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Microsoft’s move to outsource hollow‑core fiber (HCF) production to established glass and fiber manufacturers marks a key inflection point for a technology that has long promised dramatic gains in latency, bandwidth and energy efficiency for cloud and AI workloads. Over the last week Microsoft publicly announced strategic manufacturing collaborations with Corning and Heraeus to scale its HCF output, building on the company’s 2022 acquisition of Lumenisity and a string of laboratory breakthroughs that have pushed HCF attenuation below the historic limits of silica fiber. These partnerships are intended to accelerate the industrialization of Microsoft’s proprietary Double Nested Antiresonant Nodeless Fiber (DNANF) design and to seed a multinational supply chain capable of supplying Azure’s global network needs.

A high-tech lab with robotic arms, holographic world maps, and scientists.Background​

What is hollow‑core fiber (HCF) and why it matters​

Hollow‑core fiber routes light through an air‑filled central channel surrounded by a microstructured ring of thin glass tubes. By guiding light in air rather than in solid silica, HCF reduces the effective refractive index and therefore the propagation delay, while also dramatically reducing nonlinear effects and chromatic dispersion that limit conventional single‑mode fiber (SMF). In practical terms, the result is lower latency per kilometer, higher permissible launch power, and—when the losses come down—longer unamplified spans and a wider usable optical spectrum. Microsoft and its research partners have been explicit about the potential gains: the company’s published materials and technical briefings point to roughly 47% faster effective light propagation through HCF compared to standard silica glass and up to about a 30–33% reduction in propagation latency per kilometer, depending on the specific fiber geometry and operating wavelength window. Those headline numbers have been cited in Microsoft’s public blogs and partner statements.

Microsoft’s HCF strategy so far​

Microsoft acquired the UK‑based HCF specialist Lumenisity in December 2022, inheriting the company’s NANF‑derived products and a new 40,000 sq ft manufacturing plant in Romsey, UK. Since the acquisition Microsoft has invested in research, production upgrades and test deployments across Azure regions, turning a lab curiosity into a fieldable asset. The firm has publicly documented test deployments and pilot links and has described operational rollouts in multiple regions since 2023. Most recently, Microsoft confirmed it will partner with Corning to produce HCF at Corning’s U.S. facilities, and with Heraeus Covantics to produce HCF at European and U.S. sites. Those partnerships are explicitly aimed at scaling manufacturing capacity and building a resilient, global supply chain for Azure’s HCF requirements. Microsoft says Azure engineers will work alongside Corning and Heraeus to transfer manufacturing IP, implement training, and drive the yield and metrology improvements required for larger‑scale production.

The technical leap: DNANF and record‑low loss​

The breakthrough in context​

A string of 2024–2025 research milestones culminated in an industry‑level breakthrough: a DNANF design demonstrated attenuation below the long‑standing ~0.14 dB/km floor associated with the best silica fibers. Laboratory and test‑bed work by teams that include University of Southampton researchers and Microsoft/Azure fiber researchers reported attenuation as low as 0.091 dB/km at 1,550 nm, with sustained sub‑0.2 dB/km performance across exceptionally wide spectral bands and long test spools. That performance, if reproducible in long reels and commercial production, flips the conventional wisdom that hollow‑core fibers must accept worse loss in exchange for lower latency. Independent science and engineering outlets noted not only the low loss but also significant reductions in chromatic dispersion—claims that, if borne out across production lengths, could ease transceiver DSP complexity and power draw in high‑capacity systems. The DNANF family uses concentric nested thin glass membranes (the “double‑nested” elements) to produce anti‑resonant reflection that confines light in the air core while suppressing leakage and higher‑order modes. Precise wall thickness, capillary geometry and contamination control are all critical to achieving the lab numbers.

What the numbers mean in practice​

  • 0.091 dB/km at 1,550 nm: If reproducible at scale, this loss figure is lower than the historic minimum for silica SMF and implies longer unamplified spans, fewer repeaters or amplifiers, and reduced energy consumption for long‑haul links.
  • ~47% faster propagation: This is not a magic doubling of throughput, but a reduction in propagation delay per kilometer arising from the lower effective refractive index of air versus glass—the key advantage for latency‑sensitive workloads like financial trading and real‑time AI inference.
  • Broad spectral window: DNANF designs report sub‑0.2 dB/km across tens of THz, opening the possibility of using wavelengths beyond standard telecom windows for capacity expansion.
Caveat: the low‑loss figures reported in the scientific literature and company briefings primarily reflect controlled laboratory measurements and production pilot runs; moving from meter‑scale or spool‑scale performance to thousands of kilometers of installed cable requires reproducible control of manufacturing tolerances and contamination—an engineering challenge that the new Corning and Heraeus partnerships are intended to address.

