AWS Loses Startups Chief Jon Jones Amid AI Talent War and Exec Churn

Amazon Web Services has lost Jon Jones, its vice president who ran the global startups and venture-capital outreach, in a move that crystallizes a wider 2025 pattern of executive churn and intensifying competition for AI talent across the cloud sector.

Background / Overview​

Jon Jones joined AWS in 2017 and rose through go-to-market and product leadership roles before taking the startups portfolio in 2024. He held the global startups and venture capital brief for roughly a year after succeeding his predecessor, who exited for a role with an AI hardware and software leader. Jones’s remit centered on keeping early-stage AI companies — the seed and Series A innovators building the next generation of models and applications — on AWS as they scaled into production, and on maintaining close ties with venture capital firms that seed that ecosystem.
The departure arrives at a time of notable leadership turnover across AWS, including reorganizations at the top of the business two years earlier and multiple senior departures through 2025. AWS’s startup-facing programs and credit incentives have been a key line of defense against competitor encroachment, and Jones had been a visible steward of those efforts. His exit therefore raises immediate questions about continuity, relationship management with VCs, and how AWS will retain influence among the startups that will define cloud-native AI demand in the years ahead.

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Why this matters: the strategic role of the startups lead​

Startups are the tip of the demand spear for AI infrastructure​

Startups — particularly those building and scaling foundation models, inference platforms, and verticalized generative-AI applications — are disproportionately expensive to run at scale. They require access to large fleets of GPUs, specialized AI chips, high-throughput storage, and expert technical support. The platform that captures them early often retains them as they mature into high-spend customers.
Jones’s job was not merely marketing; it was a blend of product advocacy, commercial structuring (credits and discounts), strategic partnership orchestration, and venture-relations diplomacy. That combination matters because early commitments can translate into multi-year, high-value cloud consumption as startups move from prototype to production.

Go-to-market influence with VCs​

A startups lead is also a bridge to venture capital. The person in that role mediates co-investments, demo days, accelerator programs, and preferential access to early-stage customers. Those touchpoints directly influence where founders choose to deploy heavy, ongoing infrastructure budgets.
Losing an executive who serves as that bridge creates a short-term vacuum in relationship continuity and increases the risk that startups — or their investors — look more seriously at rival clouds or multi-cloud deployment strategies.

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The wider context: executive churn and the AI talent wars​

Executive turnover at scale​

2025 has seen a measurable wave of leadership changes across AWS. Several engineering heads, product leaders tied to AI services, and managers responsible for mission-critical platforms have left the company or been reorganized into new team structures. This isn’t an isolated personnel note — it’s the visible side of deeper pressures: aggressive headhunting by competitors, evolving expectations for AI product leadership, and internal shifts to unify AI and agentic teams under new reporting structures.
These changes overlap with a corporate leadership transition at AWS that began in mid-2024. That transition included a CEO handover and subsequent reorgs intended to sharpen AWS’s AI strategy and accelerate product execution. Reorgs frequently trigger churn as roles and reporting relationships change; in a white‑hot market for generative‑AI leadership, they also create recruiting opportunities for rivals.

The mechanics of the talent war​

The modern AI talent war is driven by three structural realities:

• Extraordinary compensation for senior ML researchers and infrastructure engineers, often heavily weighted toward equity and rapid vesting.
• Intense competition from specialist AI companies, hyperscalers, and chip firms that offer either frontier-model missions, equity upside, or a combination of both.
• Cultural and workplace differences (remote flexibility, research freedom, team composition) that matter to senior hires and often determine where elite candidates wind up.

These dynamics have made hiring and retention especially difficult for firms with rigid compensation bands, strict return‑to‑office policies, or slower organizational pace — conditions that have been highlighted internally within some large companies as friction points in the battle for top AI talent.

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What AWS has done to defend its startups moat​

Credits, accelerators, and partnerships​

AWS has leaned heavily into programs that make it cheaper and faster for startups to build on its cloud. The portfolio includes multi-year promotional credits, a prominent Generative AI Accelerator, industry-specific fellowship programs, and an overarching Activate program that bundles credits with go‑to‑market and technical support.
Key program attributes that matter to startups:

• Substantial credit packages that can reduce early cloud bills materially.
• Hands-on mentorship and technical enablement tied to product teams and specialized engineering resources.
• Invitations to high-visibility events where startups can meet customers, partners, and investors.
• Access to hardware accelerators and inference-optimized silicon as part of the platform roadmap.

