claude 2.1, which has a context window of roughly 150,000 words — making it well suited for legal and finance use cases — in addition to internal models, like Amazon Titan Multimodal Embeddings. Breadth of models is key, but improving the native functionality within Bedrock garners the majority of investment from AWS at this layer. This largely includes features that get customers beyond out-of-the-box models to those that can be customized, fine-tuned and applied to business use cases. One example includes Knowledge Bases for Amazon Bedrock, a Retrieval Augmented Generation (RAG) service that pulls data from multiple sources (i.e., databases, APIs) to help customers bring data to their models and customize.
• GenAI applications: At the top of the stack are the actual GenAI applications built on foundation models. AWS may have a weaker association here, but this layer is important to rounding out the entire stack and keeping customers invested in AWS. This layer largely comprises Code Whisperer, the free-for-use code companion that also offers customization capability, which means the application learns from internal code to provide personalized recommendations.

It is all about breadth

With over 220 native services and 600 compute instance types, portfolio breadth has always been a hallmark attribute of AWS. For context, AWS launched 3,300 new features and services in 2022. In his opening keynote, Selipsky went as far to say that AWS offers 60% more services and 40% more features than its nearest competitor. The approach to GenAI will be no different, as AWS strives to offer the broadest set of capabilities for customers to run, build and deploy GenAI technology.
 
Even in areas where AWS lacks depth or specificity, ISV solutions prove instrumental in filling gaps and, in many cases, do more to drive up a customer’s underlying IaaS resources than AWS’ out-of-the-box services. We also know AWS has a rich history of delivering very basic services to market and quickly building them up into competitive products over time. Perhaps the best example is AWS SageMaker, which accumulated over 250 features and tens of thousands of customers in the span of six years.

GenAI applications: How AWS is entering the copilot race with Amazon Q

At re:Invent, AWS took a bigger leap into the GenAI applications space with the launch of Amazon Q. While incorporating natural language processing (NLP) into various services is not necessarily new to AWS, Q is a GenAI-powered assistant based on 17 years of AWS knowledge designed to bridge the gap between the technical and business-led functions in the enterprise. For example, Q will be integrated with the Code Whisperer environment so developers can ask questions like, “How do I create code for this function?” while admins can use Q in pretty much any environment (e.g., AWS Management Console, Slack, documentation) to ask questions as generic as, “How do I build a web app on AWS”?

But Q also connects to 40 external data sources for business-related tasks, such as data visualization and document summarization, while the assistant integrates with Amazon Connect for contact center optimization, and will soon work with AWS’ Supply Chain application launched at last year’s re:Invent. Integrating Q across functions like supply chain and customer service, in addition to the analytics stack with QuickSight, suggests AWS wants Q to be the expert assistant not just for building on AWS but also for business.

This approach is largely consistent with what we are seeing from competitors integrating copilots and assistants into their SaaS offerings; however, there are a couple of big contrasts between Q and Microsoft’s Copilot and Google Cloud’s (Nasdaq: GOOGL) Duet AI. This first one is pricing: Both Copilot and Duet AI are priced at $30 per month per user, while Amazon Q, though still in preview, will come in at $20 and $25 per month per user for Q Business and Q Builder editions, respectively.
 
AWS may be undercutting its competitors on price, but Microsoft’s and Google Cloud’s recognition and reach in the productivity space may prove challenging, at least in the context of including Q Business edition. Q Builder, however, may be another story. While including all the capabilities of Q Business, Q Builder is designed for AWS-specific use cases, and in general, anything AWS can do to make developers successful is going to be well received by the customer base. This could include tasks like troubleshooting applications, writing SQL queries or even migrating code. A small pool of Amazon developers tested this last capability internally to upgrade 1,000 applications from Java 8 to Java 17 in two days.

The other big difference is that Amazon Q leverages Bedrock, which means the GenAI assistant is pulling multiple third-party models and assigning them to the right tasks. Peers have taken a different approach, as their assistants are based on a sole provider; for Google Cloud, this is internal models like Codey, and for Microsoft, this is OpenAI’s ChatGPT. While we cannot say for certain how customers will view these approaches, for AWS, having Q based on Bedrock speaks to the company’s goal of offering a broad array of models in hopes of challenging Microsoft.