Manufacturing and supply‑chain realities​

Why Microsoft is outsourcing production​

HCF’s manufacturing is materially different from conventional silica fiber draws. DNANF geometries need extremely tight control of capillary thicknesses and nested tube concentricity at sub‑micron tolerances. Preform and tube fabrication, precision drawing, gas handling to limit core contamination, and new metrology to verify uniformity over kilometers all demand specialized equipment and process know‑how. Microsoft’s choice to partner with established fiber industry leaders—Corning, a global leader in glass and optical communications, and Heraeus Covantics, a preform and high‑purity fused silica provider—reflects the recognition that scaling HCF will require industrial‑grade manufacturing capability beyond what a single startup or in‑house facility can economically deliver. Corning will leverage existing fiber and cable facilities (U.S. operations were highlighted), while Heraeus will supply preforms, fused silica raw materials and produce fiber at European and U.S. sites—building out a multinational manufacturing footprint intended to serve Azure’s global production needs. Microsoft frames these relationships as IP‑protected process transfers where Azure engineers will work on yield improvement and quality controls with partner operations.

The manufacturing hurdles​

The DNANF structure is precise and unforgiving:
  • Extremely tight wall‑thickness tolerances on nested capillaries.
  • Strict control on gas composition inside the core and the presence of micro‑contaminants.
  • Metrology that can verify sub‑micron geometry across kilometer‑length reels.
  • Cableization and cabling jacketing methods that preserve HCF geometry and low bending loss in the field.
These constraints imply that high yields and low unit costs will only come after iterative equipment upgrades, long runs of process stabilization, and investments in dedicated inspection tooling. The recent partnership announcements explicitly acknowledge this: Microsoft will train partner teams, transfer manufacturing IP (under contractual protections), and drive continuous yield‑improvement programs to reach production grade outputs.

Deployment, interoperability and the use cases that matter​

Early deployments and corporate targets​

Microsoft has said it has been deploying HCF in Azure regions since 2023 and public reporting indicates pilot and production links have been installed. Press coverage cites that pilot programs and installed runs exceed hundreds of kilometers; some outlets report Microsoft installations totaling around 1,200 km of live HCF with corporate presentations and executive comments referencing a target to deploy up to 15,000 km across the Azure network in the coming years. Those figures appear in multiple trade reports and company talks, but the 15,000 km number should be treated as a corporate deployment target rather than independently audited route‑by‑route measurements.

Interoperability with SMF​

Practical network operations will almost certainly use HCF alongside conventional SMF for the foreseeable future. Splicing and interconnection strategies already exist—Lumenisity and Microsoft engineering have demonstrated techniques to fuse HCF to SMF and to use hybrid links—but full replacement of SMF is neither necessary nor likely in the medium term. HCF is most valuable where propagation delay or very wide bandwidth per fiber is a gating constraint: cross‑DC links, latency‑sensitive metro routes, AI fabric interconnects, and select transoceanic or long‑haul spans where amplifier count is critical.

Primary use cases​

  • AI and real‑time inference fabrics: Lower propagation delay improves end‑to‑end inference latency for distributed AI models.
  • Financial trading and high‑frequency markets: Microseconds saved per kilometer translate directly into business value.
  • Hyperscaler backbone links and long‑haul routing: Lower loss and broader spectrum could reduce repeaters and increase capacity per fiber pair.
  • Specialized sensing, laser delivery and quantum links: The air core and broader spectrum unlock niche scientific and defense applications.

Competitive landscape and industry momentum​

Microsoft is not alone in betting on HCF. Other startups and incumbents are moving: cable and fiber manufacturers (e.g., Prysmian), new entrants like Relativity Networks, and materials specialists are forming partnerships to scale HCF production and cabling for hyperscalers and telcos. These parallel efforts illustrate a broader industry recognition that HCF could be a foundational technology for next‑generation networks if production and cost hurdles are solved. The new Corning and Heraeus collaborations put Microsoft in a better position to influence standards and interoperability because they embed HCF manufacturing capacity inside companies that already supply global carriers and data centers.

Risks, unknowns and open engineering questions​

No technology is without caveats, and HCF’s path from lab to ubiquitous network layer is littered with concrete engineering and commercial risks.
  • Scaling laboratory results to production reels: DNANF low‑loss metrics were demonstrated in controlled settings and pilot‑scale runs. Producing thousands of kilometers with the same attenuation across many spools demands extraordinary process control and may reveal new failure modes (microbending sensitivity, contamination during cableization, reel‑to‑reel uniformity). The industry has noted that manufacturing impurity control was a key enabler for the low‑loss window.
  • Cost per kilometer and economics: Until HCF reaches comparable per‑km costs to SMF (including installation, amplifier savings and lifecycle maintenance), carriers will use it selectively. Economies of scale and manufacturing yield improvements are required to make HCF broadly price‑competitive for commodity routes.
  • Field robustness and cabling practices: HCF’s nested thin‑wall structure is mechanically different from monolithic glass core fiber. Cable jacketing, buffer tubes, ducts and handling procedures must be optimized to prevent microbending and preserve mode confinement. Existing fiber installers and splice teams will need new training and tools.
  • Standards and interoperability: For widespread adoption, standards bodies and industry consortia will need to define mechanical, optical and connectorization norms for HCF to ensure multi‑vendor compatibility. Microsoft’s partnerships position it to shape de‑facto standards, but the broader industry will push for open specifications.
  • Verification of deployment claims: Public figures such as “15,000 km planned” are corporate targets; independent confirmation (route‑level, audit or regulatory filings) is often absent in early stages. Analysts recommend treating such numbers as indicative of intent rather than completed installations until carrier filings or public route maps back them.

