Florian and Esther discuss the language industry news of the past few weeks, starting with senior hires in revenue and operations at DeepL and what this signals about the LTP’s next phase.

The duo then turns to new data from AI labs and hyperscalers, where Florian highlights findings from Anthropic’s research showing AI is settling into a support role rather than full automation, with usage concentrated around review and validation, and humans remaining firmly in the loop.

On the consumer side, Esther points to Microsoft Copilot data showing translation and language learning as one of the most common everyday AI use cases. Florian flags Adobe’s new “Translate this PDF” feature, where formatting was the main issue rather than translation accuracy.

The conversation then shifts to infrastructure, where Florian emphasizes how NVIDIA is positioning itself at the center of real-time multilingual voice ecosystems by open-sourcing models while driving demand for its hardware.

The duo unpacks OpenAI’s quiet launch of ChatGPT Translate. Esther notes that reactions have been mixed, with many seeing the interface as basic, while Florian stresses the strategic importance of the move. Then the two disagree on whether or not the AI’s default prompt to make the translation sound “more fluent” makes any sense.

Esther walks through recent M&A activity and funding rounds, highlighting acquisitions in Europe and the US alongside major raises by Synthesia, Deepgram, and reportedly ElevenLabs.

Florian concludes with a look at an S-1 filing by a tiny company, using it as an example of how the US capital markets accommodate everything from billion-dollar AI firms to survival-stage experiments.

JC Penet, Reader in Translation Industry Studies at Newcastle University, and Joss Moorkens, Associate Professor at DCU, join SlatorPod to talk about the new open-access book Teaching translation in the age of generative AI: New paradigm, new learning?

The duo explains how large language models (LLMs) have a different impact than earlier machine translation breakthroughs as they generate human-like text, respond to prompts, and adapt output to context. 

Public hype around LLMs has affected demand for some translators and fueled misconceptions around the value of studying translation. Although, JC and Joss stress that translation education must adapt.

JC outlines how students need to assess whether output is appropriate for purpose, audience, risk, and context. This places greater importance on skills such as selection, evaluation, and effective prompting, while still relying on core linguistic and cultural competence.

Joss adds that this shift reflects real industry practice, where different content types already receive different levels of automation and human involvement. Drawing on healthcare research, he highlights how AI can outperform traditional workflows in some contexts but fail badly in others, especially across languages with uneven data coverage.

Joss also highlights ethical blind spots that arise when performance metrics dominate decision-making. He describes a “triple bottom line” approach that weighs people, planet, and performance equally.

On fears of de-skilling, JC argues that excluding AI from classrooms poses a greater risk. Without guided engagement, students may use tools uncritically or fail to develop AI literacy altogether. Joss points to initiatives such as LT-LiDER, an Erasmus+ project designed to build AI literacy among educators.

Looking ahead, the duo contends that studying languages and translation remains valuable because it develops deep reading, critical thinking, intercultural awareness, and adaptability.

In the 2025 year-end episode of SlatorPod, hosts Florian Faes and Esther Bond reflect on a year defined by rapid AI investment, shifting policy, and structural change across the language industry.

Esther opens the year-in-review by highlighting January’s twin funding milestones in the language AI and product space. Florian follows with February, which saw hyperscalers and AI labs release data highly relevant to the way AI translation is being used.

March, April, and May saw major developments both on the regulatory side and in terms of bolt-on acquisition deals.

Past the mid-year point, OpenAI’s decision to hire a localization manager was what grabbed the industry’s collective attention. The AI lab’s decision contrasted with September’s news, which saw the closure of one of the world’s most recognized academic programs for localization.

The year closed on publicly listed LSIs releasing mixed results and major announcements in AI translation for literature and live speech translation rollouts.

The duo closes with 2026 predictions!

Josh Miller and Christopher Antunes, Co-Founders and co-CEOs of 3Play Media, join SlatorPod to talk about the company’s trajectory as a leading language solutions integrator (LSI) in multilingual video accessibility.

The duo explains how the two met at MIT, where an early challenge from OpenCourseWare revealed that captioning thousands of technical videos was financially impossible, leading to the company’s founding, where they developed proprietary tooling, leveraged AI, and incorporated expert-in-the-loop solutions.

Josh describes how their platform evolved into a dual system supporting both customers and large-scale operations. Chris notes that the LSI now serves media and entertainment, higher education, e-learning, and corporate clients.

Chris explains that three major trends — the European Accessibility Act (EAA), advancements in voice technology, and the rise of live events — drove their expansion into global localization. 

