Latest Unicorn List: 15 Companies That Joined the Billion-Dollar Club

What makes a company a unicorn is simple: it’s a privately held startup valued at $1 billion or more. This latest unicorn list highlights 15 companies that have recently entered the billion-dollar club across sectors like AI infrastructure, healthcare, fintech, and data centers. You’ll get each company’s focus, valuation at entry, and why it matters—plus practical guardrails to help you assess product fit, risk, and momentum. If you’re an operator or investor, you’ll walk away knowing how these businesses create value and which signals to watch next.

At a glance—how this list was assembled (skimmable):

• Threshold: private startups at ≥$1B post-money valuation tied to a priced round.
• Emphasis: diverse sectors and geographies from credible trackers.
• What you get: company focus, valuation at entry, quick “why it matters,” and numeric guardrails.
• Use it to: pressure-test vendors, map category trends, and refine your own fundraising or market-entry narrative.

Quick reference table

1. Nscale

Nscale joined the billion-dollar club by packaging the most in-demand infrastructure of the moment—AI-ready data centers—into a customer set that includes some of the biggest names in compute and software. The company focuses on high-density facilities that can power modern AI workloads, emphasizing connectivity, energy efficiency, and speed-to-capacity. For buyers, the appeal is straightforward: predictable performance where GPUs meet well-architected power and cooling. For investors, the thesis centers on long-duration contracts and the stickiness that comes once you’re embedded in an AI training or inference pipeline. Nscale’s positioning is strengthened by named customers and a capital plan tuned to the megaproject cadence of AI buildouts. In short, it cleared the billion-dollar bar by aligning capacity with the most supply-constrained demand curve in tech.

Why it matters

• AI infrastructure demand is compounding; capacity, efficiency, and energy sourcing are now strategic moats.
• Enterprise-grade customers de-risk utilization and create multi-year revenue visibility.
• Co-design with chipmakers and hyperscalers compresses deployment timelines and improves unit economics.

Numbers & guardrails

• Entry valuation: ~$3.1B tied to a large growth round.
• Capital raised in the round: ~$1.1B; named partners include industrial and telecom leaders.
• Named customers include Nvidia, Microsoft, and OpenAI, signaling production-grade credibility.

Mini-checklist

• Power: verify megawatt roadmap and grid commitments.
• Latency: confirm backbone peering and interconnect options.
• Sustainability: scrutinize PUE targets and renewable sourcing.

Nscale’s rise shows how disciplined, customer-anchored capacity planning can convert infrastructure scarcity into durable enterprise value.

2. Filevine

Filevine crossed the threshold by unifying case management, communication, and billing for legal teams, then layering in AI to reduce manual work. If you’ve ever wrangled matters across inboxes, spreadsheets, and point solutions, the product promise is relief through a single system of record. Filevine’s growth narrative is rooted in specialization for plaintiff and corporate practices, deep workflow customization, and integrations that keep lawyers in flow. The milestone valuation reflects a mature go-to-market: thousands of firms already rely on it for intake, document automation, and tasking. With legal operations increasingly measured on efficiency and outcomes, Filevine’s platform logic—consolidate tools, automate the repetitive, surface the critical—translates directly to saved hours and reduced leakage.

Why it matters

• Legal tech is shifting from “nice to have” to operational backbone, especially where AI can draft, route, and reconcile.
• Consolidation pressure favors platforms that already own workflows and data models.
• Embedded analytics and billing precision tie software usage to revenue capture.

Numbers & guardrails

• Entry valuation: ~$3.0B via an extension of a late-stage round.
• Reported customer base: ~6,000 organizations across segments.
• Growth focus: expanding AI-assisted drafting and matter-level intelligence within the core suite.

Mini-checklist

• Data residency: ensure compliance with jurisdictional rules.
• Interoperability: test DMS, e-signature, and billing integrations.
• Change management: plan training for partners and support staff.

Filevine earned unicorn status by turning legal work into structured, automatable flows—without breaking the nuanced practices firms need to preserve.