What this means for data centers and enterprise networking​

For data center operators and enterprise network architects, HCF presents both an opportunity and a set of design choices:
  • Opportunities: Lower latency links between critical clusters, the potential to reduce active amplification in long‑haul links, and expanded usable optical spectrum for higher per‑fiber capacity. For operators building AI fabrics that are latency‑sensitive, strategic HCF insertion could materially improve service quality and lower power per bit.
  • Practical considerations: Mixed HCF/SMF topologies will require careful fiber plant audits, new splicing best practices, and updated maintenance playbooks. Enterprises should include HCF options in their medium‑ and long‑term network roadmaps but avoid wholesale replacement of proven SMF plants until cost and operational maturity are demonstrable.

Roadmap to mainstream adoption: realistic timelines and milestones​

  • Short term (12–24 months): Pilot scale‑ups, yield improvement cycles with Corning and Heraeus, limited regional deployments for latency‑critical routes. Expect vendor‑driven hybrid HCF/SMF offerings and specialized products for hyperscalers and financial services.
  • Medium term (2–5 years): Commercialization of improved DNANF draws at higher yields, gradual price reductions, emergence of HCF‑aware transceivers and connectors, and initial standards work. Wider commercial links in metro and inter‑DC backbones become practical as costs decline.
  • Long term (5+ years): If manufacturing scale and cost converge with SMF economics, HCF could become a mainstream option for latency‑sensitive and high‑capacity routes. Even then, SMF will remain dominant for the bulk of mass‑market fiber due to entrenched infrastructure and cost advantages unless new HCF manufacturing paradigms dramatically reduce unit costs.
These timelines are conditional: breakthroughs in manufacturing automation or alternative low‑cost HCF designs could accelerate adoption, while persistent yield or reliability problems could slow it.

Final analysis: transformative, but not an immediate replacement for SMF​

Microsoft’s partnerships with Corning and Heraeus are a clear, pragmatic step toward industrializing HCF. By pairing the DNANF research and the Romsey pilot plant with global glass and fiber manufacturing leaders, Microsoft is addressing the hardest part of any materials and photonics breakthrough: volume production with reliable yields. If Corning and Heraeus can replicate laboratory DNANF performance in production reels at acceptable cost, HCF will shift from niche use to a mainstream tool for latency‑critical and high‑capacity network segments. At the same time, there are genuine technical and commercial obstacles. Achieving lab‑grade attenuation over many thousands of kilometers requires mastering contamination, geometry tolerances and cableization practices that are new to the industry. The economics must also improve before HCF will displace commodity SMF in general purpose networks. Microsoft’s approach—retaining R&D leadership while outsourcing production to proven manufacturers—mitigates manufacturing risk but does not eliminate it. The industry will watch yield curves, cost per km, field performance and industry standards activity closely over the next several quarters. For data center and network architects, the prudent posture is to plan for HCF as a strategic option for latency‑sensitive and high‑capacity links, while continuing to rely on SMF as the workhorse medium for general routing. The Microsoft‑Corning‑Heraeus axis accelerates the timeline by years compared with single‑vendor ramp approaches, but mainstream renewal of the global fiber plant will be incremental, measured and driven by cost, operational maturity and standards convergence.
Microsoft’s latest move signals that HCF has left purely academic territory and is now a materials‑ and supply‑chain problem being solved at scale—an encouraging development for cloud operators and anyone designing networks for an increasingly latency‑hungry AI era. The next meaningful milestones to watch: production yield improvements reported by Corning and Heraeus, independent validation of long‑length attenuation on production reels, and the emergence of industry standards for HCF interconnection and testing. If those checkboxes are met, HCF could rewrite the economics of latency‑sensitive connectivity; if not, the technology will find narrower—but still valuable—use cases where its unique properties matter most.
Source: Data Center Dynamics Microsoft ramps up hollow core fiber production with Corning, Heraeus partnerships
 

Microsoft’s Copilot isn’t just another add‑on—when adopted correctly it becomes the foundation for an organization’s move toward autonomous, agentic workflows, but the road from license purchase to measurable business value is shorter for some companies than others. The UC Today feature with New Era Technology’s Senior VP Steve Daly lays out a practical, field‑tested blueprint for bridging that gap: rigorous change management, continuous user enablement, and a repeatable adoption framework that treats Copilot like enterprise software rather than a novelty.

A team of professionals in a high-tech conference room review holographic dashboards at the Center of Excellence.Overview​

Copilot for Microsoft 365 charges a premium: Microsoft lists the official price at $30 per user per month, which equates to $360 per seat per year at list pricing. That positioning makes adoption economics and usage metrics front‑and‑center for procurement and IT leaders. Forrester’s commissioned Total Economic Impact (TEI) research for Microsoft 365 Copilot projects material upside—especially for SMBs—by modeling three‑year ROI scenarios that range from 132% to 353% in the SMB composite, and large‑enterprise TEI modeling that shows strong returns when adoption is disciplined. Those numbers are conditional on realistic implementation, governance, and measurement, not on license purchase alone. Despite the promise, the biggest barrier to realizing this upside is not the technology itself: it’s adoption. Organizations routinely underinvest in the human and process elements required to turn Copilot from a curiosity into a productivity multiplier. The UC Today reporting and New Era Technology’s “customer zero” experience make this plain: without structured training, ongoing engagement, and an adoption playbook, even expensive seat licenses can become shelfware.