These incentives are designed to buy early loyalty. They are a powerful defense in markets where early compute commitments predict long-term spend.

Product positioning: Bedrock, SageMaker and custom silicon​

Beyond credits, AWS has invested in platform-level services that appeal to model builders: managed model-hosting, tooling for model development, and access to a growing set of foundation models. AWS’s pitch combines managed services (for faster time-to-market) with custom silicon and capacity commitments (to address cost and scale for heavy model training and inference).
Operationally, this strategy targets multi-dimensional lock-in: if a startup optimizes pipelines and tooling around a specific provider’s managed services, migration costs rise with scale.

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Competitive pressures: why rivals smell opportunity​

Multicloud momentum and model partnerships​

Startups increasingly adopt multi-cloud strategies to avoid vendor concentration risk and to take advantage of the best pricing, unique accelerators, or strategic partnerships that other platforms enable. At the same time, model vendors and AI specialists are forming exclusive or semi‑exclusive relationships with clouds that offer privileged compute access or co-investment models, which can tilt startup decisions.
This combination of multi-cloud hedging and vendor-cloud partnerships creates a dynamic where the cloud that wins in the long run is the one that can marry the best technical fit with the most persuasive commercial and ecosystem offers.

Rival moves that amplify recruitment pressure​

Competitors have pursued several levers that attract both startups and top engineers:

• Deep equity upside and aggressive cash compensation packages for senior AI hires.
• Research-first organizations that promise publication and innovative freedom for ML researchers.
• High-profile model launches and integrated product experiences that offer prestige and visible career impact.
• Verticalized offerings (e.g., data-center networking for AI, GPU/ASIC specialization) that promise performance differentiation.

When rivals execute on these levers, they not only pull market share but also make retention harder for incumbents.

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Risks for AWS and Amazon​

Talent flight and institutional knowledge loss​

When senior leaders depart, they take institutional knowledge and relationships with them. For a role that is inherently relational — one that depends on long-standing ties with investors, founders, and partner organizations — the loss of continuity can be costly.
Short-term impacts include a slowdown in deal flow and potential miscommunications with portfolio startups. Medium-term risks include a shift in where high-growth startups choose to base production workloads.

Execution risk in a capital-intensive AI cycle​

AWS is executing a massive capital build to supply the compute required by generative AI. That investment thesis assumes continued and accelerating demand from model developers and enterprises. If leadership churn disrupts customer onboarding or partner cultivation, some of that growth could accrue to rivals that are actively stealing talent and early-stage accounts.

Reputational and strategic signaling​

High-profile exits generate market headlines that can be interpreted as signs of deeper strategic problems, even when they are individual decisions. That reputational effect can complicate hiring, investor relationships, and customer perceptions — particularly when competitors use departures as evidence of momentum in their favor.

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Strengths and countervailing advantages AWS still brings​

Market-leading scale and proven operational reliability​

AWS remains the most widely used cloud in the world. Its scale, global footprint, security certifications, and variety of managed services give it a durable advantage that can be decisive for many enterprises and startups that require predictable uptime, compliance, and global reach.

Generous startup incentives and a substantial existing startup base​

AWS’s credit programs, accelerator cohorts, and historical pipeline — millions in total distributed credits across hundreds of thousands of startups — still represent a meaningful moat. Many founders are pragmatic: cost and operational friction still matter more than brand or research prestige for production workloads.

Native access to a diversifying set of AI models and silicon​

AWS’s partnerships, investment in custom silicon, and managed model layer mean it can offer many of the technical building blocks startups need — from training to inference — with integrated security and compliance. For startups building production systems, those operational guarantees are often the deciding factor.

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How AWS should respond: pragmatic moves to steady the ship​

• Reinstate and accelerate leadership continuity plans
• Appoint an interim leader with strong venture relationships and a visible mandate to maintain continuity.
• Commit to a well-communicated succession plan to reassure ecosystem partners.
• Recalibrate compensation and workplace flexibility where it materially affects recruiting
• Design targeted compensation bands and retention incentives for elite AI researchers and product leaders.
• Increase remote/hybrid flexibility for roles where that helps attract senior talent without compromising core operations.
• Double down on credibility-building: research visibility + customer wins
• Sponsor independent benchmarks, publish applied research case studies, and highlight marquee startup success stories that show AWS as a production-first partner for AI.
• Protect and extend startup incentives strategically
• Maintain accelerator programs and credits but pair them with performance-based milestones that lock-in longer-term usage.
• Offer flexible, contractually supported capacity commitments for startups that scale rapidly, reducing migration risk.
• Strengthen partner orchestration
• Use partnerships with specialized infrastructure providers, model vendors, and chip companies to create bundled offers that are hard to replicate.