The co-CEOs detail their dubbing journey, noting rapid learning over the last 18 months and the emergence of a big mid-tier market between high-end theatrical dubbing and low-cost AI-only output.

Josh explains how the EAA is pushing companies to prepare for large columns of multilingual captioning and audio description. He notes that interpretations of the law still vary, but major media firms are already investing to avoid disruption. 

The duo shares findings from their 2025 State of ASR Report, where they found that accuracy initially improved sharply with generative models but has now plateaued.

Looking to the future, the co-CEOs are working on shifting their model to incorporate AI-generated scores and analytics, allowing customers to decide on the level of expert intervention.

Florian and Esther discuss the language industry news of the past few weeks, reflecting on SlatorCon Remote and announcing that SlatorCon London 2026 is open for registration.

The duo touch on IMDb’s decision to recognize dubbing artists as part of new professional credit categories, explaining how this expands visibility for multilingual voice talent. They then move on to Coursera’s strategy shift and outline how its new CEO is betting on AI translation and AI dubbing to revive slowing growth. 

Florian and Esther talk about Amazon’s rollout of AI-translated Kindle eBooks, and question authors' willingness to rely on automated translation despite Amazon’s promise of fast turnarounds, in as little as 72 hours.

Florian highlights research on spatial audio improving AI live speech translation, and reflects on how clearer speaker differentiation could enhance comprehension. Although he stresses ongoing challenges in live settings, like latency and overlapping speech.

In Esther’s M&A and funding corner, healthcare AI technology startup No Barrier raises USD 2.7m, Cisco acquires EZ Dubs to enhance WebEx’s real-time speech translation capabilities, and audio AI startup AudioShake raises USD 14m. 

Florian analyzes OneMeta’s financials and notes its rapid revenue growth despite significant ongoing and limited marketing presence. Esther details the landmark UK NHS framework agreement for language services, including scope and the number of awarded vendors.

Florian concludes with updates on interpreting performances at Teleperformance and AMN Healthcare, noting mixed results.

Amir Haramaty, Co-Founder and President of aiOla, joins SlatorPod to talk about how spoken, multilingual data can transform enterprise workflows and unlock real ROI.

The Co-Founder introduces himself not as a serial entrepreneur but as a serial problem solver, focused on one core challenge: most enterprise data remains uncaptured, unstructured, and unused.

Amir emphasizes that traditional speech tech fails in real-world conditions, where accents, noise, and hyper-specific jargon dominate. He illustrates how he tackles this challenge by building workflow-specific language models that extract only the data relevant to a process.

Amir says aiOla converts speech not into text but into structured, schema-ready data, allowing organizations to automate workflows, improve compliance, and identify trends long before humans can. He explains that the company focuses on narrow processes rather than general conversation, enabling precision in niche environments.

Amir shares how aiOla routinely cuts multi-hour procedures down to minutes, drives efficiency across frontline roles, and creates previously unavailable datasets that feed enterprise intelligence. He highlights ROI examples from supermarkets, airlines, manufacturing, and automotive industries.

Amir explains that after proving aiOla’s value, he realized the fastest way to scale was through firms already embedded in enterprise digital transformation. He notes that aiOla now partners with UST, Accenture, Salesforce, and Nvidia, creating a distribution engine capable of replicating wins across thousands of clients. 

He calls this channel strategy a force multiplier that shortens sales cycles and embeds aiOla inside broader modernization initiatives. Amir adds that these partners not only bring scale but also domain expertise aiOla deliberately chose not to build in-house. 

Amir outlines future priorities, including product-led growth, speech-based coding, and speech-prompted AI agents. He predicts that agentic systems will rely heavily on high-quality spoken data, making aiOla’s role even more central.

Tom Kocmi, Researcher at Cohere, and Alon Lavie, Distinguished Career Professor at Carnegie Mellon University, join Florian and Slator language AI Research Analyst, Maria Stasimioti, on SlatorPod to talk about the state-of-the-art in AI translation and what the latest WMT25 results reveal about progress and remaining challenges.

Tom outlines how the WMT conference has become a crucial annual benchmark for assessing AI translation quality and ensuring systems are tested on fresh, demanding datasets. He notes that systems now face literary text, social-media language, ASR-noisy speech transcripts, and data selected through a difficulty-sampling algorithm. He stresses that these harder inputs expose far more system weaknesses than in previous years.

He adds that human translators also struggle as they face fatigue, time pressure, and constraints such as not being allowed to post-edit. He emphasizes that human parity claims are unreliable and highlights the need for improved human evaluation design.