3. Strive Health

Strive Health became a unicorn by tackling the costliest corner of chronic care: kidney disease. Its model partners with providers and payers to identify at-risk patients earlier, coordinate care paths, and manage progression with data-driven interventions. The economic logic is compelling—preventing emergency dialysis and avoidable hospitalizations changes both outcomes and costs. Operationally, Strive blends care teams with analytics that flag rising risk and recommend next actions. For payers, the promise is predictable total cost of care. For patients, it’s earlier detection and smoother navigation across specialists, labs, and meds.

Why it matters

• Value-based care continues to favor condition-specific platforms with measurable savings.
• Kidney disease is high-prevalence and high-cost; incremental improvements compound quickly.
• Data-backed care plans improve adherence and reduce friction for providers.

Numbers & guardrails

• Entry valuation: ~$1.8B.
• Fresh capital: ~$300M primary financing aligned with expansion and care delivery capacity.
• Model: long-horizon payer contracts that reward outcome improvements.

Mini-checklist

• Integration: EHR connectivity (lab values, meds, encounters).
• Quality metrics: define baseline and improvement targets up front.
• Patient support: logistics for transport, education, and adherence reminders.

Strive’s milestone shows that clinically focused, outcomes-tied platforms can unlock meaningful enterprise value while improving patient trajectories.

4. Ultragreen.ai

Ultragreen.ai entered the club by bringing AI-powered fluorescent imaging into the surgical workflow, giving clinicians crisper intraoperative views of tissue and perfusion. By improving visibility at the moment of decision, the platform aims to reduce complications and re-operations. Hospitals and surgical centers like tools that slot into existing systems; Ultragreen.ai’s proposition hinges on reliability, fast setup, and clear gains in accuracy. The market pull is straightforward: better data during surgery can pay for itself via shorter stays and fewer adverse events.

Why it matters

• Surgical imaging sits at the intersection of patient safety and hospital economics.
• AI assistance improves signal-to-noise when minutes (or seconds) matter.
• Device-plus-software models can drive recurring revenue through usage and upgrades.

Numbers & guardrails

• Entry valuation: ~$1.3B.
• Capital raised: ~$188M from growth and sovereign investors backing clinical expansion.
• Strategic angle: de-risk clinical adoption with training, simple instrumentation, and clear ROI.

Mini-checklist

• Validation: confirm clinical performance metrics and reader studies.
• Workflow: test setup time and compatibility in the OR.
• Service model: SLAs for device uptime and software updates.

Ultragreen.ai’s ascent underscores how precise, workflow-native imaging can compound value across clinical outcomes and operating margins.

5. Lila Sciences

Lila Sciences hit unicorn territory by building AI tools that scientists actually use—from hypothesis generation to experiment planning and model-driven screening. Instead of monolithic “AI for science,” Lila focuses on discrete, high-signal tasks across diagnostics, materials, compute, and energy research. The pitch is productivity: compress the cycle between idea, experiment, and iteration. With foundations in research-grade modeling and secure data handling, Lila’s moat is earned where it matters—bench-level reliability and reproducibility.

Why it matters

• Scientific R&D is a massive but fragmented market hungry for cycle-time reductions.
• AI copilots for researchers work when they’re grounded in domain constraints and lab data.
• Vendor credibility rises with peer-reviewed methods, transparent benchmarks, and traceable outputs.

Numbers & guardrails

• Entry valuation: ~$1.2B.
• Capital raised: ~$235M in a large early round backing category-defining ambitions across verticals.
• Focus: applied AI spanning diagnostics, material science, compute, and energy.

Mini-checklist

• Data custody: IP protection and on-prem options.
• Bench validation: side-by-side comparisons with incumbent workflows.
• Governance: auditability for generated protocols and results.

Lila Sciences shows how targeted, lab-grade AI can move the needle on discovery rather than merely describing it.