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What startups, VCs, and enterprise customers should watch​

• Leadership announcements and interim appointments: who fills the gap, and do they have the VC network to maintain deal flow?
• Any material changes to credits or accelerator terms: a retreat would be a market signal; an expansion would be a commitment signal.
• Hiring and retention moves at AWS across AI engineering and product ranks: a renewed hiring cadence could restore confidence.
• Competitor playbooks: watch for rivals offering startup teams not only cash and credits but also R&D partnerships and co-development incentives that tie model IP to their platforms.

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What’s verifiable and what remains speculative​

• Verifiable items that shape the narrative: the startups leader’s departure; the broader pattern of senior AWS exits; the existence of AWS startup credit programs and the size of recent accelerator commitments; the corporate leadership change that saw a previous CEO step down. These are established operational facts that underpin the strategic stakes.
• Speculative or partially verifiable items: whether any specific exit signals a wholesale strategic pivot inside AWS, or whether one departure will accelerate defections across AWS’s startups programs. Those are plausible scenarios but depend on follow-on hiring decisions, program changes, and market movements over the next quarters.
• Market forecasts (for example, optimistic growth trajectories for AWS tied to AI demand) are analyst projections grounded in data and modeling — useful for context, but not guarantees. They should be read as scenario planning, not determinative outcomes.

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Sizing the impact: short-term vs. long-term​

Short-term (0–6 months)

• Disruption risk is concentrated in relationship management and deal cadence with early-stage startups.
• If an interim leader is named quickly and program continuity is publicly reaffirmed, the practical impact can be limited.

Medium-term (6–18 months)

• The real test is whether AWS converts accelerator and credit recipients into durable, high-spend customers as they scale.
• Talent retention across specialized AI engineering and product teams will determine AWS’s ability to deliver platform features founders need.

Long-term (18+ months)

• The winner among clouds will be the provider that combines scale, predictable pricing, best-in-class operational tooling for AI, and a talent-driven roadmap that sustains innovation.
• Leadership stability matters here only insofar as it underpins product direction and partner relationships over cycles measured in quarters, not weeks.

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Practical implications for WindowsForum readers and IT decision makers​

• For enterprises and IT teams planning AI deployments, the immediate takeaway is operational: cross‑cloud resilience and deployment portability remain prudent strategies.
• For companies evaluating vendor lock-in versus integrated managed services, this moment highlights the trade-offs: a mature cloud offers operational certainty, while multi-cloud and hybrid strategies can mitigate supplier-specific risk.
• For Windows-focused shops that historically favored a major productivity-cloud provider, these shifts are a reminder that infrastructure selection for AI workloads will increasingly be a business decision balancing price, latency, regulatory constraints, and strategic partnerships.

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Final assessment: stability through execution, not headlines​

Jon Jones’s departure is consequential in its domain — startups and VC outreach — because those relationships are upstream drivers of future cloud demand. But it is not, in itself, determinative of AWS’s long-term prospects. AWS retains material strengths: scale, established startup pipelines, and deep operational capability.
The more pressing question for AWS is not whether a particular executive leaves, but whether the company can combine:

• rapid, credible hires for critical AI roles,
• pragmatic adjustments to incentives where they materially influence hiring,
• and an operational sprint to convert startup programs into long-lived production relationships.

If AWS moves decisively on those fronts — stabilizing leadership, protecting program continuity, and demonstrating technical advantages at scale — it will blunt short-term investor and media anxieties and keep the company competitive in the high-stakes market for AI infrastructure.
If not, the immediate risk is erosion of influence among the startups that will ultimately determine which clouds carry the next generation of AI workloads. The cloud wars are now as much about people and relationships as they are about chips and datacenters; that is why executive retention at the intersection of startups and AI is a strategic priority, not merely a human‑resources problem.

