From scattered tools to a shared capability
AI is entering everyday work through a growing collection of subscriptions, browser tabs and individual experiments. That is a useful beginning, but it is not an operating model. When software teams, creative teams and business functions work in separate AI environments, an organization can struggle to see what it spends, which information is shared and which outputs are genuinely useful. The next step is not simply another account. It is an intentional environment.
Blobal’s mission is to design private enterprise AI studios around that environment. An AI studio brings suitable models, software, permissions and infrastructure into a coherent system deployed within an organization. People access capabilities through workflows that fit their responsibilities. Leaders can establish boundaries. Technical teams can understand what runs, how it is maintained and when a different model or deployment choice makes more sense.
We are developing this proposition from the idea stage. Our ambition is not a claim of existing customer deployments or a promise that every organization needs its own GPU server. Some businesses will be better served by managed applications or cloud APIs. Our responsibility is to identify where a private or carefully governed hybrid AI studio has a credible operational case—and where it does not.
The evidence supports discipline, not hype
The Stanford HAI 2025 AI Index reports that 78% of surveyed respondents said their organizations used AI in 2024, compared with 55% in 2023. It also reports generative AI use in at least one business function at 71% in 2024. These are survey findings summarized in a dated report, not a census of every business and not evidence that all deployments generated a return.[1]
Research also shows why the workflow matters. The November 2023 version of the NBER working paper Generative AI at Work studied an assistant introduced to 5,179 customer-support agents. It reported a 14% average increase in issues resolved per hour, with much larger benefits for less experienced workers. That result belongs to one studied setting; it does not establish a 14% gain for every AI product, department or company.[2]
| Evidence | What it suggests | What it does not prove |
|---|---|---|
| 78% reported organizational AI use in 2024 [1] | AI deserves operating discipline, not isolated purchasing. | Adoption alone does not demonstrate profitability. |
| 14% average productivity improvement in a support-agent study [2] | Context, onboarding and task design can matter. | The result is not a forecast for Blobal customers. |
| Benchmark-equivalent inference prices fell over 280-fold [1] | Model and deployment choices need regular review. | Private infrastructure is not automatically cheaper. |
There is an important counterargument to simplistic infrastructure pitches: some AI prices have fallen dramatically. Stanford compares a GPT-3.5-equivalent MMLU performance threshold at $20 per million tokens in November 2022 with $0.07 in October 2024. This is a benchmark-based comparison across model offerings, not a universal price index. It directly challenges the claim that buying hardware is justified because token prices must always rise.[1]
Our conclusion is more durable. Organizations need the freedom to choose the right deployment for each workload. A recurring internal task may suit a local model. An occasional complex task may suit an approved external service. The value lies in governed choice, evaluated quality and reliable access—not in defending one infrastructure decision forever.
Start with your work, not a shopping list.
Bring one recurring workflow, your data boundaries and an outline of current AI spending. Let’s explore whether a private studio deserves a deeper assessment.
Discuss your AI studioPrivate AI economics without shortcuts
“No token bill” is not the same as “no operating cost.” A private AI environment still requires equipment, electricity, cooling, maintenance, licensing where applicable, security work and people who keep it useful. Its financial case depends on utilization and output quality. An inexpensive response that needs repeated correction can cost more than a higher-priced response that completes the task properly.
We therefore favor total cost of ownership alongside cost per accepted task. A comparison should include peak demand, waiting time, human review and the cost of failure. Hardware depreciation and cash expenditure should be distinguished. Capital invested upfront has a financing cost, and equipment may need replacement before the original business case expects it.
| Monthly comparison | Lower external spend | Higher external spend |
|---|---|---|
| Comparable external AI spend | $2,500 | $6,000 |
| $60,000 setup allocated over 36 months | $1,667 | $1,667 |
| Assumed monthly operation and support | $1,800 | $1,800 |
| Illustrative private monthly equivalent | $3,467 | $3,467 |
| Difference versus external spend | Private costs $967 more | Private costs $2,533 less |
These invented assumptions show why workload qualification matters. They assume equivalent accepted output and comparable scope. Values are rounded; taxes, financing, residual equipment value and any external API charges are excluded. The $1,800 allowance is hypothetical, not a measured operating estimate. Real proposals must replace every assumption with scoped costs and tested performance. Neither savings nor a payback period can be inferred for a particular customer from this table.
Control is a practice, not a location
Keeping a model on your premises can help define where information is processed. It does not automatically make the system secure, compliant or trustworthy. Access controls, retention rules, backups, audit trails, software updates and incident procedures remain essential. Organizational memory should be permission-aware: an assistant must not become a shortcut around the restrictions already protecting sensitive documents.
The NIST AI Risk Management Framework offers a voluntary foundation for addressing trustworthiness throughout the AI lifecycle; its 2024 generative AI profile adds guidance for generative systems. We treat these publications as references for sound questions and risk planning, not as a certification that Blobal or a future deployment already holds.[3] Finance and HR workflows in particular require clear human accountability for consequential decisions.
From assessment to everyday adoption
Our intended delivery sequence begins with discovery. We identify the tasks people repeat, the information they need and the outcomes worth improving. Next, we specify models, software connections, interfaces and permissions. Only after those requirements are understood do we estimate capacity: concurrent users, expected throughput, memory, storage and acceptable response times.
Hardware follows this plan. Specialist infrastructure partners help translate the requirements into equipment and installation conditions, including networking, power and cooling. After approval, procurement and deployment establish the environment. Models and integrations are configured, and the agreed workflows are tested before handover. This order protects the customer from buying a powerful machine without a practical path to value.
Training is part of the solution, not an optional last slide. People need to understand what the studio can do, what information it may access and when outputs need review. Where useful, Blobal intends to develop role-specific interfaces inside the software. The goal is to make approved capabilities easier to use than improvised workarounds, with ongoing support defined by the service agreement.
For software teams, a pilot might test internal documentation assistance or a bounded coding workflow. For media teams, it might test approved product-image variations and review time, with model and asset licensing checked first. For business operations, it might test document classification or retrieval. These are candidate use cases, not claims that one model can perform every task or replace expert judgment.
A mission measured in useful work
The right question is not how many models fit on a server. It is whether your teams can complete important work more reliably, with appropriate information boundaries and a defensible cost. We would measure accepted output, adoption, turnaround time and support effort together. A pilot that fails these tests should change the design—or stop the investment.
Blobal exists as a proposition for this more deliberate relationship with AI. We want organizations to retain agency as models and economics change: choosing what runs locally, deciding when external capabilities are justified and building knowledge into workflows without losing control of it. Enterprise AI, on your terms, should be an operating principle rather than a slogan.
Find out where your studio should begin.
Share your priority workflow, team size and data constraints. Start a requirements conversation before making a hardware commitment.
Start a requirements conversationInterested in the company behind the mission? Read our investor vision and validation priorities.
Research & reading
- Stanford HAI: AI Index 2025, State of AI in 10 Charts. Published April 2025; the cited survey metrics describe 2024. Benchmark-price observations cover November 2022–October 2024.
- Brynjolfsson, Li and Raymond: Generative AI at Work, NBER Working Paper 31161. November 2023 revision; figures here refer to that working-paper abstract, not to a newly measured Blobal result.
- NIST: AI Risk Management Framework. AI RMF 1.0 (2023) and Generative AI Profile (2024). Voluntary risk-management references.