Will AI "Eat Enterprise Software?"

If you are an investor in enterprise software, you are aware there is a fear that language models are going to disrupt the traditional enterprise software market and firms. And that fear seems to be playing out in equity prices.

At one level there is concern that the traditional pricing model (per-seat licenses) will be challenged.

At another level there is concern that increasingly-capable AI models will displace the need for many enterprise software functions. 

Investors are essentially moving from views that “software eats the world” (so invest) to “software is dead” (so stay away). Near-term turbulence is inevitable. 

But it also is possible to argue that long term, there will be more enterprise software, even as AI adoption accelerates. 

More to the point, though language models enable natural language interfaces, automate routine tasks and generating insights from vast datasets, they arguably cannot replace enterprise software. 

Enterprise systems are engineered for reliability, security, scalability, and regulatory compliance in high-stakes environments. Moreover, enterprises often deal with proprietary data silos, strict data privacy laws and mission-critical uptime that general-purpose models cannot easily replicate. 

Aspect	Role of Enterprise Software	Role of General-Purpose Models	Coexistence Example
User Interface and Interaction	Provides structured dashboards, forms, and workflows for consistent, role-based access.	Enables natural language querying and conversational interfaces for ad-hoc exploration.	Models integrated as chatbots within enterprise resource planning systems (querying inventory via plain English without navigating menus).
Data Management and Security	Handles secure storage, compliance (audit trails, encryption), and integration with legacy databases.	Analyzes unstructured data or generates summaries, but relies on external data feeds.	Enterprise tools feed sanitized data to s for insights, while maintaining control over sensitive information ( GDPR-compliant AI assistants in CRM).
Automation and Workflow	Executes rule-based, repeatable processes like approvals or batch processing with high reliability.	Automates creative or variable tasks, such as generating custom reports or code.	Models suggest workflow optimizations within HCM platforms, but the core execution remains in the enterprise system (auto-drafting performance reviews in Workday).
Analytics and Insights	Delivers predefined key performance indicators, business information tools, and real-time dashboards with deterministic accuracy.	Provides probabilistic predictions, trend spotting, or scenario modeling from natural language prompts.	Hybrid BI where enterprise software runs core analytics, and models enhance with exploratory queries ("What if" simulations in financial planning tools).
Customization and Scalability	Supports enterprise-grade customization via APIs, modules, and cloud scaling for thousands of users.	Offers flexible, on-demand generation but struggles with consistent scaling or versioning.	Models used to generate custom code snippets for enterprise integrations, deployed within the platform (auto-building plugins for Salesforce).
Compliance and Auditing	Ensures regulatory adherence with built-in logging, versioning, and certification	Lacks inherent auditability; outputs can be opaque or inconsistent.	Enterprise systems log  interactions as auditable events, using AI for efficiency while meeting standards (fraud detection in banking software).

It’s a bit analogous to the traditional choices between general-purpose and application-specific processing. Sometimes one makes more sense than the other, but both coexist. 

AI-enabled or AI-centric software is moving up the stack of what a product is. So consumer experiences of products include vastly more software content than in prior years.

Sometimes a general-purpose approach will suffice. But not always. ASICs still make sense as well. 

And AI will often allow software to become more capable, rather than replacing it, which is the common concern today. 

Domain experience, codified in enterprise software, arguably will be just as important tomorrow as it is today. 

But investors, at the moment, seem more focused on the near-term negative impact on enterprise software company fortunes. 

In some cases, that concern is exacerbated by huge new capital spending requirements for AI infrastructure. 

An adage suggests "markets can stay irrational longer than you can stay solvent." And that is the reality some investors might be facing in the near term.

The Best Argument for Sustainable Neocloud Role in the AI Ecosystem

Perhaps the “best” argument for a permanent role for neocloud service providers is the relevance of enterprise private cloud inference services, which arguably cost less than public cloud providers. 

While hyperscalers (AWS, Azure, Google Cloud) dominate general-purpose computing and model training, neoclouds (CoreWeave, Nebius, Hut 8 and others) have some advantages for inference operations, at least for the moment. 