Background: Why Copilot matters — and why adoption is different this time​

Copilot is built to live inside the apps your teams already use—Word, Excel, PowerPoint, Outlook, Teams—and to reason over your organization’s data inside Microsoft Graph and connected systems. That deep integration is the source of its potential: the assistant isn’t a bolt‑on chatbox, it’s a productivity layer embedded in everyday work. This makes Copilot both powerful and sensitive to the quality of your information architecture and permissions model. But Copilot’s value isn’t self‑evident like earlier enterprise software waves. Unlike CRM, which had a single, measurable use case (sales tracking), Copilot is a multipurpose assistant whose ROI looks different across departments and roles. That ambiguity creates a unique adoption challenge: you must define and measure specific, high‑value micro‑use cases that line up with business objectives before you can justify large scale rollout.
Microsoft recognizes the adoption challenge and publishes extensive adoption content—Copilot Success Kits, scenario libraries, and adoption templates—to help customers structure rollout, measure impact, and create Centers of Excellence (CoE). The vendor guidance reinforces the same lesson: adoption is a program, not a switch.

The Triple Threat: Cost, ROI, and Culture​

1) Cost is immediate and visible​

  • List price: $30 per user per month, paid annually in many commercial offers. That math is simple: 12 × $30 = $360 per seat per year at list price. For organizations buying thousands of seats, lackluster adoption can convert into six‑figure recurring waste very quickly.
  • Procurement dynamics: Because this is a recurring, per‑user charge, finance and procurement groups demand clear KPIs before large rollouts. Failure to define success criteria risks renewal‑time backlash.

2) ROI exists but is conditional​

  • Forrester’s SMB TEI modeled three‑year ROI ranges of 132%–353% depending on impact assumptions; Microsoft’s own summaries highlight these figures as achievable outcomes of structured adoption and measurement. These are not automatic returns—they depend on focused pilots, data readiness, and measurable outcome design.
  • Large organizations can show meaningful net benefits too; but the variance in outcomes underscores the need to treat Copilot projects as investments requiring baseline measurement and continuous improvement.

3) Culture and expectations​

  • Media hype creates both fear of missing out and unrealistic expectations. This dynamic often causes organizations to rush rollouts without the scaffolding needed to create persistent usage. New Era’s experience shows that without ongoing change programs, initial excitement fades and usage recedes.
  • End users assume conversational AI is intuitive. That perceived ease can be deceptive: good outcomes typically require role‑specific enablement, curated prompts, and templates that map Copilot features directly to routine tasks.

Case study: New Era Technology — customer zero turned playbook​

New Era Technology used itself as a test bed for Copilot and then turned that internal experience into a service offering—an approach often called customer zero. Their program illustrates a repeatable path from pilot to scale:
  • Build continuous communication channels: change managers engaged users with cadence and pressure to keep Copilot top‑of‑mind.
  • Create a learning ecosystem: bi‑weekly lunch‑and‑learn sessions, a living center of excellence knowledge site, and ongoing tips sustained usage beyond the initial launch.
  • Drive engagement through gamification: the Copilot Cup, treasure hunts, and a points system turned habits into friendly competition and maintained momentum.
  • Treat Copilot like software: New Era emphasizes governance, measurement, and iterative improvement—accepting that Copilot is a long‑lived product that requires lifecycle management, not a one‑time rollout.
These actions produced measurable internal adoption: New Era completed a 300‑user rollout and continued to invest in enablement rather than declaring victory at go‑live. These concrete tactics form the backbone of what New Era calls an “Intelligent Adoption Framework.”

The Intelligent Adoption Framework — a pragmatic four‑phase approach​

New Era’s playbook maps to a disciplined lifecycle that many organisations can replicate. Condensed into four stages, the approach looks like this:
  • Discover & Baseline
  • Map current processes, data sources, and permissions.
  • Select 2–3 high‑value micro‑use cases with measurable KPIs.
  • Pilot & Prove
  • Run time‑boxed pilots with cross‑functional cohorts.
  • Instrument outcomes (time saved, error reduction, throughput).
  • Scale & Embed
  • Operationalize learnings: templates, CoE artifacts, automated governance.
  • Deploy training at scale and allocate licenses to validated users.
  • Optimize & Govern
  • Maintain continuous learning programs and governance.
  • Use analytics to refine agent behavior, prompts, and permissions.
This lifecycle reframes Copilot adoption as operations—continuous with measurable gates—not as a one‑time project. New Era’s field experience confirms that treating Copilot as enterprise software dramatically increases the odds of sustained ROI.

