Built for One Era, Tested by Another: Armenian Tech and AI

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Charles Darwin developed the theory of evolution by studying living systems under pressure. Species survive when their traits fit the environment around them. When the environment changes, the same traits that once helped them can lose their advantage.

Technology companies live in ecosystems of their own. Their traits are the promises they make to the market. Every company begins by telling users that something can become faster, cheaper, easier, more accessible, or more profitable than it was before. When that promise holds, customers believe in it, the market rewards it, and the business grows around it. When the environment shifts, that same promise becomes a trait under pressure. What once helped the company grow has to prove it still fits.

By the end of 2022, as the technology industry entered what we now describe as the AI era, many of those promises were tested. In some markets, AI made existing products easier to replace by offering users faster or cheaper alternatives. In others, it changed what customers considered good enough, especially as speed, automation and first drafts became part of the expectation. For some companies, however, AI opened new space around what they had already built, creating product, market, or business model opportunities that had not existed in the same way before.

The global examples are easy to name because they quickly became part of the public AI narrative. Less visible is the way in which companies in smaller technology ecosystems responded when the same shift reached their users, competitors and investors. Armenian-founded tech companies were part of that test as well, especially because many were already building for international markets.

That is why their stories are worth paying attention to. Wirestock, Renderforest and CodeSignal show how the same phenomenon, the rise of AI, can affect companies in very different ways. The pressure did not arrive through the same door, and the responses could not follow one formula. Each company had to understand its environment, rely on its strengths, and decide which part of its original promise needed to change.

Wirestock: When Creative Work Found a New Market

Recent news of Wirestock’s $23 million Series A funding round makes its case especially relevant. Led by Nava Ventures, with participation from SBVP, Formula VC, I2BF Global Ventures and others, the investment marked a new stage for a company that began with a much simpler promise: helping creators earn from their work more easily.

Wirestock began in 2019 with a familiar creator-economy problem. Photographers, videographers, illustrators and digital artists could produce commercially valuable work, but selling it online meant spending hours on the least creative part of the process. Creators had to upload files to multiple platforms, write titles and descriptions, add keywords, meet technical requirements, and manage several accounts. Wirestock’s early promise was to reduce that friction: creators could upload once, while the platform helped prepare, tag and distribute the content to marketplaces such as Shutterstock, Adobe, Pond5, Alamy, Freepik and others.

The idea was simple: let creators focus on making art, and leave the repetitive business layer to a platform built for it.

That promise had already found demand before AI became central to Wirestock’s public story. By 2021, the company had reportedly reached 5 million uploaded files and more than $1 million in creator sales. Those numbers suggest Wirestock had already identified a real pain point in the stock-content economy: creators wanted broader distribution without repeating the same administrative work across multiple marketplaces.

Then the creative-content market changed. Images, videos, design files, gaming assets, and 3D materials could still be licensed as finished products, but the AI era created a new demand. AI labs needed large volumes of human-made, usable, structured creative data to train, test and improve models.

Wirestock was quick to read where the market was heading. In 2023, the company moved from stock-content distribution to supplying multimodal datasets to AI labs. In a TechCrunch interview, co-founder and CEO Mikayel Khachatryan said artists could opt out of the data supply business. He also said early deals began with existing library content, then expanded into custom requests for new content and data, creating additional opportunities for creators.

The Series A funding shows where the company wants to take this shift. Wirestock says the investment will support two priorities: expanding its creator platform with new tools, project formats, and earning pathways; and scaling its data creation capabilities to produce more complex custom datasets and new creative formats. Put simply, Wirestock is building both sides of the new marketplace: more ways for creators to produce and earn, and more capacity to deliver the data AI companies are seeking.

The scale already looks very different from the company’s early stock-content years. Wirestock now works with more than 700,000 creators, hosts over 50 million assets, and has licensed more than 10 million assets for AI. TechCrunch reported that the company has reached around $40 million in annual run-rate revenue and has paid $15 million to contributors.

Wirestock’s adaptation is essentially a business model expansion. Its original promise was creator monetization, but AI reshaped where that monetization could happen. The company’s existing strengths: creator access, content flow, tagging, licensing and marketplace experience, have become valuable in this new commercial context.

The strength of this strategy is also where its future questions lie. Demand for licensed, human-made data is rising, especially as AI companies face legal and quality concerns around unclear datasets. Wirestock has found a timely opening in that market. Its longer-term position will depend on whether AI labs continue to need this kind of creative data at scale, and whether creators continue to see the exchange as clear, fair and worth joining.

Renderforest: When “Easy to Create” Had to Become Faster

Renderforest’s case differs from Wirestock’s because the company didn’t need to find a new market for its assets. The challenge was closer to the user experience: a platform that had already made creative production easier had to adapt to a world where “easy” was no longer enough on its own.

Founded in Yerevan in 2013, Renderforest began with online video creation and later grew into a broader branding platform. Its company story says the idea came from a simple observation: creating visual content was limited and expensive for people without editing skills. The early promise was to make quality branding accessible to freelancers, startups and small businesses that didn’t have the time, budget or production resources to create professional materials from scratch.

