There is an architectural revolution of enterprise software. In December Two giant funding projects announced in late February illustrate the extent to which AI-native infrastructure investors are drawing venture capital: Temporal Technologies raised $300 million in a round that has taken total capital to 649.5 million, and Profound raised 96 million in a round led by Lightspeed Venture Partners.
These fundraisers are an indicator that VC companies are putting money on the fact that the future of enterprise software is not in either apps but in the layers of orchestration that would allow AI agents to be deployed.
Temporal Technologies has established the ultimate platform of durable execution-which means that business processes with complex processes and long lasting ones are executed correctly even in the case of system failure. With the workflow being hundreds of microservices and third-party APIs in an AI-driven world, the reliability of Temporal turns into a crucial infrastructure.
Their list of investors is a who’s who of enterprise software investing: Andreessen Horowitz, Sequoia Capital, Index Ventures, and Tiger Global Management all invested, meaning there is agreement on the importance of workflow infrastructure.
The scale of the capital needs of infrastructure platforms is estimated by the size of the round, amounting to 300 million dollars. Temporal has a support of more than 4.5 million developers and billions of workflow steps each day. This scale will require huge investment in reliability engineering, security certifications and global deployment infrastructure- costs that do not pay off until revenue is achieved, and they build competitive advantages that are insurmountable once they are achieved.
Artificial Intelligence Search Optimization Frontier.
Profound is another yet similar infrastructure play. With the emergence of AI search engines such as Perplexity and ChatGPT that are replacing the old Google searches, brands encounter existential visibility crises. The platform offered by Profound guarantees the continued discoverability of companies in this new paradigm, which is what they call AI search visibility.
The urgency of this problem to Fortune 500 firms can be traced to their $96 million Series C that have increased their overall financing to 154.5 million dollars.
The technical problem that Profound is solving is a fake problem. Compared to SEO, which maximizes page ranking algorithms, the AI-based search optimization entails that the information must be framed in a way that allows the large language models to correctly recall and refer to brand-related material. This is natural language processing, knowledge graph building, and real-time content adaptation, which requires a sizable investment in AI infrastructure.
Why These Two Startups Jut Raised 400 Million To Repair the Largest Enterprise Issue of AI.
The Revolution in Agentic Workflow.
Temporal and Profound also allow moving to agentic AI, i.e., systems capable of providing complex solutions, not only answering questions. This change demands infrastructure that can deal with uncertainty, failure, and protracted processes between distributed systems. The traditional enterprise software presupposed predictable and synchronized communication; AI-native infrastructure addresses asynchrony and resilience.
The financing of AI infrastructure is no longer tied to the overall venture capital trends. Non-AI enterprise software has a problem of compression of valuations whereas AI-native infrastructure attracts high multiples. The company Code Metal had only raised a sum of $125 million in AI-enabled code translation after three months of their Series A which is the speed at which capital is being deployed in this industry.
Render raised $100 million in their cloud platform serving developers of AI applications, which shows that infrastructure layers separate complexity on the wider ecosystem.
The bar has significantly increased to founders who want to raise capital to startups in this space. Investing in early startups in venture capital now demands evidencing technical differentiation, which can not be easily copied by open-source projects or extensions of the established platforms. The companies merely applying UI to GPT-4, the so-called wrapper, have been deprived of funding because the investors understand that the latter is not defendable. Playing such as True infrastructure such as Temporal and Profound are integrated into customer architectures to the extent that they create switching costs which can be valued at a premium.
Distribution of AI Infrastructure Geographically.
The AI infrastructure is concentrated in funding, which implies that assumptions related to the geography of venture capital are challenged. San Francisco is the leader, but there are some major rounds in Salt Lake City (Jump raised $80 million to finance advisor AI), Boston (Code Metal), and even Pittsburgh (Efficient Computer raised $60 million to make energy-efficient processors).
This dispersion indicates the AI infrastructure talent is more decentralized than the consumer application development, which offers a chance to the regional venture capital ecosystems.
The equity structure in such rounds also indicates strategic interests of the investors. Salesforce Ventures has been involved in numerous AI infrastructure acquisitions, such as Code Metal, which implies that the CRM giant is forming an ecosystem by investing in ventures, and not necessarily developing it internally.
This venture capital effort by corporations develops validation and the possibility of exit modes to portfolio organizations.
Investment Opinion: Evolve Venture Capital Financial Adviser Insight:
The capitalization phase that AI infrastructure is undergoing is biased towards the incumbents rather than the new entrants. We think that the time to start new AI infrastructure companies is rapidly running out at Evolve Venture Capital because Temporal, Profound, etc are attaining distribution benefits. Nonetheless, vertical-domain AI workflow systems are practical among founders who have strong knowledge in the industry. The trick lies in the need to find workflows where the general-purpose platforms are not working because of the regulatory, technical or integration barriers. To the investor, we suggest the differentiation between AI-native and AI-enabled companies (where the infrastructure is designed to support specifically AI workloads versus where software is already in place with AI functionality added). The latter is a type of infrastructure that is worth valuing; the former is a type of infrastructure that is under commoditization pressure. Due diligence ought to center on technical moats proprietary data pipelines, purpose-built model structures or special integrations partnerships that lock out incumbent platform expansion.
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