A practical playbook for IT and business leaders​

Below are actionable steps aligned with the above framework and with Microsoft’s published adoption guidance.
  • Start small and measure:
  • Choose 2–3 micro‑use cases with tightly defined KPIs (e.g., meeting summaries per week, first‑draft time for proposals).
  • Use baseline measurements to quantify impact.
  • Invest in data readiness:
  • Ensure SharePoint/OneDrive/Teams content is discoverable and properly permissioned.
  • Purge duplicate content and apply retention/lifecycle policies to reduce noise.
  • Create a Center of Excellence (CoE):
  • Consolidate governance, playbooks, prompt templates, and security controls.
  • CoEs should be cross‑functional and continuously operated.
  • Make enablement continuous:
  • Deliver role‑based learning in the flow of work (micro‑learning, promptathons, hands‑on labs).
  • Gamify adoption to reward exploration and success.
  • Bake governance in from day one:
  • Enforce least‑privilege access (Azure AD/Entra controls).
  • Implement human‑in‑the‑loop checkpoints for high‑risk outputs.
  • Maintain audit logs and versioned prompts.
  • Instrument outcomes for finance:
  • Tie license renewals and allocation to demonstrated usage and outcome metrics.
  • Reclaim unused seats to avoid wasted spend.
Microsoft publishes extensive implementation guides and user‑engagement templates (Copilot Success Kits) that operational teams can adopt directly to accelerate these steps.

Technical and governance guardrails​

Copilot’s deep integration into tenant data brings technical benefits—and responsibilities.
  • Identity and access: Use Entra/Azure AD to enforce per‑agent scopes and least‑privilege service identities.
  • Data minimization: For training signals and model usage telemetry, favor synthetic or anonymized data where possible and apply Purview policies to protect sensitive content.
  • Human oversight: High‑risk outputs (legal text, clinical advice, financials) must have human sign‑off workflows and version control.
  • Agent governance: As Copilot Studio enables agentic workflows, require explicit approvals for agents that take actions (send emails, update systems) and log all actions for auditability.
These are not optional add‑ons; they are prerequisites for enterprise adoption at scale. Microsoft’s adoption materials and enterprise guidance cover these topics in depth.

Measuring success: KPIs that matter​

Moving beyond vanity metrics like seat counts requires a mapped KPI set that connects usage to business outcomes.
  • Leading indicators:
  • Active usage rate (daily/weekly active users among licensed population).
  • Successful session rate (SSR) — a metric Microsoft references as important in evaluating Copilot effectiveness.
  • Number of CoE artifacts (templates, prompts) created and used.
  • Outcome metrics:
  • Time saved per task (minutes/hours saved vs baseline).
  • Error or rework rate reduction (%).
  • Throughput improvements (e.g., faster proposal turnaround).
  • Employee satisfaction and confidence with outputs (qualitative).
  • Financial signals:
  • Reclaimed licenses and reduced FTE hours on routine tasks.
  • Measurable cost avoidance (outsourced work, consultant hours) attributed to Copilot.
Design experiments with a valid measurement window (recommended: 6–12 months) and instrument both quantitative and qualitative signals. Forrester’s TEI studies assume such disciplined measurement and should not be treated as plug‑and‑play ROI guarantees.

Risks and caveats — what to watch for​

  • Hallucinations and factual errors: Generative assistants sometimes produce plausible but incorrect outputs. Guard high‑risk workflows with human review and explicit checks.
  • Tenant data exposure: Misconfigurations in connectors or permissions can surface sensitive data. Apply strict lifecycle controls and tenant‑level restrictions.
  • Over‑automation: Agentic workflows are powerful but can cause cascade effects if an agent takes inappropriate actions. Require approvals, rollbacks, and observability for any agent that acts without explicit user confirmation.
  • License waste: Buying at scale before validating usage leads to shelfware. Tie procurement to measured outcomes and staged expansion.
  • Cultural friction: Automation changes job design and expectations. Be explicit about augmentation vs. displacement and design role transitions responsibly.
Whenever claims (especially vendor ROI numbers or case study savings) are cited, treat them as contingent on the assumptions and measurement design used by those studies and validate with your own pilots. Forrester’s TEI results, for example, are commissioned research with explicit modeling assumptions; organisations should replicate the measurement approach rather than assuming identical outcomes.

Where vendors and partners add value​

The UC Today piece highlights the role of integrators like New Era Technology in shortening the path to value: partners can act as implementation accelerators, bring playbooks, run customer‑zero programs, and staff Centers of Excellence until internal capabilities are matured. This is an effective model for organizations that lack internal AI change‑management capacity.
Microsoft also provides robust adoption assets—adoption kits, scenario libraries, and CoE guidance—that reduce execution risk. Combining vendor materials with partner operational muscle is a pragmatic way to scale while managing governance and outcomes.

Executive checklist before buying thousands of seats​

  • Have you identified 2–5 measurable, high‑value micro‑use cases?
  • Can you baseline current workflows and instrument outcomes for 6–12 months?
  • Is information architecture (SharePoint/OneDrive/Teams) mapped and permissioned?
  • Do you have a CoE or partner to own ongoing change management?
  • Have you planned for license reclamation and FinOps controls?
  • Are governance and agent safeguards defined before any agent is allowed to act autonomously?
Answering “no” to any of these means you should pilot further before a large commitment. Microsoft’s Success Kits and partner programs can fill many gaps, but governance and measurement remain the buyer’s responsibility.

