The Defense Paradox in Earth Observation: The Enabler and the Hindrance

This essay has been months in the making and leverages everything I have learned about Earth observation over nine years of consulting in the sector across EO companies, enterprises using EO and investors. It is the longest piece I've written for TerraWatch - and likely the most controversial.

The question it tackles - whether commercial EO can surpass defense - is more relevant than ever as companies and investors reckon whether the current defense funding cycle is episodic or the new status quo.

If you find yourself agreeing with what's written, great. If you don't, even better. Essays like this are meant to provoke discussion. Feel free to share your opinions!

Here is a puzzle: Earth observation (EO) satellite companies have raised billions in venture capital and SPAC money on the promise of massive commercial markets. Yet today, our analysis shows that over three-quarters of industry revenue still comes from defense and government contracts.

Why does this matter? Because, logically the commercial market for satellite data should be structurally larger than the government market. Consider the potential buyers: thousands of utilities managing grids and pipelines, hundreds of insurance companies pricing risk, agricultural firms optimizing millions of hectares, logistics companies routing global supply chains, energy operators monitoring assets across continents, financial institutions tracking economic activity. Each of these industries makes millions of operational decisions annually that satellite data could improve.

Compare this to the government market. According to our data, at least 94 countries have announced or are actively pursuing sovereign EO programs. Even if every country with a space program becomes a buyer (of satellites or data or services), that is still a finite set of defense agencies, intelligence services, and civilian government programs. The commercial addressable market - spanning dozens of industries with tens of thousands of major enterprises - should dwarf what governments can collectively spend. In every mature tech market - cloud, software, telecom - commercial spending far exceeds government. Why should EO be different?

Yet the data tells a different story: our analysis shows that about 80% of EO revenues today comes from defense and government segments, while the commercial market verticals accounts for just 20%.

This isn't because commercial applications don't work. Planet provides agricultural intelligence to Bayer and Syngenta. Iceye and Floodbase deliver flood risk assessment to major insurers. LiveEO, Overstory and AiDash monitor utility infrastructure. CropX and CropIn help growers optimize irrigation and crop inputs. So, commercial adoption exists - it is just stuck at a quarter of total revenue when the addressable market logic suggests it should be the majority. Why?

I think the answer is a paradox: defense didn't just fund the sector when commercial was not ready - it also shaped how EO companies operate. And those choices now prevent the commercial scale that billions in investment were premised on.

This essay unpacks that paradox and explores what unlocks the transition, when it happens, and which companies position to capture it.

A note: This essay focuses on the defense-commercial dynamic, particularly how defense requirements have shaped commercial EO. The role of open data programs (Landsat, Sentinel) in enabling or impeding commercial markets deserves separate treatment i.e., another essay.

Quick Definitions

Part 1: Why Defense Wins Today

To understand where the sector is headed, we first need to understand why defense dominates today and why that dominance is both rational and self-reinforcing.

The Nature of Defense Demand

Defense demand for EO is fundamentally different from commercial in ways that create sustainable business models.

Dual-Use Nature

Government EO customers span defense, intelligence, and civilian agencies - disaster response, environmental monitoring, climate science, agricultural policy, infrastructure planning. While their missions differ, they share procurement patterns: multi-year contracts, mission-critical reliability requirements, proven technology standards, and often security or sovereignty considerations. These shared characteristics - whether military reconnaissance or civilian disaster management - create similar business models and architectural requirements that differ fundamentally from commercial markets.

Pays for Capability, Not Just Consumption

First, defense pays for capability and readiness, not just data consumed. A surveillance satellite that sits idle most days but can be tasked during a crisis has strategic value. Defense budgets fund this optionality - the ability to collect anywhere, anytime - even when not actively used. Commercial customers, by contrast, won't pay for idle capacity. They pay per use or per insight delivered.

Global Access Including Denied Areas

Second, defense operates globally including denied areas where alternatives don't exist. You can't fly drones over adversary territory. You can't send inspectors to verify compliance in hostile regions. Satellites provide access where nothing else can. Commercial markets mostly operate in permissive environments - monitoring Texas power lines, insuring Florida properties, tracking Iowa corn fields - where aerial imagery and drones often compete on cost and resolution.

"Defense pays for capability and readiness, not just data consumed. Commercial won't pay for idle capacity."