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AWS’s next steps will be closely watched by founders, investors, and rivals. Filling the startups lead role with speed and thoughtfulness — or articulating a clear alternative approach for VC and early-stage engagement — will be the clearest signal that AWS is determined to keep its grip on a critical pipeline of future AI demand.

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Source: WebProNews AWS VP Jon Jones Departs Amid 2025 AI Talent Wars and Exec Exits

 

[ChatGPT]

Amazon Web Services’ startups czar Jon Jones has left the company after roughly a year in the role, a departure that lands amid a broader wave of executive exits at AWS in 2025 and deepens scrutiny of the cloud giant’s ability to hold ground in the fiercely contested AI talent wars.

Background / Overview​

Jon Jones was appointed vice president and global head of AWS Startups in late 2024, taking over responsibility for the company’s outreach to early‑stage companies, venture capital firms, and accelerator programs — a role designed to drive long‑term cloud commitments from the next generation of AI-first businesses. His elevation followed the exit of predecessor Howard Wright to Nvidia and signaled AWS’s explicit push to lock in startups building and scaling generative‑AI models and services. The departure was confirmed by an AWS spokesperson and reported publicly in industry briefings. The Information’s reporting that AWS verified the exit was echoed by trade outlets and industry coverage, which then framed Jones’s move within a pattern of senior departures at AWS through 2025. Those outlets describe Jones as a seven‑year AWS veteran who spent the last year focused heavily on courting AI startups — strategic work that AWS views as an upstream driver of future, high‑value cloud spend.

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Why the start‑ups lead role matters​

Startups are disproportionately influential in shaping long‑term cloud economics for two reasons: they are often the earliest adopters of specialized AI infrastructure, and the platform that captures them during the training and scaling phases frequently retains them as they become high‑spend customers.

• Early AI startups demand large fleets of GPUs, high‑throughput storage, specialized network topologies, and hands‑on product and sales support.
• Startup programs — credits, accelerators, technical mentorship, and go‑to‑market introductions — are designed to convert short‑term subsidies into long‑term platform dependency.
• The startups leader functions as both a commercial negotiator and a relationship manager with venture capital firms that drive founder decisions.

AWS has invested heavily in precisely these levers: the AWS Generative AI Accelerator and multi‑million‑dollar promotional credit programs for seed and Series‑A teams, access to SageMaker and Bedrock model hosting, and targeted partnerships with chip and silicon providers. Those investments are tangible incentives intended to harden loyalty while competitors make their own plays. Losing a visible steward of that ecosystem creates a continuity risk: startups and VCs value steady, personal relationships. When the person who signs credit packages and runs accelerator cohorts departs suddenly, founders often pause when evaluating vendor lock‑in and may accelerate multi‑cloud or competitor assessments.

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The wider 2025 churn at AWS: a short list and what it signals​

AWS’s 2025 personnel moves are more than anecdotes. Industry trackers and reporting identify an array of senior exits spanning product, engineering, AI leadership, and data center operations.

• Trade reporting compiled a list of at least eight prominent AWS departures during 2025, including leaders tied to Amazon Q, Bedrock, OpenSearch Serverless, AWS Glue, and global data center operations. Several of these people moved directly to rivals or specialized AI infrastructure firms.
• High‑profile generative AI lead Vasi Philomin left AWS for Siemens in mid‑2025, a move noted across press releases and analyst coverage.
• Reuters documented that AWS cut at least hundreds of cloud unit jobs in July 2025, underscoring organizational changes that accompany strategic realignment and cost management. That combination of layoffs and voluntary exits has amplified headlines about structural change within AWS.

This turnover pattern matters for three reasons:

• Execution risk: product roadmaps for complex offerings like Bedrock, SageMaker, and agentic capabilities require deep institutional knowledge — the types of domain experts who are leaving.
• Signaling: frequent senior exits can create a perception problem that rivals exploit in recruitment and in pitching startups.
• Competitor momentum: when former AWS executives join cloud‑adjacent or AI‑native players, they carry relationships and credibility that can accelerate customer or talent movement.

Industry observers see the exits as a symptom and a cause: structural reorgs designed to accelerate AI efforts often create churn, and churn in turn makes it harder to sustain relentless execution. That loop is especially costly in generative AI, where time to market and engineering talent scarcity define winners and losers.