6. Enveda Biosciences

Enveda Biosciences reached unicorn status by mining nature’s molecular diversity with AI to discover new medicines. Traditional drug discovery often struggles to explore vast chemical spaces cost-effectively; Enveda’s platform accelerates hit identification by learning from complex natural compounds. The value proposition spans speed, novelty, and tractability—find promising leads faster, with mechanisms that can survive clinical translation. Partnerships and pipeline breadth give the model multiple shots on goal.

Why it matters

• AI-enabled discovery platforms can compress timelines and expand chemical diversity.
• Natural product space offers under-explored mechanisms with real clinical potential.
• Blended pipelines (internal plus partnered) spread risk and attract capital.

Numbers & guardrails

• Entry valuation: ~$1.0B.
• Capital raised in milestone round: ~$150M to scale programs and platform.
• Sector note: balances platform licensing with in-house assets to manage dilution and control.

Mini-checklist

• Pipeline: clarity on lead assets and decision gates.
• Data: provenance and scale of training datasets.
• Clinical path: regulatory plan and biomarker strategy.

Enveda demonstrates how focused AI plus domain science can justify premium valuations through credible therapeutic optionality.

7. Thyme Care

Thyme Care’s entry into the club reflects a strong case for cancer care navigation: coordinating treatments, benefits, and support in a maze of providers and payers. The platform works alongside oncologists and health plans to guide patients from diagnosis through survivorship with empathetic, data-driven workflows. In a disease area where fragmentation causes delay and duplication, navigation reduces avoidable costs and improves the experience for patients and families.

Why it matters

• Navigation meets a high-acuity, high-complexity need that legacy systems under-serve.
• Value materializes in fewer avoidable ER visits, better adherence, and timely referrals.
• Cross-payer, cross-provider models scale well when data sharing is solved.

Numbers & guardrails

• Entry valuation: ~$1.0B.
• Fresh capital: ~$97M, enabling expansion of clinical teams and analytics.
• Model: payer partnerships aligned to measurable patient outcomes.

Mini-checklist

• Interoperability: EHR, claims, and patient-reported outcomes.
• Equity: language access and social-determinant screening.
• Measurement: define time-to-treatment and remission-support metrics.

Thyme Care shows that thoughtfully designed navigation becomes both a clinical safety net and a defensible business.

8. Baseten

Baseten became a unicorn by solving a grindy, universal problem: productionizing AI. It offers infrastructure that lets teams deploy, scale, and observe inference with less toil—from model serving and autoscaling to monitoring and cost controls. The platform’s north star is reliability: keep latency tight, costs predictable, and rollouts safe. Inference-heavy products live or die by these basics; Baseten packages them for teams that can’t or won’t build bespoke stacks.

Why it matters

• The AI boom is bottlenecked by deployment, not just model quality.
• Tooling that standardizes rollout and observability cuts time-to-value.
• Procurement prefers platforms that play well with existing clouds and MLOps tools.

Numbers & guardrails

• Entry valuation: ~$2.2B.
• Capital raised: ~$150M to scale infra breadth and enterprise go-to-market.
• Positioning: purpose-built for inference reliability at application scale.

Mini-checklist

• SLOs: define latency/error targets before migration.
• Cost: evaluate autoscaling policies with real traffic.
• Rollback: require safe deploys and shadow testing.

Baseten’s rise captures a truth of the moment: value accrues where AI becomes dependable software, not just an impressive demo.

9. Invisible Technologies

Invisible joined the club by turning messy, repetitive operational tasks into AI-assisted workflows at scale. Think data labeling, content moderation, catalog clean-up, prospect list enrichment—jobs that crush productivity when handled manually. Invisible’s pitch is orchestration: break work into steps, automate what you can, and supervise with humans-in-the-loop where precision still matters. With enterprise contracts, the platform leans into SLAs and measurable gains in throughput and accuracy.

Why it matters

• “Boring work” still runs the internet; automating it moves real KPIs.
• Human-in-the-loop designs beat pure automation in messy domains.
• As AI expands, well-governed ops become a durable moat.