Strategic Value Without ROI Requirements

Third, defense doesn't require dollar-return ROI. Strategic awareness has value even without measurable financial returns. Knowing what's happening in denied regions, detecting military buildups, verifying arms control - these justify spending without profit calculations. Commercial buyers, however, always need clear financial returns: does this reduce insurance losses? Does it increase crop yields? Does it prevent outages? Without proven ROI, commercial won't scale.

Automated Decision Pipelines

Fourth, defense has invested heavily in building automated decision pipelines. Task satellite, ingest imagery, run change detection or object recognition, assess results, deliver to decision-makers. While not fully autonomous, these workflows are more institutionalized and repeatable than most commercial applications. This repeatability justifies infrastructure investment and creates procurement predictability.

Multi-Year Procurement Cycles

Finally, defense procurement operates on multi-year cycles with sustained budgets. Agencies can commit to long-term contracts that fund constellation development and operations. This predictable revenue enables capital deployment. Commercial markets require proving value incrementally, customer by customer, use case by use case.

The Commercial Reality Today

Commercial EO adoption is shallow but potentially vast. Revenue fragments across dozens of industries - agriculture, insurance, energy, logistics, finance, construction, environmental monitoring - each with different requirements, different procurement cycles, different definitions of value. Most commercial value chains still end in PDFs or PowerPoint decks. An analyst interprets imagery, writes a report, sends recommendations. Usage is episodic, not continuous. Companies struggle to prove ROI for many use cases.

The status quo is about high-touch, low-volume delivery – custom analytics per customer, bespoke integrations and consultative sales cycles. Defense contracts worth tens of millions justify this approach, but it can't scale to thousands of commercial users.

"Logic says commercial should be bigger. But logic hasn't translated to reality yet, and there are specific reasons why."

So defense dominates today for clear reasons: aligned requirements, proven business models, automated workflows. But this dominance creates a paradox. The very factors that made defense the sustainable customer also prevent the architectural shifts commercial scale requires.

Part 2: Why Defense Dominance Prevents Commercial Scale

Defense dominance prevents commercial scale through three interconnected mechanisms: architectural mismatch, path dependency, and strategic misalignment. Understanding these dynamics explains why "we will target commercial markets later" rarely works and why the transition requires fundamentally different choices from day one.

1. The Architecture Mismatch

Defense requirements create capabilities that don't translate to commercial scale. Tasking-based architectures optimized for on-demand collection don't serve markets that need standing coverage. Security classifications and air-gapped delivery don't work for APIs and cloud platforms. High-touch bespoke analytics don't scale to thousands of users. Real-time delivery requirements create cost structures incompatible with commercial price points.

Data licensing models also diverge fundamentally. Defense licenses data to specific government entities with controlled usage rights and security restrictions. Commercial platforms, however, need redistribution rights to embed EO data in applications serving millions of end users. A utility management system can't use satellite imagery if the license prohibits redistribution. An insurance pricing model can't incorporate EO if end-user agreements require individual licensing. Commercial scale requires "license once, embed everywhere" - not "license per user" - which conflicts with defense procurement structures built around controlled access.

These are not bugs - they are features that defense customers require and pay premium prices for. Defense optimizes for denied areas and non-permissive environments where satellites have clear advantage. But many commercial use cases operate in permissive environments where aerial imagery and drones often win on resolution, cost per square kilometer, and integration flexibility.

2. The Path Dependency Trap

Companies chase defense contracts because they need to survive and also because it is where the big bucks are. Rational, yes, but also comes with a condition – each defense win reinforces defense-oriented capabilities. Product roadmaps get shaped by government requirements. Teams get built around security clearances and classified delivery. Sales cycles adapt to procurement timelines measured in years. Pricing models assume low-volume, high-margin contracts.

In other words, path dependency sets in. Defense contracts also absorb new capacity as it comes online - locking satellites into high-margin government work rather than making them available for commercial scale. The "billion dollar purchase orders" that keep companies alive also trap them in serving hundreds of defense customers rather than building for tens of thousands of commercial users.

"We will commercialize later" becomes structurally impossible. You can't take capabilities built for defense and expect them to scale commercially. The underlying assumptions, workflows, and economics are fundamentally different.