The zero-ETL integrations keep coming

Building on last year’s commitment to a zero-ETL (Extract, Transform, Load) future and the resulting integration between Redshift and Aurora, AWS launched three new zero-ETL relational and nonrelational database integrations with Redshift: Aurora PostgreSQL, RDS (Relational Database Service) and DynamoDB. Just like it wants to offer the broadest set of infrastructure options, AWS wants to ensure it has the breadth of cloud data services customers need so they do not have to compromise on the right tool for the right data task. But even if customers have an array of tools accessible to them, they still need a way to break down data silos, which requires integration.

To automatically connect data from source to destination and ease manual ETL processes, AWS is offering more integrations between its database and data warehouse services. We do suspect “zero-ETL” has become more of a marketing term and is essentially glorified data sharing, but there is undoubtedly value in simplifying how businesses connect and analyze data. Even before GenAI broke headlines, businesses were realizing the benefits of breaking down data silos and adopting an integrated data posture, but GenAI should only fast-track data strategies throughout the enterprises.

Microsoft recognized this trend years ago and recently launched Fabric, a platform that integrates multiple data services, including Synapse, which is akin to Amazon Redshift, into a single offering. Fabric is a single-source-of-truth platform that addresses the entire data cycle and charges customers based on total IaaS resources consumed, versus the compute and storage for each individual data service. AWS’ approach is different, and while customers have a suite of different data services available to them, it could take more effort for customers to stitch these services together and create a unified environment. The new zero-ETL integrations may help rectify this, but Microsoft’s single platform approach and simplified pricing model, all integrated with Copilot, will be competitive.

At AWS, “partners are the catalysts”

In the second-to-last keynote, VP Worldwide Channels and Alliances Ruba Borno discussed the critical role of partners acting as catalysts to GenAI adoption. This includes both ISVs and global systems integrators, and AWS wants to work with both parties collectively to meet a customer where they are in a journey and work backward from their needs. Delivering solutions as part of an ecosystem was a big focus of the revamped Partner Paths model two years ago, and now AWS is tasked with scaling this model to deliver the GenAI stack to customers.
 
When asked by Borno what partners can do to drive more business with AWS, Selipsky quickly called out proficiency and making sure the skills are in place to build trust with joint customers. Specializations and competencies are a big piece of proficiency and are skills customers appear to be asking for. At the event, AWS announced the general availability of specializations in resilience and cyber insurance and is also revamping its Competency, Service Delivery and Service Ready designations into one program. Another piece of advice for partners was to focus on putting the necessary resources in place to go to market with AWS, which could be anything from established business units to codeveloped centers of excellence.
 

It is always a balancing act between the vendor and partner as to who should invest what in terms of go-to-market resources to achieve collective goals. But the message during the talk between Selipsky and Borno seemed to be that AWS has all the funding, tooling and programs available to partners that can make for a successful go-to-market strategy, but the partner has to be willing to engage. Put another way, it may be difficult for some Tier 2 partners to grow their AWS business and get in on the GenAI opportunity given the massive resource scale of some of the Tier 1 competitors.
 
As an example, Accenture pledged to train 50,000 developers and technical specialists on Amazon Q and Code Whisperer over the next two years. Despite GenAI’s potential to automate labor, the technology will only broaden the vast IT skills gap, so vendors that can acquire and train the right talent will continue to outperform when it comes to doing business with AWS.
 
Lastly, Selipsky reiterated the important role partners will play in the data ecosystem. Considering it is not the actual foundation models that will differentiate the customer, but rather their data, there is an opportunity for partners here, and anything they can do to help customers establish a data layer that will pave the way for AWS’ GenAI stack will be well received.