How sustainable that advantage might be is the question. But the logic for long-term relevance generally rests on a few key arguments. 

Enterprises increasingly are wary of sending sensitive proprietary data (financial records, healthcare data, IP) to public multi-tenant clouds for inference.So neocloud providers can offer private services that , satisfy strict compliance (GDPR, HIPAA) and data sovereignty requirements that public clouds arguably struggle to guarantee.

The former bitcoin miners are essentially energy arbitrage firms. They have spent years securing the world's cheapest power contracts and building high-density cooling infrastructure. So they might have an advantage supporting services using high-performance graphics processing unitss (Nvidia H100s/Blackwell) at a lower marginal cost than hyperscalers.

Some believe neoclouds can undercut hyperscaler GPU hourly rates by 30 percent to 50 percent.

Neocloud providers also offer advantages in portability. Since neoclouds typically provide raw compute (Kubernetes/bare metal), enterprises can build portable inference engines. If a company wants to move their Llama 3 or Mistral inference workload from CoreWeave to Nebius to chase a lower price, they can do so easily.

Even if long-term evolution remains unclear, and even if questions about sustainability remain, virtually all forecasts project near-term revenue growth, though differing in the magnitude of those forecasts. 

Provider	2025 Est. Revenue / Run-Rate	2026 Forecast / Target	Key Enterprise Strategy
CoreWeave	~$8.0 Billion	$10B+ (High visibility from backlog)	The market leader. Deep partnership with Microsoft (serving as Azure's "overflow" valve) allows them to capture massive overflow inference demand.
Nebius Group	~$500–550 Million	$3.45B – $7.0B	Aggressive growth. Heavily focused on "AI Cloud 3.1"—a full-stack platform specifically targeting enterprise model deployment and inference.
Iris Energy	~$300 Million (AI specific)	~$3.4 Billion (Annualized AI Revenue target)	"Mega-deal" focus. Recently secured a $9.7B deal with Microsoft, validating the model of miners acting as sovereign infrastructure partners.
Hut 8	~$150–200 Million (HPC)	~$450 Million (Annualized NOI from new lease)	Infrastructure-first. Pivoted to a "landlord" model for AI, signing a $7B/15-year lease deal to host AI workloads, providing extreme stability.
Hive Digital	~$100 Million (Annualized Run-Rate)	~$140 Million (HPC Run-Rate Target)	The "Double Helix." Maintains a strong Bitcoin mining arm while upgrading legacy fleets to Tier 3 data centers for boutique enterprise inference.

CoreWeave arguably has the greatest degree of revenue visibility, though, based on its backlog, which might be as much as two orders of magnitude greater than other competitors. 

Provider	2024 Revenue	2025 Revenue (Forecast)	2026 Revenue (Forecast)	Notes [Sources]
CoreWeave	$1.92B	$5.0-5.1B zacks​	$10.9-14.9B spglobal​	$55B backlog; GPUaaS leader marketwise​
Lambda Labs	~$425M ARR (late)	$520M+ (FY Sep) finance.yahoo​	N/A	$500M ARR mid-2025; cloud GPU growth sacra+1
Crusoe Energy	$276M tsginvest​	$998M ainvest+1	~$2B tsginvest​	Stargate project; stranded gas power tsginvest​
Hut 8	N/A	~$650M (annualized) ainvest​	N/A	AI expansion; Q4 $162M quarterly ainvest​
HIVE Digital	N/A	N/A	BUZZ HPC $140M ARR hivedigitaltechnologies​	285% YoY Q2 growth hivedigitaltechnologies​
Vast Data	~$200M ARR (early)	N/A	$600M ARR calcalistech+1	AI storage; 5-7 yr contracts calcalistech​
Soluna Computing	N/A	N/A	N/A	Q3 2025 rev. up 37%; 100MW AI sites barchart+1

All that noted, perhaps the best rationale for a continued value chain role for the neoclouds is demand for private inference capabilities needed by enterprises.