3. The Strategic Choice: What Defense Revenue Enables vs. What Commercial Scale Requires

Defense and intelligence work is crucial - and arguably more important now than ever given rising geopolitical tensions, great power competition, and the need for strategic awareness in an increasingly contested world. This work will remain essential regardless of commercial market growth.

But here is the business model reality for EO companies: for venture-backed constellations sized for commercial TAM projections, defense revenue alone typically cannot justify the satellite infrastructure investment deployed. Hundreds of satellites, global ground stations, processing pipelines, operations teams - this space infrastructure was funded on the assumption that commercial markets would eventually scale. The venture capital, the SPACs, the billion-dollar valuations - all premised on commercial TAM, not defense budgets.

Companies chase defense contracts for good reasons. Defense customers pay premium prices, provide multi-year stability, and enable capabilities with strategic value. This revenue is necessary and the work matters. But defense also shapes everything: architectures get optimized for tasking and classification, teams get built around cleared personnel and bespoke delivery, pricing models assume low-volume high-margin contracts.

The strategic tension isn't "defense versus commercial" in importance. It is that the capabilities defense requires don't translate to commercial scale. It is extremely hard to optimize for both defense customers well AND commercial customers well with the same architecture. Tasking-based systems don't provide standing coverage. Classified delivery pipelines don't become open APIs. High-touch bespoke analytics don't automate to serve thousands.

So companies face a choice: architect primarily for defense customers who provide revenue today, or architect for commercial scale that justifies the infrastructure investment long-term. Both markets matter. Both are legitimate strategies. But the capabilities required are fundamentally different, and path dependency is real.

My honest opinion? For venture-backed companies that deployed large constellations sized for commercial markets, defense revenue alone typically cannot justify the infrastructure investment. The path to justifying that investment requires commercial eventually becoming the larger market, and that requires different architecture from day one.

My thesis is that the transition to commercial dominance in the coming decades is inevitable given the structural size of commercial markets, but it will be possible ONLY if companies break free from defense-shaped path dependency through fundamentally different architectural choices made today.

Part 3: The Four Mechanisms of Commercial Crossover

Defense orientation explains why commercial hasn't scaled yet. But something is changing. The sector has been technology-driven rather than problem-driven - asking "what could satellite imagery do?" rather than "what could satellite imagery do better than alternatives?" EO providers built standalone solutions showcasing satellite capability rather than integrated solutions where satellites provide unique, irreplaceable value.

More satellites than ever. More data than ever. But commercial adoption isn't accelerating proportionally. Why? Because data availability doesn't equal decision utility.

"Data availability doesn't equal decision utility. The constraint isn't supply - it's the translation layer from pixels to business outcomes."

The constraint isn't supply - it is the translation layer from pixels to business outcomes. This gap is what the integration of artificial intelligence is perhaps best positioned to close as the technology matures.

The pattern of how technologies cross from defense to commercial dominance offers a roadmap. GPS didn't surpass defense usage through better GPS receivers - it happened when GPS became invisible in every phone, car, and logistics system. It took roughly 20-25 years from when Reagan opened GPS to civilian use in 1983 to when it became ubiquitous infrastructure in smartphones and navigation systems. Today, nobody thinks about "using GPS"- they just use services that happen to rely on it. EO follows the same pattern. Value explodes when it disappears into infrastructure (for more, see my Invisibility Curve essay).

So how specifically does the transition happen? What are the concrete mechanisms that move EO from defense-dominated to commercially scaled? Four distinct but interconnected shifts make it possible.

The Four Mechanisms That Enable Commercial Crossover

1. Operating Input → Continuous usage replaces episodic analysis
2. Automation & AI → Removes human bottlenecks, enables scale
3. Line-of-Business Value → Financial returns justify adoption
4. Bundled & Invisible → Embedding in platforms reaches millions of users

Each mechanism builds on the previous. All four must work together for commercial scale to surpass defense dominance. Continuous usage without automation hits human bottlenecks. Automation without economic value fails ROI tests. Economic value without bundling limits distribution to direct sales. Each enables the next.

Mechanism 1: EO Becomes an Operating Input for Commercial Use Cases, Not a Specialist Tool

The point: Usage shifts from episodic to continuous when EO powers operational decisions, not just reports.

Commercial scale happens when industries adopt EO as background infrastructure - powering pricing models, asset scheduling, logistics planning, vegetation management, supply chain quality assurance, and risk scoring. Usage becomes continuous, not episodic.