Conclusion

While later to the GenAI movement, AWS, with its early establishment in cloud infrastructure, has actually been involved with AI for quite some time. In many ways, the company used re:Invent to raise its voice over the din of AI chatter and showcase the long-standing innovations that it aims to use to build new capabilities and play catch-up with competitors, namely Microsoft. The best example is Amazon Q, a business-focused assistant that is somewhat comparable to Microsoft Copilot, while more Redshift integrations underscore AWS’ goal of better connecting customers to other AWS services, an approach Microsoft is similarly taking with Fabric. Meanwhile, custom compute offerings will continue to serve as a landing spot for net-new workloads, and in some cases, they could be providing cost and performance benefits that help AWS become viewed as not just a hosting provider but also a long-term digital transformation partner.

At the end of the day, customers’ considerations of these GenAI offerings will heavily depend on their existing infrastructure footprint, level of integration required and business use case. Working with partners to land new business and maintain its IaaS leadership lays a foundation for AWS to build the broadest set of integrations, features and services. In doing so, AWS ensures it can meet clients anywhere in their journey regardless of technical requirement or business need. If properly executed, this approach will help AWS further grow off a $88 billion run rate and maintain its lead over its very fast-following peers in IaaS and PaaS.

A platform company at its core, Microsoft is less concerned with migrating monolithic applications and instead is focused on building a complete data integration and management layer to capture value-add workloads that tie into said applications, all while maximizing clients’ underlying Azure infrastructure usage. To replicate this approach for the AI era, Microsoft has spent years integrating its various data services, from Synapse to Power BI, to automate customers’ entire data pipelines and prepare them for AI adoption. The result is Microsoft Fabric, a new end-to-end SaaS-like data platform that could help Microsoft reach new audiences and spur Azure growth in the continued race for cloud and AI dominance.  

Microsoft Is Investing in Data Cloud to Support its GenAI Strategy

What Is Microsoft Fabric?

Simply put, Microsoft Fabric is a unified data platform comprising seven core Azure data services: Data Factory, Synapse Data Engineering, Synapse Data Warehouse, Synapse Real Time Analytics, Power BI and Data Activator. While Microsoft Fabric makes it easier for customers to connect to different personas within an organization, from data engineers to business analysts, the hallmark of the new service is its simplified pricing model, which charges customers based on the total amount of IaaS resources consumed, rather than the compute and storage for each individual Azure data service.
 
When we interview enterprise buyers, we continue to find that consolidating point solutions in favor of complete, integrated platforms is a common trend, and Fabric is bound to resonate with customers trying to control runaway cloud costs in a still widely uncertain economy.
 
The other key defining attribute of Microsoft Fabric is the underlying architecture it is built on, OneLake. Microsoft Fabric is based on a repository that allows customers to query data on not just MySQL databases but also object storage, as is customary in the data lake architecture.
 
With OneLake, we see Microsoft moving squarely into the data lake space. Given the symbiotic relationship between data lakes, which are designed for unstructured data, and generative AI (GenAI), OneLake is Microsoft’s under-the-hood way of ensuring that customers can easily load data from multiple sources, put it through the Fabric platform for data management and visualization, and build GenAI applications.
 
Altogether, the unification of Microsoft OneLake and Fabric is the right step for Microsoft and exemplifies how far the company has been willing to go to execute its AI-based growth strategy.
 

Fabric Will Help Microsoft Change the PaaS Landscape but Not Without Infringing on Partners

As highlighted in TBR’s 3Q23 Cloud Data Services Market Landscape, Amazon Web Services (AWS) is the clear leader in the cloud data warehouse market, with Microsoft falling squarely in second place and not significantly ahead of Google Cloud and Snowflake. Azure Synapse has not gained the same level of interest and traction in the market as AWS and Google Cloud’s BigQuery. As a result, Microsoft partnered with Databricks in 2017, developing and delivering the first-party Azure Databricks service.
 
Partnering with Databricks to ensure customers have an effective data analytics platform natively available on Azure rather than Synapse was a strategic move. With Fabric, however, we now see Microsoft essentially re-delivering Synapse as part of a more complete product that gets to the heart of what customers want: an end-to-end set of capabilities that automate entire data pipelines from data collection and ingestion up to analytics and visualization.
 