That promise was powerful because it solved a practical problem. A user could choose a template, add text, upload a logo, select music, and produce something polished without hiring a studio or learning complex editing software. Renderforest made professional-looking video and branding production manageable for non-specialists.

The model showed clear traction before the current AI wave. By 2019, Renderforest’s own timeline listed 10 million users and 20 million created projects, suggesting global demand for simplified creative production. The platform kept expanding around the same promise, adding tools for logos, websites, mockups, graphic design and mobile creation.

Then AI changed the rhythm of creative work. Users began seeing videos, images, and designs generated from short prompts. Templates still mattered for people who wanted control over structure, style and branding. But the fastest starting point was no longer always choosing a template; increasingly, it was describing an idea and receiving something to edit.

Renderforest’s response was to add generation at the start of the creative workflow it already served. Its text-to-video editor, released in June 2024, lets users move from text to visuals, videos, and animations in a few clicks. Its current AI Video Generator continues that direction by helping users turn text, images, or scripts into a draft video that can be edited, branded and exported.

The formula here is product augmentation. Renderforest kept its original promise of making creative production accessible, then added AI to reduce the work needed before users see a first result. The challenge now is differentiation. When general AI models can already generate visual content, Renderforest has to give users a reason to choose a dedicated creative platform. That reason will likely depend on what happens after generation: how easily users can edit the result, keep it on brand, adapt it to different formats, control the final look, and turn a draft into something ready to publish. Whether Renderforest can turn this into a convincing advantage will become clearer over time. 

CodeSignal: Measuring Skills in the AI Era

The final case raises the question of how people adapt in the AI era: how skills are measured, how they are developed, and whether AI has changed either. CodeSignal began in hiring, where the central problem was knowing what a candidate could actually do. The company started as CodeFights, a platform where developers could practice and compete through coding challenges, then rebranded as CodeSignal in 2018 with a clearer focus on technical assessment for job candidates. Its early promise was to make skills more visible by giving employers structured coding tests and skills data, rather than relying too heavily on resumes, university names, previous employers, or job titles.

That promise found a place in a competitive market. CodeSignal entered a space where companies such as HackerRank and Codility were already trying to make technical assessments faster, more scalable and consistent. Still, the Armenian-founded company established its position, raising $25 million in Series B funding in 2020 and $50 million in Series C funding in 2021 for its technical hiring and assessment platform.

Then AI changed the work environment those assessments were designed for. Developers, marketers and analysts increasingly use AI to write, debug, draft, review, test, and refine their work. In that context, skill is harder to separate from the tools supporting it. If AI is already part of the job, the ability to use it well also becomes part of what needs to be tested.

CodeSignal’s first response was to add AI to the hiring workflow it already knew. In 2025, the company launched AI-assisted coding assessments and interviews, bringing its AI assistant Cosmo into the Hire Suite so candidates could complete coding challenges with AI support. It also introduced an AI Interviewer that conducts structured interviews and gives hiring teams reports, transcripts, and skill evaluations to review. This is the augmentation side of the strategy: CodeSignal updated its assessment product for a workplace where AI assistance is becoming part of how people actually work.

The second response was to move into AI-powered learning. In 2024, CodeSignal launched CodeSignal Learn, built around Cosmo as an AI guide that can support practice, give feedback, and help learners work through technical concepts more interactively. In 2025, the company expanded this direction with Cosmo as a mobile learning app, offering more than 300 short courses across areas such as AI, coding, marketing, finance, leadership and communication. This move takes CodeSignal beyond evaluation and into the stage where skills are built, practiced and updated.

The formula here combines AI augmentation with product expansion. CodeSignal added AI to the assessment business it already had, while using Cosmo to enter learning as a new product line. The opportunity is clear: a company that once helped employers judge ability now wants to play a role earlier, where that ability is formed. The risk is whether AI-guided practice can be trusted as more than a convenient learning experience, and whether CodeSignal can make learning and assessment reinforce each other rather than remain separate products under the same brand.

Adaptation Without a Final Form

The AI era has made one thing clear: adaptation does not look the same from one company to another. A strategy that works for one business may not for another, because the pressure comes through different products, users, markets and expectations. What matters is not the appearance of adaptation, but whether a company understands how change has reached its own promise.

For Armenian-founded companies building beyond the local market, this question carries particular weight. Their products are measured against global competitors, global tools and global habits. The environment around them can shift quickly, even when the company has not changed its original mission. In that kind of market, survival depends less on finding one perfect formula and more on responding without losing the strength that made the company relevant in the first place.

This is where Darwin’s idea returns. Adaptation is not movement in every direction. It is the search for fit. In technology, that fit is constantly renegotiated between what a company has built, what users now expect, and what the market has begun to reward.

The companies that last will likely be the ones that treat adaptation as an ongoing discipline rather than a finished transformation. They return to the promise they were built on, test it against the world around them, and decide what it must become next.

Creative Tech