The shift happens when EO moves from answering questions to being a required input for ongoing operations. Episodic usage asks "what changed?" - a customer requests an analysis, receives a report, makes a decision, then comes back weeks or months later for another assessment. Continuous usage means EO data feeds operational systems that run daily or hourly - a utility's maintenance system that needs current vegetation data to schedule crews, an insurer's underwriting platform that requires updated property conditions to price policies, a logistics system that monitors port activity to optimize routing. The data becomes a dependency, not a deliverable.

But continuous usage only scales if it is automated. Manual analyst interpretation creates bottlenecks that prevent volume growth.

Mechanism 2: Automation and AI - The Multiplier that the Commercial Segment Lacks

The point: Removing humans from the decision loop enables the volume that makes commercial larger than defense.

Defense has more consistently funded automation through operationalized repeatable pipelines - from satellite tasking systems to change detection algorithms to analyst support tools. Task satellite, ingest imagery, run automated processing, present results to analysts for action. Commercial, on the other hand, remains fragmented: most EO value chains still end in PDF reports or manual analyst interpretation.

Artificial intelligence closes the gap between pixels and business logic. Damaged roof detection goes from "analyst marks polygons in a report" to "insurance claim automatically triggered with confidence score". Vegetation encroachment goes from "quarterly report with recommendations" to "utility work order automatically generated with priority ranking."

AI also enables translation from what satellites see to what business systems need. The real barrier isn't image availability - it is converting "this pixel changed" into "your insurance premium should adjust" or "your delivery route should reroute" or "your credit model should update." AI makes this semantic translation economically viable at scale. It enables multimodal fusion: combining EO with weather data, IoT sensors, transactional records, and ground-based monitoring to create insights no single data source provides alone.

The moment these pipelines automate - when vegetation risk scores update continuously, when flooding probabilities refresh with new imagery, when emissions anomalies trigger alerts automatically - usage scales from hundreds of customers to tens of thousands – the inflection point.

Automation enables volume, but volume only happens if the economics work. Commercial buyers need clear financial returns.

Mechanism 3: Line-of-Business Value Outweighs National Security Value

The point: Small percentage gains across massive commercial portfolios create more economic value than defense budgets.

For insurers, utilities, energy operators, agricultural input companies, financial institutions, and logistics providers, small percentage gains across massive portfolios dwarf typical defense budgets.

If EO helps a utility reduce outage minutes by 1%, that translates to billions in avoided costs and revenue protection. In a representative scenario for a utility firm that serves over 10 million customers, preventing just one hour of outages per year through better vegetation management could save $200-300 million in avoided costs and regulatory penalties. For a typical top-tier insurer, avoiding 0.1% loss ratio deterioration through better EO-based intelligence, translates to billions in preserved underwriting profit. Similarly, reducing supply chain disruptions by 0.5% means the savings in avoided delays and alternative routing costs reach billions. And, enabling agricultural input companies to optimize recommendations by 2% means that the value in increased yields and reduced waste is enormous.

Defense rarely creates measurable ROI in dollar terms - it creates strategic advantage and operational capability. Commercial always can and must show financial return. When the math works, commercial budgets exceed defense budgets by orders of magnitude. The total addressable market isn't limited by national security requirements - it is limited only by how many economic processes can be optimized with Earth observation data.

Economic value attracts buyers, but mass adoption - millions of users instead of hundreds - requires invisible embedding in platforms they already use.

Mechanism 4: Commercial EO Becomes Bundled and Standardized

The point: When EO is invisible inside existing platforms, it reaches orders of magnitude more users than direct sales ever could.

Defense buys satellites, data, and analytics as distinct procurement items. Commercial markets will buy outcomes embedded into existing workflows. Grid operations platforms with EO built into asset management systems. Underwriting models where satellite data becomes another risk input alongside credit scores and loss history. Maritime logistics systems where EO powers routing optimization. Carbon accounting platforms where satellite monitoring provides emissions verification. Crop forecasting models where EO informs agronomic decisions. Construction monitoring suites where imagery validates progress for financing drawdowns.

"When EO is invisible inside these systems, adoption reaches orders of magnitude beyond defense procurement."