This approach should bring Synapse into more client conversations while helping Microsoft expand its reach outside the analytics department. This, of course, raises the question: What becomes of Microsoft’s partnership with Databricks? As part of OneLake, the architecture underpinning Fabric, Microsoft is leveraging Delta Lake — Databricks’ protocol for storing data in an open table format — and this move could persuade Databricks customers to adopt Fabric.
 
Even still, Microsoft OneLake adopts the data lakehouse architecture pioneered by Databricks, and with Fabric’s feature-rich set of upper-stack capabilities, customers may be more inclined to go all in with Microsoft Fabric and its comprehensive pricing model, which would bring a new layer of competition to the Microsoft-Databricks relationship.
 
This trend is indicative of what we are seeing across the cloud landscape. The hyperscalers, even those perceived as more partner friendly, are expanding into new areas of the cloud stack, posing potential risks to their partners, especially as customers continue to indicate their interest in consolidating point solutions.
 
That said, coopetition is nothing new in the cloud landscape, and vendors are getting more adept at navigating competitive differences to deliver outcome-specific solutions to their joint customers.
 
Perhaps the best example is the relationship between AWS and Snowflake, which are both spending millions of dollars to get legacy data warehouse customers to Snowflake’s platform on AWS. While AWS would naturally prefer customers adopt its own data warehouse service — Redshift — over Snowflake, AWS has realized the trade-off of forfeiting some Redshift customers to Snowflake as long as those customers are running on AWS infrastructure.
 
Microsoft Fabric is much broader than the data warehouse, but if AWS and Snowflake are a barometer of a successful partnership, Microsoft and Databricks will similarly learn to overcome these obstacles.
 
With Fabric, we expect Microsoft will slowly chip away at AWS’ share and potentially Snowflake’s and Databricks’ in the coming years. However, it is important to note we do not see Fabric as any kind of direct threat to pure play data cloud platforms, particularly Snowflake, which has the established presence and reputation in the data warehouse space specifically, not to mention easy inroads into AWS’ customer base.
 
In our talks with enterprise buyers, we often find customers value Snowflake as it allows them to run separate workloads as part of a shared data layer that is not tied to any specific cloud infrastructure. Despite the multicloud capabilities in OneLake, nothing changes the fact that the core data warehousing capabilities within Synapse are still built specifically for Azure infrastructure for the seamless integration with other Azure services.
 
We have no doubt Fabric will be attractive to Microsoft-centric shops, but attracting customers invested with other cloud providers may be a more difficult feat, solidifying Snowflake’s and Databricks’ unique value propositions.

Data Lakes and GenAI Go Hand in Hand, and Microsoft Wants to be the First Hyperscaler Strongly Associated with the Architecture

One other interesting consideration with Fabric is Microsoft’s choice of open table format. Considering its partnership with Databricks, Microsoft has opted for Delta Lake, although it plans to add external support for two other popular frameworks: Apache Iceberg and Hudi.
 
In general, for customers that want to build a data lake, Delta Lake is the preferred format while Apache Iceberg is more aligned with data warehouses. Defaulting to Delta Lake reflects Microsoft’s intent to remain relevant with Databricks customers, while allowing customers to query data on object storage (Amazon S3 and eventually Google Cloud Storage) reflects Microsoft’s commitment to the data lake architecture.
 
Due to data lakes’ ability to combine both structured and unstructured data for prescriptive analytics use cases, they are becoming increasingly popular and, in some scenarios, offer customers a way to bypass data warehouse operations altogether. GenAI, which relies on unstructured data sources, such as documents or images, will fuel customers’ desire to consolidate data warehouses into data lakes, leading us to believe that Databricks is in a strong position despite Microsoft’s Fabric announcement.
 
This is also one of the reasons why Snowflake is trying to add more features that support unstructured and semistructured data in hopes of changing its perception in the market from a data warehouse company to a data lake company.
 
The hyperscalers, however, have been arguably behind in their data lake services and messaging, and with OneLake, Microsoft wants to make sure it is the hyperscaler most strongly associated with data lakes and by default, GenAI.