Alon underscores that harder test data also challenges evaluators. He explains that segment-level scoring is now more difficult, and even human evaluators miss different subsets of errors. He highlights that automated metrics built on earlier-era training data underperformed, particularly COMET, because they absorbed their own biases.

He reports that the strongest performers in the evaluation task were reasoning-capable large language models (LLMs), either lightly prompted or submitted with elaborate evaluation-specific prompting. He notes that while these LLM-as-judge setups outperformed traditional neural metrics overall, their segment-level performance varied.

Tom points out that the translation task also revealed notable progress from smaller academic models around 9B parameters, some ranking near trillion-parameter frontier models. He sees this as a sign that competitive research is still widely accessible.

The duo concludes that they must carefully choose evaluation methods, avoid assessing models with the same metric used during training, and adopt LLM-based judging for more reliable assessments.

Nikita Agarwal, Founder of Milestone Localization, joins SlatorPod to talk about her journey founding a language solutions integrator (LSI) and launching Cavya.ai, a platform designed to streamline translation project preparation.

Nikita began Milestone Localization in 2020 after discovering the language industry while working in international sales. She was drawn to the field’s global scope and low barrier to entry. She emphasizes that sales experience played a crucial role in landing early clients and understanding the value of hiring people from within the industry. 

The founder reflects on the past 16 months as a period of intense change marked by AI disruption, client pressure on pricing, and shifting expectations. She highlights how regulated sectors like life sciences have helped stabilize the company amid volatility. She details how the LSI specializes in medical device translations and regulatory submissions across Europe.

Nikita explains that her new platform, Cavya.ai, emerged from internal needs to improve project preparation. She says the tool automates glossaries, style guides, and document analysis, reducing time and boosting consistency for small and mid-sized projects.

The founder shares her observations on India’s evolving language technology landscape, noting significant progress in AI for major Indian languages. She says increased internet access and AI-driven localization are expanding education and job opportunities across the country.

Nikita concludes that she sees the future in expanding life sciences work, refining Cavya, and developing an AI-powered QA tool. She notes that some clients are showing “AI fatigue” and returning to human-led workflows.

Thordur Arnason, Global AI GTM Lead at Capgemini Invent, joins SlatorPod to talk about how the consulting giant is embracing language AI through BabelSpeak, its new real-time AI speech translation platform.

Thordur explains that the idea emerged from Capgemini’s AI Futures Lab while researching multimodal AI. Inspired by Meta’s launch of the Seamless M4T model, the team set out to tackle the hard problem of live AI speech translation.

He notes that early pilots with DNB Bank, the Norwegian Red Cross, and the Norwegian Police tested BabelSpeak in critical situations — from refugee banking access to emergency communication.

Thordur highlights Capgemini’s partnerships with Nvidia and Telenor, saying Nvidia provides the AI hardware and models, while Telenor’s sovereign AI infrastructure ensures security, GDPR compliance, and data sovereignty.

He emphasizes that BabelSpeak’s reliability comes not just from AI models but from engineering precision, reducing latency from three seconds to under 300 milliseconds.

Thordur discusses Capgemini’s exploration of agentic AI, where autonomous systems perceive, reason, and act independently. He describes how the company built an “Agentic Workbench” to help non-technical users experiment with AI agents safely and sees BabelSpeak as a potential tool within larger agentic systems.

He concludes that Capgemini is expanding BabelSpeak into a broader suite of language tools, combining secure AI infrastructure with advanced multilingual communication for enterprise and government clients.

Florian and Esther discuss the language industry news of the week, with congratulations to Villam Language Services on its sale to InAnyLanguage. Slator served as joint exclusive advisor with Maveria Advisory, representing Villam throughout the end-to-end M&A process.

The duo turns to Perplexity’s Localization Manager job posting, which they found almost identical to OpenAI’s earlier post, down to matching structure, order, and phrasing. They question whether copying such a specific ad shows a lack of seriousness or simply reflects practicality and efficiency.

Esther and Florian talk about RWS's new leadership hires: Stephen Lamb as Chief Financial Officer and Michael Wayne as Head of Media and Entertainment. Esther outlines how the appointments strengthen RWS’s investment strategy in media localization, dubbing, and content adaptation.

Esther next mentions that Visual Data has named Maz Al-Jumaili as SVP of Worldwide Localization, to lead subtitling and dubbing operations and strengthen client partnerships.

The duo wrap up with the UK government’s bizarre energy-efficiency study, claiming AI translation is a thousand times greener than human translation. They review the flawed logic, where the report assigns human translators the entire office energy costs while excluding AI infrastructure.