When EO is invisible inside these systems i.e, when users never know they are "using EO," they just use better platforms that happen to consume satellite data - adoption reaches orders of magnitude. This embedded approach is the most viable path to millions of end users versus hundreds of defense customers. (For a deeper exploration of this adoption pattern, see The Earth Observation Adoption Curve: From Hype to Invisibility.)

Standardization enables this bundling. Defense can specify exact requirements per mission and pay for bespoke delivery. Commercial markets need "good enough" thresholds that make providers interchangeable. Insurance needs defined accuracy for flood risk mapping. Agriculture needs specified refresh rates for yield forecasting. Utilities need detection confidence thresholds for vegetation management.

Without application-specific performance standards and neutral validation frameworks, every EO integration remains a one-off experiment rather than repeatable procurement. This standardization will likely emerge from large platform buyers - utilities, insurers, logistics operators - demanding repeatable performance thresholds, potentially formalized through industry consortia or regulatory procurement specifications. Standardization allows both commoditization of data sources and industrialization of processing - turning EO from specialist service into infrastructure.

These four mechanisms - continuous usage, automation, economic value, and invisible embedding - create the path from defense to commercial gravity. The transition unfolds over decades and requires specific architectural choices that companies must make today.

Part 4: Timing and Strategy

The sector has predicted commercial growth for two decades. What's different now is the mechanism: we are finally moving from supply-side promises to gradual demand-side necessity.

For years, EO companies talked up commercial adoption while building architectures optimized for defense. Now, in a few sectors, legacy methods are no longer sufficient and workflows are being redesigned around continuous monitoring and automation. For instance, insurance can't price climate risk using historical baselines alone. Utilities face escalating wildfire exposure and regulatory pressure that is driving large-scale vegetation management. At the same time, cheaper compute and more capable AI models make automation economically viable, and cloud-native EO tech stacks are making processing far less painful.

The shift is that buyers aren't asking whether EO might help. They are rebuilding parts of their operational infrastructure and for spatially distributed assets at scale, satellite data plus automation is increasingly the most practical path. That is demand-pull, not supply-push.

When, Not If

What I am talking about here is a 10-15 year arc from today's defense dominance to commercial gravity. The timeline reflects infrastructure transitions - faster than GPS's 20-25 years as technology adoption accelerates, but slower than cloud's 8-12 years because EO requires deeper enterprise integration and workflow redesign.

While proving ROI across industries takes time, I firmly believe that the architectural decisions that enable this transition MUST happen in the next 5 years. Workflows need to be redesigned. Enterprise systems need to be upgraded. Procurement processes need to change. ROI needs to be proven repeatedly across industries.

"The decisions being made today - in product roadmaps, architecture design, go-to-market strategy - determine which companies inherit the commercial market in 2035-2045."

But the strategic decisions that enable this transition must happen in the next 5 years. By then, the platforms that become operational infrastructure will have been chosen. The companies that position for commercial scale today will inherit the market. The companies that optimize exclusively for defense will get left behind or acquired for their technology by larger defense primes.

The Crossover Threshold

The crossover happens when EO evolves from "a situational awareness tool" to "a system that continuously optimizes economic activity." Not through regulation, compliance mandates, or sustainability requirements - though those help create initial adoption. The transition happens through economics and operations. Does it make money reliably? Does it save costs predictably? Does it reduce risk measurably?

Markets driven by productivity, efficiency, throughput, risk reduction, and cost optimization are structurally larger than markets driven by national security. The question isn't whether commercial surpasses defense. It is when the economic value proposition becomes compelling enough to overcome switching costs and integration friction.

What This Means for Strategy Today

The architectural fork in the road is now. Defense contracts provide essential revenue and enable important capabilities - government customers offer pricing stability and fund technology development that benefits the entire sector. But companies must also architect for commercial scale from day one. Standing coverage models, not purely tasking-based systems. Automated pipelines that can serve thousands, not high-touch delivery optimized for dozens. Open APIs designed for cloud platforms, not only classified endpoints. Embedded outcomes sold through partners, not only direct sales of data products. "Good enough" reliability at massive scale, not only perfect bespoke delivery for premium prices.

The tension is real. Defense customers want control, customization, and exclusivity. Commercial markets want simplicity, standardization, and wide availability. Serving both requires intentional architecture decisions that don't optimize exclusively for either.