Numbers & guardrails

• Entry valuation: ~$2.0B.
• Capital raised: ~$100M earmarked for platform automation and enterprise expansion.
• Category context: positioned as a Scale AI competitor in data operations.

Mini-checklist

• Quality: define acceptance criteria per task type.
• Security: data handling, redaction, and vendor access controls.
• Telemetry: measure rework and turnaround time.

Invisible’s milestone reflects how disciplined, measurable automation can transform the unit economics of “undifferentiated heavy lifting.”

10. Distyl AI

Distyl AI crossed into unicorn territory by offering a services-plus-software approach that helps large enterprises actually become AI-native. The company assembles cross-functional squads that implement use cases—agents, copilots, retrieval—while building internal muscles for ongoing delivery. Distyl’s edge is speed with governance: ship value in weeks while aligning to security, privacy, and compliance. It’s an appealing model for Fortune-scale organizations that need results, not just proofs of concept.

Why it matters

• Many enterprises have AI ambitions but limited internal bandwidth to execute.
• Services that install repeatable patterns and operating models unlock durable value.
• A credible bench of builders (often ex-platform veterans) builds stakeholder trust.

Numbers & guardrails

• Entry valuation: ~$1.8B.
• Capital raised: ~$175M to scale delivery capacity and tooling.
• Go-to-market: focus on high-impact vertical use cases and centralized governance.

Mini-checklist

• Use-case triage: rank by ROI, feasibility, and data readiness.
• Security: standardize secrets, key management, and audit.
• Change: train champions and set adoption targets.

Distyl’s step-function valuation rewards an execution model that pairs hands-on help with reusable building blocks.

11. You.com

You.com earned unicorn status by helping enterprises adopt AI through a search-native interface and platform components that slot into existing knowledge systems. The product vision blends generative answers with source transparency and enterprise controls. In a world where teams are drowning in documents and chat threads, the promise is simple: faster, trustworthy answers with guardrails. Adoption improves as You.com maps to compliance needs and delivers measurable productivity wins.

Why it matters

• Enterprise AI adoption hinges on trust, auditability, and clear provenance.
• Search-first UX matches how people actually start tasks.
• Integrations and permissions determine whether deployments scale.

Numbers & guardrails

• Entry valuation: ~$1.5B.
• Capital raised: ~$100M to broaden platform capabilities and enterprise reach.
• Buyer profile: departments needing fast knowledge retrieval with policy enforcement.

Mini-checklist

• Sources: ensure connectors cover your core systems.
• Controls: DLP, RBAC, and logging baked into rollout.
• Measurement: track question resolution time and deflection rates.

You.com’s valuation reflects a pragmatic path to AI at work: useful answers, visible sources, and security that enterprise buyers can sign off on.

12. Tide

Tide joined the club by building a financial platform for small and medium-sized businesses that unifies banking, invoicing, payments, and working capital. The win is removing friction from day-to-day finance—opening accounts, sending invoices, reconciling payments, managing cash flow. Tide’s expansion across regions gives it a diversified customer base, and its product footprint keeps users engaged well beyond a single transaction.

Why it matters

• SMEs want fewer tools and tighter integrations to manage money.
• Banking-as-a-service plus credit products create flywheels when underwriting is sound.
• Local market execution (KYC, onboarding, compliance) is the moat.

Numbers & guardrails

• Entry valuation: ~$1.5B.
• Growth round: ~$120M to fuel product and geographic expansion.
• Customer base: hundreds of thousands of small businesses across key regions.

Mini-checklist

• Reconciliation: test invoice-to-payment mapping.
• Fees: model total cost versus incumbent bank plus software.
• Credit: understand risk controls and data sources.

Tide’s elevation to unicorn signals that SME-first financial platforms can compound engagement into durable revenue.

