The American startup landscape, long reliant on a steady influx of highly skilled international tech talent, now finds itself confronting an abrupt and potentially devastating policy shift: the imposition of a $100,000 fee on new H-1B visa applications. With that steep barrier now in place, many fast-growing and capital-intensive companies those counting on global engineering, AI, fintech, and deep tech talent face a new class of existential risk. This piece examines the contours of that risk, identifies key companies whose business models depend heavily on H-1B lines, and analyzes how the startup ecosystem may fracture, shift, or adapt under this new regime.
The Policy Shift and Its Implications
The executive order raising the H-1B visa application fee to $100,000 is part of a broader push by the administration to curb perceived abuses in high-skilled immigration, with officials arguing that the previous visa system sometimes displaced domestic workers or allowed companies to undercut wages. According to the White House, the new surcharge is intended to “curb abuses that displace U.S. workers.”
Yet the suddenness of the measure, along with its breathtaking magnitude, has roiled the tech and startup community. Previously, the cost of filing an H-1B petition was substantial but manageable for many well-capitalized enterprises. The jump to six figures per hire for new visas imposes a dramatic, structural distortion in labor economics. For early-stage ventures or those scaling rapidly, every engineering hire now becomes a six-figure bet before the first line of code is written.
The rationale of the policy wage alignment, preventing displacement, and increasing enforcement cannot be dismissed outright. But the side effects are profound. Startups that once relied heavily on overseas talent will need to reassess hiring strategies, burn rates, growth models, and geographic footprints. Many will hesitate to sponsor visas altogether, shifting hiring toward remote or local talent pools. Others may redirect expansion away from the U.S., leaning instead toward jurisdictions with more permissive or stable immigration policies.
Beyond individual firms, the broader ecosystem venture capital, talent flows, innovation clusters risks fragmentation. Investor expectations around growth, scale, and global talent arbitrage may need recalibration. Nationally, the U.S. may cede brainshare to Canada, the U.K., Germany, or Singapore, which have been actively courting foreign talent with streamlined visa regimes. Some founders and VCs are already pointing to these as attractive alternatives.
In effect, the $100,000 surcharge is less a marginal tax than a structural wall. It injects a fixed, steep financial friction into every new hire via H-1B tilting the calculus away from investment in people toward capital intensity, algorithmic leverage, or automation. For startups in capital-hungry spaces AI, robotics, biotech, deep learning the disruption could be existential.
How We Identified At-Risk Startups
To assess which startups might be hardest hit, analysts turned to public data from the U.S. Department of Labor, U.S. Citizenship and Immigration Services (USCIS), and aggregate filings in the first three quarters of the year. The metric: number of certified H-1B approvals per company. Although certified approvals don’t cleanly translate to current employment, they provide the best available proxy for how heavily each business relies on the H-1B channel.
Startups with the highest volume of certified H-1B petitions are likely the ones most exposed to the new surcharge. That list includes companies whose growth depends on technical manpower, many in AI, fintech, data infrastructure, and enterprise SaaS. Some are household names; others are quieter scale-ups whose work is deeply embedded in algorithmic, infrastructure, or automation domains.
Below, I analyze each of the top 20 (by certified approvals) companies, assessing the magnitude of exposure, their business model levers, and potential mitigations or vulnerabilities.