Addressing the Counterarguments

"Defense Will Always Pay More"

Defense will always pay premium prices for priority access, highest resolution, lowest latency, and operational control. This is true. But volume × repeatability × automation beats unit price × limited customers.

"Defense will remain important - likely 30-40% of a much larger EO-enabled economy in the future, but it won't be the center of gravity."

It is the enterprise software pattern. Defense is the whale customer i.e., large contracts, deep integration, strategic importance. Commercial is the long tail that becomes the ocean - millions of smaller transactions that aggregate to something far larger. AWS doesn't make most revenue from defense contracts. It makes it from millions of commercial workloads at lower unit economics but vastly higher volume. Stripe doesn't optimize for its largest enterprise customers. It optimizes for transaction volume across millions of businesses.

The same pattern applies to EO once automation and embedding unlock commercial scale. Defense will remain important - likely 30-40% of a much larger EO-enabled market in the future, but it won't be the center of gravity. That shifts to the industries where EO becomes invisible infrastructure enabling continuous economic optimization.

"Maybe Commercial EO Just Doesn't Work"

The alternative explanation deserves consideration: maybe commercial EO just isn't valuable enough at scale. Maybe the TAM logic is wrong and most enterprises genuinely can't justify paying for satellite data when free alternatives like Landsat and Sentinel exist and ROI of using commercial EO remains unproven. If that is true, defense dominance isn't a paradox - it is the market working correctly, and the sector should optimize for what customers will actually pay for.

But, I think the evidence suggests otherwise. The growing vegetation management market, the emergence of operational parametric insurance products, and financial terminals integrating satellite data for commodity price discovery, point to demand-side forcing functions that didn't exist before. Commercial offerings differentiate through higher resolution, higher revisit rates, automated analytics, and integration into operational workflows - capabilities open data programs don't provide.

The question isn't settled - commercial may yet fail to scale - but the forcing functions are new enough to test the hypothesis again this decade. My 10-15 year timeline accounts for this uncertainty: proving ROI across industries, establishing who actually pays (individual enterprises vs. industry consortia), and determining whether revenue flows to EO companies or platform integrators.

Closing: The Direction of Travel

Defense will always be a significant buyer. Persistent demand for unique capabilities - denied area access, strategic monitoring, tactical intelligence - ensures government customers will remain important, at least in the short-term. But history has shown that markets driven by profit optimization, operational efficiency, and risk management are structurally larger than markets driven by national security. The gravitational center shifts not through regulation or policy but through economics.

When EO becomes infrastructure that continuously improves business outcomes - when it is embedded invisibly in the platforms that run utilities, price insurance, route supply chains, and optimize agriculture - commercial demand outgrows defense demand. Not because defense shrinks, but because commercial expands into hundreds of industries, thousands of companies, millions of decisions.

What This Means for Companies

Personally, I think it would be a missed opportunity if EO's future is primarily as a defense contractor, with commercial markets as an afterthought. Serving defense and intelligence is crucial work - perhaps more important now than ever given rising geopolitical tensions and the need for strategic awareness. But EO's potential extends far beyond national security. The real promise is economic and societal: optimizing how we manage infrastructure, responding to climate change, ensuring food security, reducing disaster impacts, making better decisions about our shared planet. Defense provides essential revenue and enables critical capabilities. Commercial adoption growth should define the sector's ultimate purpose and justify its infrastructure investment.

Again, the question isn't whether commercial surpasses defense. It is which companies position to capture that inevitable gravity shift and whether they make the right architectural choices while they still can.

The decisions being made today - in product roadmaps, architecture design, go-to-market strategy, team composition, capital allocation - determine which companies inherit the commercial market in 2035-2045. Those who serve defense customers well while also building architecture for commercial scale will lead. Those who optimize exclusively for defense will remain important but niche players, or get acquired for their technology by companies that understood the transition to larger commercial markets.

The Path Forward

The sector needs honesty. Defense has been both enabler and hindrance. It kept EO companies alive when commercial markets weren't ready. It funded constellation development and proved technical capabilities. But it also created path dependencies that now prevent commercial scale - architectures optimized for the wrong use cases, business models that can't serve volume markets, and teams built for classified delivery rather than API platforms.

Breaking free requires admitting the current approach won't get us to commercial scale. It requires making fundamentally different choices before the window closes. Because the companies that figure this out in the next 5 years inherit the next 10-15.

Until next time,

Aravind