GenAI Enablement Sits at the Heart of Microsoft’s PaaS Strategy

Considering Microsoft has arguably made the biggest splash in generative AI, the company’s latest PaaS developments come as no surprise. As TBR discussed in our 2Q23 Cloud Ecosystems Market Landscape, a large language model (LLM) is only as good as the data that goes inside, which means the ability to establish a centralized, single source of truth is very important for an enterprise pursuing a serious generative AI strategy.
 
OneLake’s ability to provide an enterprisewide repository and a no-code API to manage data will help the company address this need, and the GenAI tools embedded within Fabric will help accelerate the transition to unified data pipelines.
 
Mostly in preview today, there are three Copilot solutions embedded within Fabric: Copilot for Data Science and Data Engineering, Copilot for Data Factory, and Copilot for Power BI. Broadly, the Copilot solutions in Microsoft Fabric enable code generation capable of automating routine tasks and expediting the transformation from raw data to structured, which is what LLMs hunger for.
 
The integrations built over the years between Microsoft’s platform assets and its application portfolios ensure there is plenty of raw data entering Fabric, which, as it becomes structured, presents an ideal environment for enterprises to pursue custom GenAI development. This is where the Azure OpenAI Service enters the conversation.
 
While the Copilot solutions offered by Microsoft provide quick-and-easy access to GenAI capabilities, true transformational value will be unlocked as enterprises build their own GenAI applications around their proprietary data and business processes, presenting a large opportunity for Microsoft.
 
The Azure OpenAI service has been enabling customers to train LLMs on their proprietary data since it became generally available in January, and, at Ignite 2023, Microsoft took another step forward with the public preview launch of Azure AI Studio. A new addition to the Azure OpenAI service, Azure AI Studio brings together developer tools like Azure AI SDK with the company’s growing catalog of foundation models to enable customers to build their own copilots and other generative AI applications.
 
As more enterprises pursue custom GenAI development, the unified approach to data management offered by Microsoft Fabric and OneLake will become more valuable, drawing interest from enterprises with large Microsoft footprints, yet coopetition at the data layer will remain the standard.
 
Ultimately, Microsoft’s priority is ensuring all data can be easily fed into its foundation model service, so integrations that connect the Azure OpenAI Service with third-party data leaders like Snowflake and Databricks will prove to be popular alternatives to Microsoft’s end-to-end approach.

Microsoft Is Not Just after the Data Layer: The Race for Hybrid Cloud Control Plane Continues as Azure Arc Reaches 21,000 Customers

Throughout this report, we have touched on Microsoft’s pursuit of the data layer, but it is important to note that Microsoft’s PaaS capabilities are much broader and extend closer to the box. Owing to Windows Server, Microsoft has captured a significant portion of the enterprise OS layer, allowing the company to effectively move into the multicloud control plane, which Microsoft calls Azure Arc.
 
Best thought of as an abstraction layer that stiches together infrastructure assets for capabilities like monitoring, provisioning and observability, all while securing the OS instance, Azure Arc has amassed 21,000 customers in the span of four years.
 
In recent quarters we have seen Microsoft become increasingly transparent in its customer reporting. For instance, in 2Q23 and 3Q23 Azure Arc customer count grew 150% and 140% year-to-year, respectively, putting the customer count at just 7,200 in 2Q22. This is much lower than the 21,000 customers announced in 3Q23 and indicates vast interest from Microsoft’s install base of customers trying to bridge the gap between the cloud and legacy data center.
 
Another factor driving the platform’s success is Microsoft’s early support for both virtual machines (VMs) and Kubernetes. This approach contrasts with Google Cloud, whose primary goal is getting customers to move away from VMs and use containers. In other words, Google Cloud wants customers to use GKE (Google Kubernetes Engine) on premises to containerize a VM and keep it there, but also wants customers to build net-new, cloud-native apps in containers.
 
Google Cloud did launch Anthos for VMs in 2021, which we viewed as a direct counterattack to Azure Arc, albeit not a very effective one, as Anthos’ customer count is comparatively low and could suggest the company has not been as adept at tapping into the VMware customer base and attracting enterprises that are not ready to migrate VMs.
 
We will continue to monitor Azure Arc’s growing customer count in the coming quarters, and it will be interesting to see if Microsoft begins to leverage Fabric to support other managed data services outside Azure SQL via Arc to turn the hybrid platform a more complete, centralized management layer.