List of 20 Startups with High H-1B Dependence
-
ByteDance
As the parent company of TikTok and a global technology conglomerate, ByteDance leads the list with 1,360 certified H-1B approvals. With a workforce base spanning multiple geographies, ByteDance likely strings U.S. R&D and engineering nodes in its global architecture. The new fee threatens to raise the marginal cost of U.S. engineering centers. ByteDance may respond by shifting hiring to non-U.S. locales or reducing incremental expansion in American offices. -
Databricks
With roughly 248 approved H-1Bs, the data infrastructure pioneer sits squarely in harm’s way. Databricks’ growth depends on engineering scale and talent arbitrage across geographies; the new cost will make U.S. hiring significantly more expensive relative to locations like Bangalore or Toronto. -
Stripe
Known for its payments infrastructure, Stripe has 151 certified H-1B approvals. Payments infrastructure requires both deep domain expertise and robust engineering teams. For Stripe, each incremental hire becomes costlier, pressuring margins or requiring higher revenue per engineer to justify the overhead. -
OpenAI
With 76 approved H-1Bs, OpenAI is deeply exposed. As one of the vanguards of generative AI, OpenAI competes globally not just on product but on talent the world’s top researchers and engineers. The surcharge may push more of that talent to choose jurisdictions like Canada, Europe, or Singapore where entry costs are lower. -
Cohesity
As a data security and management startup, Cohesity shows about 65 approvals. Their product domain demands strong engineering teams with security, storage, and distributed systems skills. The new cost structure compresses room for talent margin and may hamper expansion. -
Applied Intuition
With also 65 certified approvals, Applied Intuition works in autonomous systems and simulation. The autonomous vehicle, robotics, and simulation space is capital- and talent-intensive higher barriers to hiring may shift some R&D out of the U.S. -
Saviynt
With 55 certified approvals, Saviynt operates in identity governance, compliance, and security. Demand for security expertise is rising, but the surcharge raises the cost of scaling engineering staff. -
Anthropic
Anthropic, an AI research firm, registers 41 approved H-1Bs. As a competitor to OpenAI, Anthropic is heavily dependent on high-end ML research staff. The surcharge introduces an added barrier to executing global hiring strategies. -
Scale AI
Scale AI, centered on data labeling, high-throughput systems, and ML infrastructure, has 34 approvals. The new surcharge compresses margins per engineer and may make certain scaling steps cost-prohibitive. -
Ripple
Ripple, in the blockchain and payments space, has 33 approvals. While some of its workforce is dispersed globally, the U.S. hubs are critical. The surcharge undermines the economics of U.S.-based engineering growth. -
Gusto
Gusto, a payroll and HR platform, also shows 33 approvals. Its domain involves regulatory complexity, security, and fintech integrations, requiring seasoned engineers. The surcharge raises the overhead of each new hire. -
Rivos
Rivos, a hardware or systems-scale startup (often linked to security environments), has 32 approvals. Given hardware, firmware, and systems engineering demands, talent is scarce; the surcharge adds a steep barrier to growth. -
Magic Leap
With 28 certified approvals, Magic Leap builds augmented-reality hardware and software. Its engineering needs span optics, AR/VR, embedded systems. The cost shock may slow new R&D in U.S. labs or push development to lower-cost geographies. -
Verkada
Verkada, working in physical security, cameras, cloud infrastructure, has 26 approvals. The interplay of hardware and cloud software demands a broad talent stack; higher visa fees shrink capital available to talent expansion. -
Plaid
With 24 approvals, Plaid is in the fintech infrastructure space (APIs connecting bank accounts, open banking). Fintech demands reliability, security, and regulatory compliance. Each engineer added now carries an additional six-figure cost. -
Nuro
As a robotics/autonomous delivery company, Nuro has 23 approvals. Robotics and autonomy engineering are deeply specialized; hiring friction may push development offshore or slow U.S. deployment. -
Form Energy
With 23 approvals, Form Energy operates in energy storage, battery systems, and long-duration energy innovation. Clean energy hardware and power systems are specialized domains. The surcharge may slow labs, prototyping, and scale-up. -
Deel
Deel, a remote payroll and HR platform, logs 22 approvals. Ironically, a company that itself supports distributed work may be forced to reduce U.S. hiring given the massive surcharge; it might double down on remote-first models and non-U.S. team growth. -
Zipline
Zipline, known for drone logistics, sees 21 approvals. Drone operations intersect hardware, software, autonomy, and regulatory systems: each new hire in systems or autonomy now carries a steep fixed overhead. -
Brex
Brex, in fintech and corporate credit, also has 21 approvals. Its engineering teams support credit modeling, infrastructure, compliance, security, and payments. The surcharge raises the cost curve at scale.
Degrees of Market Exposure & Sensitivity
Not all of these startups will be equally devastated. Some have deep war chests, diversified global hiring strategies, or less reliance on incremental engineering growth. But the sensitivity gradient is real. To understand who is most at risk, one must consider:
-
Engineering headcount dependence: Firms scaling rapidly or in talent-intensive domains (AI, robotics, security) are more exposed.
-
Burn rate and runway: Startups with limited runway will struggle to absorb sudden hiring cost increases.
-
Ability to pivot hiring strategy: Some firms can shift more aggressively toward remote or non-U.S. hubs; others, especially hardware or systems companies, require local labs.
-
Global footprint flexibility: If a company already has operations in Canada, Europe, or Asia, shifting R&D might be easier.
-
Capital reserves & funding environment: Well-funded startups may weather the shock better; those reliant on future rounds may find investor calculus shifting.
Put bluntly, the surcharge acts as a steep upfront tax on scaling teams rather than an incremental cost. The marginal cost of hiring is now drastically higher for some, prohibitively so.