Conclusion: Copilot adoption is the bridge to autonomous AI — but it’s built, not bought​

The pathway to an autonomous, agent‑driven future runs through disciplined adoption. Copilot’s technical integration across Microsoft 365 offers unique leverage: it can surface real productivity gains if an organization invests in the people, processes, and governance that make those gains credible and repeatable. New Era Technology’s internal program—customer zero, gamified enablement, a living CoE, and an Intelligent Adoption Framework—demonstrates how to turn a costly seat license into a strategic asset.
Microsoft’s price point ($30/user/month) and Forrester’s TEI projections make one thing clear: the financial upside is real, but only when adoption is intentional and measurable. Treat Copilot as enterprise software—scope, pilot, instrument, scale, and govern—and the seats you buy won’t just be licenses, they’ll be engines of sustained competitive advantage. The window to gain first‑mover benefits is finite; the organizations that combine speed with discipline will capture disproportionate advantage. The rest risk paying for potential that never materializes.

Source: UC Today Self-Sufficiency Unlocked: How Successful Copilot Adoption Is Key to an Autonomous AI Future
 

Ramp’s quiet acqui‑hire of the three‑person Jolt AI team into its engineering platform is a small transaction with outsized symbolism — it crystallizes a broader shift in the AI developer‑tools market from a vibrant ecosystem of independent specialist startups toward consolidation under a handful of platform owners that already own developer workflows and balance sheets.

Four people walk through a glowing doorway into a high-tech RAMP hub with holographic screens.Background / Overview​

In early October 2025 Ramp announced that it had brought the Jolt AI team into its engineering organization, describing the move as a talent and capability acquisition aimed at making Ramp engineers “as productive as possible.” Ramp’s CTO Karim Atiyeh framed the deal as a strategic fit for Ramp’s AI roadmap; Jolt’s founder Yev Spektor echoed that the company’s obsession with developer productivity matched Ramp’s engineering cadence. The transaction is structurally an acqui‑hire: Ramp took on people, not the Jolt product. Ramp itself is a deeply capitalized fintech — valued at roughly $22.5 billion after a late‑stage funding round — and has been investing aggressively in internal AI tooling and product features that fold AI into finance workflows. That combination of scale, product scope, and cash explains why a small specialist team would trade independence for the runway and distribution Ramp can deliver. This deal sits in the context of several recent exits, shutdowns, and talent scoops that together sketch a market with limited breathing room for tiny, independent AI‑coding startups. Examples that illustrate the pattern include the hire of Alex’s Xcode team into OpenAI, the shutdown of CodeParrot after product pivots failed to reach sustainable revenue, the wind‑down of Subtl.ai, and earlier examples like Kite’s 2022 closure. Each case points to similar pressures: intense competition from giant platforms, high inference and evaluation costs, and the difficulty of turning early technical wins into profitable, defensible products.

Why Ramp wanted Jolt: talent over product​

People who can productionize code AI are rare​

Ramp’s stated rationale — bring world‑class engineers who can make other engineers faster — is exactly the calculus behind many acqui‑hires. The logic is simple: building a research‑grade prototype is one thing; integrating AI into a production engineering lifecycle, instrumenting safety and eval pipelines, controlling costs, and aligning outputs with code style and tests at scale is another. Ramp’s blog and Crunchbase coverage emphasize that Ramp bought the team, not the Jolt product, and plans to embed that expertise into its developer platform and AI infrastructure. This is a recurring theme across the developer tools market: talent is more defensible than a single feature. Small teams produce creative demos and early traction, but the heavy lifting—operationalizing models, managing inference economics, building evaluation frameworks and authenticity checks, and embedding code‑aware guardrails—requires engineering depth and sustained investment. Ramp has both engineering scale and the fiscal runway to absorb these costs; most three‑to‑ten‑person startups do not.

Ramp’s scale and product fit​

Ramp’s valuation and revenue trajectory give context to what it can do with the Jolt hires. With new funding and reported annualized revenue in the hundreds of millions to low billions, Ramp can amortize model costs, build internal caching and batching, and iterate fast on developer‑facing agents in ways that small startups cannot. That mismatch in resources explains why the Jolt team chose integration over independence: the odds of surviving and scaling a standalone product in this environment are low unless a startup finds a very narrow, defensible niche.

The broader pattern: small AI code startups either get absorbed or fail​

High‑profile collapses and acqui‑hires​

The Jolt→Ramp acqui‑hire is one data point in a sustained pattern. Recent examples:
  • Alex (Xcode AI assistant): its three‑person team joined OpenAI’s Codex division; the move was framed as a talent integration rather than a product sale after Apple integrated ChatGPT support into Xcode, which removed Alex’s niche.
  • CodeParrot (Figma→React/Flutter code): the YC‑backed team shut down in mid‑2025 after burning through seed funding and failing to exceed roughly $1,500 MRR after pivots. Founder posts and entrepreneurial coverage make this bluntly transparent: even promising LLM‑driven products can struggle to convert early interest into revenue.
  • Subtl.ai: an AI “developer sidekick” that reportedly wound down in 2025 after funding and product fit issues. Its closure clustered in time with other shut‑downs, signaling a wave of attrition among early specialist tools.
  • Kite: an earlier poster child, Kite wound down operations in 2022 after failing to monetize a large user base. Founder Adam Smith noted the technology and market timing challenges, suggesting it may have been “10+ years too early” and that building a production‑grade, monetizable coding assistant can demand extraordinary capital.
Each of these outcomes reinforces a central lesson: technical novelty alone does not equal commercial viability. The costs and complexity of delivering safe, reliable code at scale — plus fierce competition from platform owners — make standalone survival rare.