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Verifying the key claims: what’s confirmed and what’s still speculation​

Several elements in reporting about Jones’s exit and AWS’s 2025 churn are verifiable:

• Jon Jones’s departure was publicly reported and confirmed by AWS via industry briefing outlets.
• A measurable list of notable AWS executives have left in 2025, documented by trade outlets that tracked moves and LinkedIn updates.
• AWS announced substantial startup incentives in 2024 and 2025, including a $230 million commitment to generative‑AI startups and an expanded accelerator program. Those programs and their credit limits are documented on AWS channels.
• Reuters reported the July 2025 AWS job cuts, confirming organizational changes that are more than rumor.

Areas that remain at least partially speculative and should be treated cautiously:

• Motivations for Jones’s departure: while departures are often attributed to “internal frictions” or “strategic shifts,” those reasons are rarely confirmed publicly. Insiders and former colleagues may offer interpretations, but absent a first‑party statement from Jones, claims about friction or disagreement are unverified and should be labeled as such.
• Long‑term strategic pivots: a single executive departure does not, by itself, prove a wholesale change in cloud strategy. The real indicator will be hiring decisions, program continuity, and product delivery over the coming quarters.

Where reporting extends into inference, the cautious formulation is to describe likely scenarios rather than assert causality. The immediate fact set supports concern about continuity — not a foregone conclusion about market share loss.

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Competitive dynamics: rivals, recruiting, and where AWS stands in AI​

The modern AI talent market is driven by giant swings in compensation, equity upside, research reputation, and mission clarity. AWS faces two structural headwinds relative to some rivals:

• Compensation and flexibility: Business Insider reporting based on internal documents shows Amazon’s rigid compensation bands and more prescriptive return‑to‑office policies have hindered some efforts to recruit elite GenAI talent. Those documents prompted internal discussion and signaled that AWS may have to adjust incentives and workplace flexibility to stay competitive.
• Research prestige and product marquee: competitors with conspicuous investor narratives (for example, Microsoft’s partnership with OpenAI and Google’s announcements) have created clearer front‑of‑mind product stories, which can help in recruiting researchers who want brand‑defining work.

Despite these challenges, AWS retains durable strengths:

• Scale and operational reliability: AWS’s global footprint, security posture, and breadth of managed services remain unmatched in many enterprise contexts. Scale matters for production AI.
• Startup incentives and programs: multi‑million‑dollar credit offers and accelerators are concrete tools to keep early customers building on AWS — and those programs are active and public.
• Product investment: AWS has pushed into agentic AI with new announcements such as Amazon Bedrock AgentCore, expanded SageMaker customization for its Nova models, and S3 Vectors storage optimized for vector workloads — all signals it is investing in the technical fabric needed by model builders.

The competitive frame here is not binary. AWS’s advantage is production readiness; rivals’ advantages are speed of recruiting and headline product integrations. The cloud war will be decided on combinations of price, tooling, ecosystem relationships (including VC networks), and the ability to attract and retain core AI engineers and product leaders.

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Short‑term and medium‑term implications for startups, VCs, and enterprise customers​

Short term (0–6 months)

• Relationship continuity matters most. If AWS names an interim leader quickly and publicly reaffirms its startup programs, the practical disruption can be limited.
• Startups in active accelerator cohorts or with near‑term scaling plans will watch contractual terms and capacity commitments closely; those are the levers that determine migration cost and friction.

Medium term (6–18 months)

• The test is conversion: how many accelerator recipients translate into durable, large‑spend customers? Execution on product features (ease of model hosting, inference cost, autoscaling for GPUs) will determine the outcome.
• Talent retention in key engineering groups (training infrastructure, orchestration, data platforms) will shape AWS’s ability to deliver features that matter to founders.

For enterprise IT and Windows‑centric shops making platform decisions, a pragmatic approach is warranted:

• Evaluate vendor lock‑in risk realistically: vendor‑specific managed services accelerate development but raise migration costs for AI pipelines.
• Consider multi‑cloud and hybrid architectures for critical workloads to preserve negotiation leverage and resilience.
• Prioritize evaluation of developer productivity on each platform — not only headline features but how teams actually build and operate model training and inference pipelines in production.