Strategic Responses & Mitigation Pathways
What can these startups do (or already are doing) to respond? The options are limited and often imperfect, but some strategies may partially offset exposure:
1. Greater reliance on remote or non-U.S. talent
Many firms will accelerate hiring outside the U.S., either remote or via distributed structures. Canada, the U.K., Germany, India, Israel, Singapore, and Australia become more attractive. By shifting more roles offshore, they minimize new H-1B applications and thus avoid the surcharge.
However, this strategy has tradeoffs: managing remote teams across time zones, aligning technical culture, and ensuring data and regulatory compliance. Some product domains especially hardware, embedded systems, or AI requiring physical labs resist full remote decentralization.
2. Relocate or expand R&D centers abroad
Startups may lean into opening or expanding innovation centers outside the U.S. Some already maintain R&D hubs in Bangalore, London, Toronto, or Tel Aviv; now those hubs may become default growth poles. The U.S. office may shift more toward business development, marketing, or compliance.
Yet moving core functions abroad may dilute U.S. brand, complicate investor narratives, or raise coordination costs. Firms will need rigorous leadership alignment and robust cross-border governance.
3. Prioritize internal talent development and upskilling
With external hiring made more expensive, startups may invest more in developing junior or mid-level domestic talent. Upskilling programs, internal promotion paths, apprenticeship models become more valuable. While this is a long-term play, it softens reliance on expensive external hires.
That said, the pace of execution matters. For scaling startups facing aggressive timelines, internal development alone may not suffice.
4. Lean harder on automation, tooling, and AI augmentation
If hiring engineers becomes costlier, startups will push more toward productivity, automation, or AI-driven augmentation to reduce headcount growth pressure. Investments in developer tools, code generation systems, auto-scaling infrastructure, and synthetic data may accelerate.
Ironically, those very startups in AI or infrastructure might gain dual leverage: they use their own tools to mitigate the hiring drag.
5. Rework the finance and pricing model
Some firms may adjust pricing or business models to absorb the additional cost. For example, startup SaaS firms might charge higher “engineering overhead” in contracts, push pricing increases, or structure margins to partner more aggressively.
But clients especially cost-sensitive enterprises might resist paying higher fees tied to engineering cost inflation. The elasticity of demand becomes a key factor.
6. H-1B grandfathering, exemptions, or legislative pushback
Some companies may explore policy carve-outs: for example, lobbying for exemptions in R&D-heavy sectors, grandfathering schemes, or wage-level safe harbors. The administration has floated exemptions (e.g. for doctors) or alternative visa structures. Firms with regulatory or lobbying capacity may attempt to negotiate for relaxations or waivers (e.g., adjusting the fee structure for small businesses or venture-backed growth stages).
7. Stretching hiring schedules and pruning headcount expectations
Firms may slow hiring cadence, demand more output per engineer, or hire more conservatively. Growth projections may be trimmed; investor expectations must be reset. Some expansions may wait until fundraising rounds reassure runway.
8. Geographic arbitrage for product launch markets
Startups may shift product launches or growth scaling phases to regions where costs are lower and incentives more favorable. For example, launching new features first in non-U.S. markets, then retrofitting for the American market.
Broader Ecosystem Effects & Risks
Venture capital recalibration
VCs will adjust models. If hiring costs are now more rigid and expensive, projections around “engineers per dollar” weaken. Investors may favor capital-intensive models over labor-intensive ones or bias toward founder-architected, leaner teams. Terms, multiples, and valuation criteria may shift accordingly.
Early-stage firms may become less attractive unless they show early technical leverage or IP defensibility. Closer scrutiny will be given to engineering productivity metrics (revenue per engineer, output per person) and capital efficiency. In sectors like AI, cybersecurity, or biotech, where domain knowledge is hard to shortcut, VCs may demand stronger operations before writing big checks.
Talent flows and brain drain risk
As the U.S. raises the threshold, other countries will look more competitive. Canada, with its tech visas and startup-friendly immigration, may absorb more talent. The U.K.'s recent visa reforms and tech visas may become a compelling alternative. Europe (Germany, France, Netherlands) also may benefit. Over time, the U.S. risks losing its attractiveness as a “first stop” for top global engineers or researchers.
Additionally, existing U.S. talent may increasingly consider moving to locales with lower regulatory friction or more stability, particularly if their companies decide to shift engineering hubs overseas. The sense among global technical talent may shift: why go where hiring new colleagues costs six figures, when in Toronto or London the barrier is trivial?
The result could be a long-term devaluation of U.S.-based innovation clusters Silicon Valley, Boston, Seattle if talent magnetism weakens.