Giants dominate developer workflows​

Companies that already control a developer’s environment or platform have massive advantages:
  • GitHub Copilot, backed by Microsoft and OpenAI, continues to expand user reach; public company comments and reporting place Copilot’s user counts in the tens of millions, underlining its distribution advantage inside IDEs and enterprise contracts.
  • Google reports that “more than a quarter of all new code at Google is now AI‑generated,” underscoring the internal leverage Google has when it folds AI into its development stack and product processes. That scale matters when shipping agentic workflows.
  • Anthropic, OpenAI, and other major labs are investing heavily in developer tools and agents; OpenAI’s acquisition of Statsig for roughly $1.1 billion is a recent example of platform players consolidating tooling, experimentation, and developer workflows under a single umbrella.
When the dominant platforms incubate or bundle code‑generation capabilities directly into IDEs, cloud consoles, or enterprise suites, standalone players are quickly boxed into commoditized niches or forced to sell talent.

Why building a profitable, production‑grade code AI is so brutal​

Economics: inference costs, eval pipelines, and SLAs​

Large language models and agentic systems are expensive to run in production, especially when teams must support low‑latency interactive experiences, long context windows for large codebases, and rigorous evaluation to prevent regressions. Founders and post‑mortems routinely cite rising compute costs and the expense of running extensive automated evaluation and testing infrastructure. Kite’s post‑mortem explicitly estimated that building a production‑quality code synthesis tool might require “over $100 million” in engineering and infrastructure investment. Beyond raw compute, the unseen cost is engineering labor for instrumentation: continuous evaluation frameworks, unit and integration testing for generated code, guardrails against security vulnerabilities, and human‑in‑the‑loop review processes. Those systems are costly and scale poorly for startups with limited runway.

Product fit: developer trust and workflow integration​

Developers are conservative by design: they inherit massive codebases, depend on predictable interfaces, and are accountable for production reliability. Winning developer trust requires more than accurate completions; it demands predictable edit patterns, compatibility with linters and CI systems, and ergonomics that integrate with existing workflows.
Startups often promise dramatic velocity gains but struggle with edge cases and the high cost of false positives (buggy code injected into a codebase). Even compelling demos must be proven across months of real‑world use to reach enterprise buying cycles.

Competitive moats: platform integration and distribution​

The biggest moat in developer tools is distribution. Tools that live inside a developer’s IDE, source control provider, or cloud console enjoy a natural retention advantage. Giants owning those platforms can roll out features to millions of users at minimal incremental distribution cost. This dynamic makes it hard for niche startups to scale their user base and revenue before capital runs out.

Consolidation in action: the Windsurf/Cognition/OpenAI story and its lessons​

The mid‑2025 discussions around Windsurf (formerly Codeium) illustrate how competition for talent and IP is reshaping the field. Reports suggested OpenAI considered a ~$3 billion acquisition; the deal later collapsed, Google DeepMind hired key Windsurf leaders in a reported $2.4 billion licensing‑and‑hire arrangement, and the remaining business and assets were absorbed by other players, including Cognition. This episode shows three dynamics at work: (1) the market value of specialized agentic coding talent is extraordinarily high; (2) large players prefer talent and selective IP licensing rather than always buying whole companies; and (3) the volatility of deal outcomes can rapidly redraw the competitive map. Those talent grabs cascade: when leaders and technical cores move into DeepMind or OpenAI, the remaining product, customers, and staff become acquisition targets or get folded into other startups. The result is fewer independent full‑stack product players in the coding‑AI space.

Is there any survival path for indies?​

Yes — but it’s narrow, and it demands discipline.

Viable strategies for independent teams​

  • Laser focus on a narrow, defensible niche where a small team can outperform a generalist model by deeply instrumenting domain knowledge (e.g., carefully curated enterprise‑domain libraries, compliance hooks, or specialized firmware workflows). Narrowness reduces the number of edge cases to handle and can make evaluation tractable.
  • Early ecosystem alignment: embed into a larger platform (cloud provider, IDE vendor) via partnerships, OEMs, or revenue share arrangements. That reduces distribution risk and can convert the startup into a “feature partner” rather than a direct competitor.
  • Turn product into a cloud service with predictable metering and enterprise SLAs, then negotiate enterprise agreements that cover inference economics and data residency. This requires business maturity many early teams lack but is necessary for durable revenue.
  • Monetize vertically: sell to teams and enterprises willing to pay for mission‑critical reliability (observability, reproducibility, security) rather than chasing broad free adoption.
Even these paths are challenging: platform owners can replicate integrations and undercut pricing; cloud providers can bundle features into broader agreements; and the compute economics remain material obstacles.