New: Explore the future of the cloud market in 2025 and beyond in TBR’s 2025 Cloud Market Predictions special report. Learn how AI, GenAI and alliances will shape the industry and affect market share. Download your free copy today!

Top 3 Predictions for Cloud in 2024

1. Simply providing cloud services at scale is no longer enough for vendors to gain cloud market share
2. IaaS will become more tailored to workload and regulation
3. SaaS vendors promote multiproduct sales with generative AI

GenAI’s Rise Amid Cloud Challenges: Navigating 2024’s Landscape and Shaping the Future

For all the challenges that cloud vendors faced in 2023, there was a promising sprout of opportunity that developed quite rapidly with generative AI (GenAI) technologies. The pace with which GenAI gained not only awareness but also real investment and usage in the market was notable, and we expect end customers’ real investments in the solutions to continue to grow and develop in 2024.
 
However, GenAI solutions on their own will not overcome the headwinds that worked against the market throughout 2023. Many of the forces that caused revenue growth rates to slow precipitously for nearly every major cloud vendor remain in place heading into 2024.
 

 
The general macroeconomic conditions remain uncertain, wars continue to threaten global stability, IT buyers remain cautious about spending, and cloud has reached a saturation point in many IT organizations. So, while we do not expect GenAI technology to return the market and leading vendors to their pre-2023 pace of revenue expansion, it will serve as a small yet rapidly growing segment in 2024 and should become a significant market in 2025 and beyond.
 
We also expect the intensity of AI-focused strategies during 2024 to reflect the importance of the technology to long-term growth. AI could reset the cloud leaderboard for the next decade, so incumbents like Amazon Web Services (AWS) and Salesforce will be keen to protect their large customer bases against mounting AI competition from the likes of Google, Microsoft and SAP.
 
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Top 3 Predictions for Telecom in 2024

1. New round of M&A and bolder combinations are likely to be allowed by regulators
2. Cash flow management becomes priority due to increase in cost of capital and other headwinds
3. Open RAN will not be ready for mainstream adoption in 2024

CSPs and Telecom-centric Vendors Will Have to Adjust to Headwinds in Their Industry and the Wider Economy

Macroeconomic and telecom industry-specific challenges that manifested in 2023 — for example, rising interest rates, inflation, lack of 5G ROI, technological complexity, and the end of key stimulus programs and various other economic support mechanisms instituted by governments during the COVID-19 pandemic — are expected to persist through 2024, prompting a response from communication service providers (CSPs) and their vendor partners.
 

The most impactful and pervasive issue confronting the telecom industry is the rising cost of capital, which has been increasing due to central bankers’ shift from quantitative easing (QE) to quantitative tightening (QT) in an attempt to tamp down inflation. The result thus far is companies are now paying on average two to three (or more) times the interest rates they had grown accustomed to since the Great Recession, when central banks began holding interest rates at close to zero. This relatively abrupt change in monetary and fiscal policy has created a concerning situation for entities that are heavily levered with debt, which encompasses nearly all CSPs and many telecom vendors.
 

CSPs with the weakest financial positions began changing their behavior in 2023, primarily in response to the rising cost of capital, evidenced by fiber build targets being scaled back, assets being revalued and written down, and overall capex budgets being reduced. Some CSPs have also had to layer on more onerous covenants, such as pledging assets as collateral, to secure new debt issuances and partially offset the rise in interest rates.
 

TBR expects many CSPs with relatively stronger financial positions to also change their behavior in 2024. Changed behavior typically occurs after a reassessment of capital structure and capital allocation, which can lead to a variety of outcomes ranging from dividend cuts to capex reduction to M&A events. Said differently, CSP CFOs worldwide will be under an unusual amount of pressure to meet their objectives in 2024 and they are highly likely to place greater emphasis on cost optimization and cash flow management.
 

TBR maintains its belief that the telecom industry will look very different by the end of this decade as current events and entrenched challenges push the industry through an evolution.
 

To read the entire 2024 Telecom Predictions special report, request your free copy today!