Innovation concentration and fragmentation
If policy friction becomes too steep, innovation may fragment geographically. New “Silicon Valleys” could emerge in Toronto, Berlin, Bangalore, or Singapore. Startups may avoid U.S. incorporation or headquarters altogether in favor of jurisdictions with friendly ecosystems. Over time, the innovation leadership in categories like AI, biotech, quantum, and deep tech may tilt toward those global clusters.
Conversely, large U.S. incumbents Google, Microsoft, Amazon, Apple have scale and capital heft to absorb the surcharge. In effect, the barrier might raise the floor and tilt advantage away from nimble startups toward entrenched giants.
Regulatory and geopolitical pushback
The policy may invite legal challenges or bipartisan resistance in Congress. Tech industry groups, immigration activists, and business coalitions may lobby for relief. Over time, if the economic damage is perceived as severe, the policy could be rolled back, softened, or supplemented with carve-outs.
In parallel, international governments will likely respond by lobbying, matching visa incentives, or promoting “dual passports of innovation” to attract U.S. displaced startups.
Effects on specific sectors
-
AI / Deep Learning / ML Research: Arguably the most vulnerable sector, since research quality depends on top-tier global talent. The surcharge raises the upfront cost of hiring any new researcher.
-
Robotics / Autonomous Systems: Hardware, embedded engineering, simulation, and control system work often require collocation, labs, and specialized gear; remote hiring is less feasible, meaning U.S. labs may weaken.
-
Fintech / Payments / Infrastructure: These sectors often scale rapidly and require strong security, regulatory, compliance talent. But they may also more easily outsource non-critical infrastructure offshore.
-
Biotech / Drug Discovery: Though not heavily represented in the top 20, biotech and related deep sciences may also feel pressure wet labs, regulatory engineering, and bioinformatics require specialist hires.
-
Cybersecurity / Privacy / Identity Tech: Demand is rising, and companies often rely on global talent; higher hiring friction may slow innovation or shift centers offshore.
Risk Tiering & Severity Estimates
To better understand which firms are in the “danger zone,” we can define risk tiers:
-
Critical Risk (High Exposure + Weak Offset Capacity): Startups with large headcounts, high per-engineer cost sensitivity, thin margins, and limited ability to shift abroad. Examples: OpenAI, Anthropic, Magic Leap, Rivos, Nuro.
-
Moderate Risk (Exposure + Some Mitigation Options): Firms with meaningful H-1B dependence but with wider geographic or product flexibility. Examples: Databricks, Stripe, Plaid, Gusto.
-
Lower Risk (Diversified or Less Dependence): Firms with lower volumes of H-1B petitions or more modular roles that can shift offshore. Examples: Deel, Ripple, Zipline.
Even those in “lower risk” are not immune. The surcharge raises the floor cost of new engineering hires across the board, squeezing even opportunistic growth.
It’s almost certain that across the 20, hiring plans for 2025–2026 will be revised downward, buffer headcounts will be reduced, and budgets will shift away from headcount-led expansion to capital leverage, automation, or outsourcing.
Case Studies: Adaptive Paths & Hypothetical Scenarios
OpenAI & Anthropic: AI Talent Wars
OpenAI, even before this move, competes globally for superstar researchers and engineers. With 76 approved H-1B petitions so far, OpenAI’s dependence on U.S.-based hires is meaningful. Under the new fee regime, every new hire in U.S. labs now carries a $100,000 overhead, making the marginal cost of bringing in a foreign researcher materially higher than onboarding an equivalent in Canada, the U.K., or Europe.
OpenAI might respond by shifting more research labs abroad perhaps expanding London, Toronto, or EU nodes and routing new talent through those hubs first. It might also accelerate internal automation of model training, tooling, or recruiting dynamic pipelines to reduce dependence on new headcount.
Anthropic, similarly, may choose to stagger U.S. hires or front-load them before legislation fully takes effect. It might also lean more heavily into international recruitment, collaborations with foreign academic institutions, or open research programs outside the U.S. to neutralize the barrier.
Databricks & Stripe: Infrastructure Plays
Databricks, with its data pipeline and AI infrastructure push, needs scalable engineering teams. The surcharge may force it to slow aggressive hiring in U.S. R&D hubs and lean more heavily on satellite engineering centers in India or EU. It may also prioritize internal optimization to reduce headcount growth.
Stripe, as a payments and infrastructure backbone, must balance regulatory presence in the U.S. with engineering economics. The surcharge might push Stripe to evolve more of its internal tooling, enforcement, and backend to non-U.S. teams or reallocate more R&D outside American soil.
Robotics and Hardware (Nuro, Magic Leap, Rivos)
These firms have the toughest tradeoffs. Robotics and hardware engineering often require physical labs, prototyping, testing, and tight coordination. Remote work is limited. For Nuro and Magic Leap, the surcharge may force scaling abroad or reduce lab expansions in the U.S.