Examples of durable approaches​

  • Tabnine, TabNine’s trajectory shows one pragmatic path: integrate widely and merge into larger platforms or acquire by larger devtools companies, reshaping the company rather than scaling independently forever. The company effectively merged with or into broader developer ecosystems over time to remain viable. The lesson is that slow, platform‑oriented growth often beats risky, wide‑market bets.
  • Firms that become indispensable infrastructure (feature testing, observability, experimentation) can command higher valuations or strategic exits: OpenAI’s Statsig acquisition illustrates how product experimentation and developer tooling can be folded into a larger platform where they multiply value.

Risks and downsides of consolidation​

Reduced competition and innovation friction​

When the market concentrates around a few large players, incremental innovation may continue, but radical experimentation becomes rarer. Platform owners tend to prioritize features that serve the broadest customer base or the most lucrative enterprise segments. Niche ideas that require patience and small‑scale iteration may find it harder to secure funding or distribution.

Talent centralization and the “winner‑take‑talent” dynamic​

The biggest risk for the ecosystem is not only fewer products but also concentration of talent. When the best AI‑for‑code engineers move to DeepMind, OpenAI, Anthropic, or large cloud and fintech firms, the independent R&D capacity shrinks. That accelerates a feedback loop: platforms innovate faster because they have the talent, and they attract more talent because they move fast.

Vendor lock‑in and escalation in inference costs​

Enterprises that adopt a single giant’s suite of AI devtools may face lock‑in and rising inference costs. Even with initial integration benefits, organizations must build governance, cost controls, and backup plans; otherwise, they risk having a large percentage of their engineering output mediated by a single vendor and paid on opaque pricing terms. Recent company disclosures and analyst commentary underscore the cost and governance attention this requires.

What the Jolt→Ramp deal actually signals​

  • Tactical: Ramp wanted specific engineering talent to supercharge its internal developer platform and product‑facing AI capabilities. That is straightforward and verifiable in Ramp’s own blog and Crunchbase coverage.
  • Strategic: The deal is symptomatic of a market where independent code‑AI startups struggle to survive unless they either (a) find a very narrow, defensible niche; (b) embed early with a large platform; or (c) accept acqui‑hire exits. The pattern is visible across recent startups and talent movements.
  • Structural: Platform control of developer workflows (IDE, source control, cloud) is decisive. Whoever owns the UI and distribution for developers holds disproportionate leverage in who wins the market for code‑generation features. GitHub Copilot’s tens of millions of users and Google’s internal adoption numbers are the clearest proof points.

Critical assessment: strengths, risks, and what to watch next​

Strengths of consolidation (short term)​

  • Faster productization: large companies can convert prototypes into integrated enterprise features more rapidly because they own distribution and have deep engineering resources.
  • Better safety and governance: platforms can build centralized eval, testing, and governance frameworks that smaller startups often cannot afford.
  • Smoother economics for users: when AI features are integrated into broader subscriptions or enterprise agreements, procurement can often smooth per‑query cost volatility.

Risks and trade‑offs​

  • Less diversity of approaches: with talent concentrated, alternative architectures, creative UI experiments, and smaller proofs of concept may decline.
  • Market power and pricing pressure: dominant platforms can set pricing and terms that make it harder for smaller players to re‑enter space with differentiated, paid offerings.
  • Fragility of innovation pipelines: if platforms make conservative bets, new research ideas that require long incubation will struggle to find path to market.

Signals to monitor​

  • Talent flows: who gets hired by the big labs and clouds; pay attention to non‑product acqui‑hires that signal platforms are buying engineering velocity.
  • Adoption metrics: Copilot and Claude Code usage, enterprise seat counts, and internal AI‑generated code percentages at hyperscalers will indicate where real developer behavior is shifting.
  • Open source and standards: whether new interoperability standards for agents and code generation emerge to allow smaller players composable access to platform ecosystems. The current landscape shows some API and licensing pluralism but limited composability in practice.
  • Early winners among niche indies: watch startups that choose vertical focus and sign enterprise partnerships early; these may be the closest thing to sustainable indies in the current climate.

Conclusion​

Ramp’s incorporation of the Jolt AI team is simultaneously unremarkable and symbolic: unremarkable because it’s a three‑person talent acquisition in which a small team traded independence for scale; symbolic because it exemplifies a larger structural shift in the AI coding market. The combination of high model and evaluation costs, the distribution power of platform owners, and an arms race to acquire both talent and engineering velocity means the era of many tiny, independent AI‑coding startups growing into standalone champions is fading.
Independent teams that survive will be those that either find an extremely narrow, defensible niche, align early with platform partners, or accept the probability that their optimal outcome is a talent sale into a larger organization. For practitioners and IT leaders, the practical implication is to evaluate vendor lock‑in, demand clearer SLAs and cost transparency, and design governance for AI‑generated code. For founders, the lesson is stark: build with the structural realities in mind — or design your exit around joining the winners.
(The analysis above draws on reporting and statements from Ramp’s announcement and industry coverage, augmented by public company disclosures and founder posts; where specific quantitative claims are available (company valuations, user counts, PR acceptance rates), the article cites recent public reports and research papers to verify them. Any claim that lacks independent public confirmation is flagged in the text as such.

Source: AIM Media House Jolt’s Exit to Ramp Marks the End of the Indie AI Coding Era
 

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