13. Firmus Technologies

Firmus Technologies entered unicorn territory by operating AI-ready data centers in fast-growing APAC hubs. The value story rides on demand for compute close to customers, lower latency into emerging markets, and relationships with chip suppliers and cloud platforms. As AI workloads go global, regional capacity with the right power and cooling profile becomes a strategic asset. Firmus’s geographic footprint and investor mix point to long-run expansion plans across multiple sites.

Why it matters

• AI data gravity is shifting toward regional proximity and compliance requirements.
• Multi-site operators can arbitrage power, land, and permitting dynamics.
• Cross-border partnerships de-risk supply chains and deployment schedules.

Numbers & guardrails

• Entry valuation: ~$1.2B.
• Capital raised: ~$220M with participation from notable infrastructure investors.
• Operations span Singapore and Tasmania, serving AI-heavy customers.

Mini-checklist

• Permitting: validate local zoning and environmental reviews.
• Connectivity: confirm subsea and terrestrial routes.
• Resilience: dual-site DR and energy mix.

Firmus’s rise highlights how regional AI infrastructure can be both a compute business and a logistics business—and win at both.

14. Modular

Modular crossed the unicorn line by building an AI software compute layer that abstracts away chip specifics, letting developers write high-performance AI applications without hand-tuning for each hardware target. The thesis is portability plus speed: a consistent developer experience and runtime performance across heterogeneous silicon. For enterprises, this reduces lock-in and future-proofs investments as new accelerators arrive.

Why it matters

• Hardware proliferation makes portability and performance portability critical.
• Toolchains that simplify deployment expand the developer base for AI apps.
• A strong open ecosystem builds resilience and vendor leverage.

Numbers & guardrails

• Entry valuation: ~$1.6B.
• Capital raised: ~$250M for product expansion and community growth.
• Use case focus: inference acceleration, optimized runtimes, and developer ergonomics.

Mini-checklist

• Benchmarks: test with your models on your datasets.
• Integration: check build pipelines and observability.
• TCO: compare infra cost before/after optimization.

Modular’s valuation validates a simple idea: if you make AI fast and portable everywhere, everyone ships more—and better—software.

15. Rebellions

Rebellions joined the club by designing chips tuned for AI inference, a segment that demands low latency, predictable throughput, and efficient power usage. The company’s strategy blends novel architectures with partnerships across manufacturing and cloud channels. For buyers, Rebellions offers a path to better performance-per-watt and cost savings in workloads where GPUs are over-sized or over-booked. For investors, the bet is that inference volumes will dwarf training—and favor silicon optimized for deployment.

Why it matters

• Inference is where AI meets end users; efficiency wins at scale.
• Vertical partnerships (foundry, OEMs, cloud) compress time to market.
• A differentiated software stack is as important as the chip.

Numbers & guardrails

• Entry valuation: ~$1.4B.
• Capital raised: ~$250M with backing from major semiconductor players.
• Strategy: carve out niches where latency, cost, and availability beat general-purpose accelerators. Crunchbase News

Mini-checklist

• Compatibility: frameworks, compilers, and model support.
• Supply: delivery timelines and yield risk.
• Ecosystem: drivers, SDKs, and integrator partners.

Rebellions shows that purpose-built inference silicon can convert market scarcity into sustainable differentiation.

Conclusion

The newest additions to the billion-dollar club underscore three truths. First, AI is now a horizontal capability that seeps into everything—from data center buildouts and developer tooling to medical imaging and care navigation. Second, infrastructure and enablement layers accrue durable value: companies that make AI reliable, compliant, and cost-effective become the scaffolding everyone builds on. Third, measurable outcomes win—lower latency, better margins, improved clinical metrics, faster time-to-knowledge. If you’re evaluating vendors, anchor on clear KPIs and integration realities. If you’re building, chase wedge products that solve painful, repeatable problems where you can own the workflow and the data model. Use the numbers here as guardrails, not gospel, and keep pressure-testing: are you creating compounding advantages or just joining the noise? If this list helps you short-list a partner or refine your roadmap, pass it along to your team and start a pilot this week.