Rivos, due to its national security or high-assurance systems, may face additional compliance constraints. Its ability to relocate or remote work may be limited, leaving it exposed to the full shock of hiring cost inflation.
Challenges & Constraints of Mitigations
While the strategies above offer mitigation paths, each comes with limitations:
-
Coordination costs & culture dilution — splitting core teams across borders increases communication friction, misalignment risk, and cultural fragmentation.
-
Data governance, IP protection, and regulatory compliance — moving or decentralizing work across jurisdictions may invite legal or compliance complexities, especially for fintech, health, or national security–adjacent domains.
-
Talent competition abroad intensifies — other countries will vie aggressively for displaced talent, bidding up salaries or benefits. Startups relocating abroad may find they still face fierce competition.
-
Venture visibility & legitimacy — U.S. headquarters often confer signaling advantages to investors, customers, and partners. Relocating core functions abroad may carry reputational or logistical drawbacks.
-
Institutional inertia and sunk costs — many startups have built ecosystems, labs, partnerships, and regulatory relationships in the U.S. Severing or shifting these is costly and risky.
-
Delayed impact & funding constraints — the surcharge shock may manifest gradually but funding cycles may not allow for extended adjustments. Startups in the middle of scaling may not have the luxury to pivot wholesale.
Thus, mitigation will likely be partial, staggered, and messy. Some may succeed; others may falter under the weight.
Long-Term Outlook & Strategic Implications
Rebalancing global innovation
Over time, the U.S. may lose its edge as the undisputed magnet for ambitious tech talent. As regulatory, financial, and immigration friction mount, international innovation clusters become more attractive. Canada (via Start-Up Visa and high-skill programs), the U.K. (via Global Talent), Singapore, Germany, Israel all stand to gain.
Innovation may become more regionally distributed. Flagship startups may now emerge from Toronto, Berlin, or Singapore first, rather than pivoting into the U.S. The archetype of “build in Silicon Valley, scale globally” may invert.
Acceleration of capital- and automation-led growth
Startups will double down on capital leverage and automation. The winners will emphasize tooling, AI productivity, low-code infrastructure, and intelligent orchestration such that per-engineer output is maximized. Headcount growth will decelerate; capital deployment to compute, model scale, and infrastructure will accelerate.
In effect, human capital becomes rarer, more expensive, and more precious. Startups may optimize intensely on engineering efficiency and cost arbitrage.
Pressure on policy and public discourse
If the disruption proves damaging if economic growth slows, tech investments wane, or sectors drift offshore political pressure to re-evaluate the surcharge likely will rise. Tech coalitions, chambers of commerce, and affected firms may lobby vigorously for exemptions, carve-outs, or tiered discipline (e.g. reduced fees for small businesses or R&D-heavy firms). Legislative balancing will be critical.
Stratification favoring incumbents
Large tech incumbents, with vast capital reserves and internal talent pipelines, can absorb extra costs more readily than scrappy startups. Thus, the barrier may reshape competitive dynamics lean startups may find it harder to compete against entrenched giants with built-in resources. Barriers to entry rise.
Shift in venture capital behavior
VCs increasingly emphasize capital efficiency, engineering leverage, and founder-architected scarcities. The metrics of evaluation may shift from “growth by headcount” toward “growth by automation, model scale, tool yield.” Seed and early stage investments may demand more rigorous engineering traction before backing.
Emerging alternatives to H-1B
The industry may push for new visa categories, global recruitment pathways, or talent pipelines via academia and remote-first programs that sidestep U.S. restriction. Talent marketplaces, global remote-first hiring platforms, and distributed R&D may accelerate.
The $100,000 surcharge on H-1B visa applications is not a marginal tweak but a deep structural shock to the economics of scaling technical teams in the United States. It will disproportionately hit startups especially those in AI, robotics, fintech, and infrastructure forcing drastic recalibrations in hiring, geographic expansion, automation strategy, and capital deployment.
Among the 20 spotlighted startups ByteDance, Databricks, Stripe, OpenAI, Anthropic, and their peers some will manage to absorb the friction, pivot, or relocate; others may falter or slow. Over time, the policy could reshape innovation geographies, redistribute global talent flows, and favor capital-intensive giants over lean, headcount-intensive disruptors.
In response, startups must reexamine their assumptions, strengthen internal productivity, diversify talent pipelines, and, where possible, lean on alternative geographies. The resilience of the U.S. innovation ecosystem may depend on how successfully this transition is navigated or whether it is.