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How AWS can and likely will respond​

Restoring confidence requires pragmatic, visible moves that address both people and product:

• Appoint interim leadership with deep VC network ties and a mandate for continuity to reassure founders and investors.
• Preserve accelerator / credit commitments and add contractual capacity guarantees for graduating startups that scale — making it more expensive to leave.
• Reassess compensation and RTO policies for critical AI roles where market forces are most acute; targeted, role‑specific pay bands and hybrid work arrangements have worked elsewhere.
• Publish applied research and independent benchmarks that highlight AWS’s production advantages — a credibility play to counter the narrative that AWS is only “infrastructure.”

These suggestions align with playbooks used by hyperscalers that have navigated similar churn. Success hinges on speed and clarity: the market reacts to action more than promises.

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Risk assessment: what to watch and where AWS is exposed​

• Talent flight: continued exits in AI‑infrastructure and product roles would constructively degrade AWS’s roadmap delivery capabilities. Watch leadership announcements and high‑value hire patterns.
• Partner confidence: VCs and founders respond to perception; an extended period of instability could nudge some teams toward multi‑cloud strategies, especially if rivals pair aggressive hiring with credit and R&D co‑development incentives.
• Execution on agentic and inference economics: announcements like Bedrock AgentCore and S3 Vectors are strategic, but technical and commercial execution (latency, cost per inference, ease of tooling) will determine whether those features convert to lock‑in.

Caveat: media narratives amplify signals; not every departure equals crisis. AWS still commands market share and profit contribution that competitors envy. The critical measure is whether AWS translates scale into differentiated developer productivity and favorable price/performance for model builders.

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What this means for the cloud market and the AI ecosystem​

This episode illustrates two broader dynamics shaping cloud and AI in 2025:

• The cloud war is now as much about people and partnerships as it is about chips and datacenters. Senior leaders who control relationships with venture capital and startup ecosystems are high‑leverage assets.
• Generative and agentic AI have raised both the stakes and the friction. Engineering talent scarcity combined with structural compensation differences across companies makes leadership stability and tactical incentives decisive.

For buyers, the practical risk is not that AWS will vanish, but that friction in recruitment and leadership gaps can slow feature delivery and erode partner confidence. That creates opportunities for rivals to make targeted gains — especially among AI startups that prize research freedom, equity upside, or differentiated silicon.

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Final assessment: stability through execution, not headlines​

Jon Jones’s exit is notable because the startups role is an important upstream lever for future cloud demand. But it should be interpreted as a stress point, not a strategic obituary. AWS still retains deep advantages — global scale, broad managed services, and substantial startup incentives — that are material in enterprise and production contexts. Three outcomes are plausible over the next 12–18 months:

• AWS stabilizes quickly: appoints experienced internal leadership, reaffirms accelerator and credit programs, and accelerates feature delivery for Bedrock and SageMaker — preserving its startups moat.
• AWS adjusts compensation and workplace policies for targeted AI roles, slowing attrition and improving recruitment competitiveness — a people‑centric fix that reduces execution risk.
• Prolonged instability: continued departures, combined with execution gaps on agentic AI and pricing pressures, allow rivals to chip away at AWS’s influence among the most dynamic AI startups.

The most probable near‑term scenario is a mix: AWS will move to reassure partners publicly while quietly recalibrating incentives and leadership roles. The cloud leader’s ability to convert large capital investments, product enhancements, and startup credits into reliable long‑term consumption will determine whether headlines about churn matter in the long run.

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Practical checklist for IT decision makers and startup founders​

• Confirm contractual capacity guarantees and exit costs before committing critical AI production workloads to a single provider.
• Track leadership announcements in vendor accounts and evaluate program continuity (accelerator cohorts, credit windows, support SLAs).
• Prioritize pilot projects that validate operational cost and latency for inference at scale on each platform under consideration.
• Keep cloud‑agnostic automation and CI/CD pipelines where possible to reduce migration friction if strategic vendor changes occur.

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AWS’s departure of Jon Jones is a timely reminder that in a market defined by extreme technical demands and acute talent competition, people are strategy. What matters now is not the headlines but the follow‑through: who AWS appoints next, how quickly it stabilizes its startup programs, and whether it can deliver the product and economic advantages that make migration costly and loyalty rational. The answers to those questions will shape the cloud landscape for the next wave of generative‑AI businesses.

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Source: WebProNews AWS VP Jon Jones Departs Amid 2025 Exec Exits, Raising AI Concerns