FAQs

1) What exactly qualifies a startup for the “unicorn” label?
A unicorn is a privately held, venture-backed startup valued at $1 billion or more in a priced funding round. Trackers generally require a verifiable post-money valuation linked to a disclosed round, not an internal 409A estimate. This ensures apples-to-apples comparability across companies and avoids inflated vanity numbers.

2) How reliable are the valuations on a latest unicorn list?
They’re snapshots from priced rounds—useful but imperfect. Valuations can drift with market conditions, investor markdowns, and secondary sales. Treat them as directional indicators, not guarantees of intrinsic value. Cross-reference multiple sources and, if you’re a buyer, weigh product performance above headline figures.

3) Why are so many new unicorns in AI infrastructure and data centers?
Because compute hunger is outpacing supply. Organizations need predictable performance, efficient power, and lower latency to deploy AI. That makes data centers, silicon, and inference tooling prime beneficiaries. The companies here reflect where bottlenecks are turning into businesses.

4) Do healthcare unicorns depend on reimbursement changes to succeed?
Often—but not exclusively. Care navigation and AI-assisted documentation, for example, create value by reducing avoidable utilization and documentation burden. The durable winners align with existing reimbursement pathways and show clear outcome improvements with real-world evidence.

5) Are all unicorns great buys or vendors?
No. A billion-dollar valuation says investors believe in the upside; it doesn’t guarantee product fit for you. Before buying, confirm performance on your data, test service levels, and model TCO. Before investing, pressure-test unit economics, gross margin, and sales efficiency.

6) What’s the biggest risk in picking from a latest unicorn list?
Assuming the curve goes up and to the right. Markets change, and execution risk is real. Insist on proof points: customer logos, renewal rates, reference calls, and shipping velocity. Avoid lock-in without portability, especially in AI infrastructure.

7) How should enterprises pilot an AI platform from these companies?
Start with one high-impact use case, define measurable SLOs, and run a time-boxed pilot. Shadow traffic and staged rollouts reduce risk. Bring security and compliance in early so you don’t redo work. Keep a kill switch if metrics don’t move.

8) Why include companies outside software (e.g., data centers, chips)?
Because value chains are converging around AI workloads. The fastest growth is where compute meets application—and vendors that deliver reliable capacity or efficiency often capture the spend that makes everything else possible.

9) What signals suggest a unicorn is graduating to long-term category leadership?
Recurring revenue with high net retention, expanding multi-product attach, credible ecosystem partnerships, and evidence of operational excellence (on-time delivery, strong SLAs, robust governance). Founder-market fit and shipping pace matter more than press.

10) How do geographies influence unicorn formation now?
Policy, power availability, and access to talent influence where companies cluster. You’ll see hubs where capital, compute, and regulation align. Regional players with credible partnerships are scaling faster as workloads globalize.

11) Can valuations retreat below the unicorn threshold?
Yes. Down rounds and markdowns happen. That doesn’t erase product value, but it does change dilution math and option morale. Focus on cash runway, gross margins, and payback periods when assessing resilience.

12) What’s the smartest next step for a buyer using this list?
Pick two vendors to pilot in parallel for your top use case, define success upfront, and compare outcomes. If neither clears the bar, iterate your requirements and try again—data-driven selection beats brand momentum.

References

• Highest Count Of New Unicorns Join Crunchbase Board In Over 3 Years As Exits Also Gain Steam — Crunchbase News — https://news.crunchbase.com/venture/unicorn-board-count-soars-september-2025-nscale-filevine/
• The Crunchbase Unicorn Board (methodology and list) — Crunchbase — https://news.crunchbase.com/unicorn-company-list/
• The Complete List of Unicorn Companies — CB Insights — https://www.cbinsights.com/research-unicorn-companies
• Crunchbase’s Global Unicorn List Tops $6T In Value — Crunchbase News — https://news.crunchbase.com/venture/global-unicorn-board-august-2025-ai-robotics/
• Definition of Unicorn Company — Investopedia — https://www.investopedia.com/terms/u/unicorn.asp