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AI NewsMisleading CVs are Forcing AI Hiring to Rethink How Talent is Identified & Evaluated
10:26 AM IST · May 5, 2026
Because historical hiring was itself biased—favouring certain schools, companies, and demographics—the AI reproduces those biases at scale.
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The U.K. has just imposed legal guardrails on Google’s AI search onslaught. On Wednesday, Googleannouncedcompliance with the U.K.’s regulatory requirements, which state that the tech giant must offer publishers a way to opt out of being aggregated into AI search. To opt out, publishers will be able to use a new toggle in Google’sSearch Console, a free service that allows website owners to manage their web presence in Google’s search results. Once opted out, the publisher’s site will not be shown in Google’s generative AI Search features, like AI Overviews, AI Mode, or AI Overviews in Discover. (Google, of course, makes a point to note in the same announcement that its AI Overviews now have over 2.5 billion monthly active users, and its AI Mode has surpassed one billion monthly users.) The tech giant says it will initially test the opt-out option with a subset of U.K. publishers before rolling it out globally. The U.K.’s Competition and Markets Authority (CMA)calls the moveto put publishers back in control of how their content is used a “world first,” and points out that it will put publishers, including news organizations, into a stronger position to negotiate content deals with Google for use of their content in AI features. The CMA had first designated Google as having “strategic market status” last October, laying the groundwork for future regulations. InJanuary, it pushed Google to give website publishers a choice as to whether their content is aggregated into AI search features or used to train stand-alone AI models. Alongside the opt-out toggle, Google will also now be required to make sure publisher content in AI features is properly attributed, using clear links. Google suggested that it’s complying with this as well, noting that it had recentlyincreasedthe number of inline links directly within its AI responses, and added website previews to encourage users to click through. Google notes that a website’s decision to opt out of generative AI search features will not be used as a ranking signal for traditional Google search. The company, however, will present new metrics in its Search Console to hopefully sway publishers who could be considering opting out, including impression metrics and other information about which of their pages appear in AI responses, and in which countries. More metrics will be added over time, Google said.
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Customer support and service are among the hottest sectors in voice AI right now. But building a product that sounds human and responds without noticeable delay turns out to be much harder in some markets than others — and most of the major players weren’t built with Africa and the Middle East in mind. AethexAI, a startup founded last year to close that gap, has raised $3 million in pre-seed funding led by 4DX Ventures, with participation from Enza Capital, Dorm Room Fund, Mojo Ventures, and Stanford GSB 26 Fund. Individual investors include Stanford faculty, telecom executives, and AI researchers from Anthropic. Rather than using existing orchestration tools likeVapiandLiveKit, the company built its own small model and orchestration layer from scratch to handle the localized dialects of English, French, and Arabic spoken across its target markets — a decision driven, as we’ll get to, by the particular demands of operating in the region. The company is also launching its platform for enterprises to try out its tech and sign up for its services, along with APIs and SDKs for developers to experiment with its models. The startup was founded by Mariama Diallo and Ayooluwa Odemuyiwa. CEO Diallo worked at Goldman Sachs and later joined YC-backed ModelML as a product and growth hire. CTO Odemuyiwa graduated from Caltech, worked at Meta, and enrolled at Stanford Business School before co-founding the company. The pair wanted to build something for emerging markets and started looking for opportunities. Businesses around the world are racing to adopt AI tools to automate parts of their operations. But that doesn’t always work out. In Egypt, a call center automated a significant share of its calls, but rolled the system back because of poor results, the founders found. Several support centers in Africa told them that finding and hiring engineers to automate calls at the right cost was a persistent headache. “The latency and jitter that we saw on automated calls in this region were outrageous. If we had become orchestrators, we might have had to use large models that were hosted outside the region, resulting in higher latency. We realized that in order for this to work, we have to use very small models and cut latency at every step,” Odemuyiwa told TechCrunch about the decision to build the company’s own models and orchestration layer. AI labs that deploy their latest models usually spend millions training them and acquiring data. AethexAI found a solution for both. Rather than chasing the largest possible models, it decided that small models are enough to tackle the latency problem while maintaining accuracy and developed its own Kora series, with parameters ranging from 300 million to 1.7 billion. That’s a fraction of the size of the LLMs, which is precisely the point. To train these models, the startup used anonymized recordings from a call center partner. It also shipped hard drives to radio stations across Africa to collect more audio data. To keep costs down, it built a contributor network of university students to annotate data and pronounce local names. As a result, the startup says, it’s now handling more than 17,000 calls per day. On the business side, the company is taking care to walk clients who are new to voice AI through the process, offering onsite demos and workshops to help them identify the best use cases for automation. “We always tell customers that we cannot be everything for everybody right now. We’re small. When we start talking to a company, we ask them to pick one use case that is the most important to them to start [with],” Diallo said. The startup is open to working across all industries, but at the moment, a big part of its use cases involves calls for debt collection, customer activation, or KYC — Know Your Customer verification, the standard identity-checking process used by banks and telecoms. The company is hiring forward-deployed engineers on a contract basis to serve local markets and building channel partnerships with telecoms providers to handle telephony for voice AI calls. Plug-and-play solutions, it says, simply won’t work here. Walter Baddoo, co-founder and managing partner of 4DX Ventures, argues that the Africa and Middle East market is fundamentally different from the markets most voice AI companies were built to serve. “Enterprises in Africa and the Middle East process roughly three times the call volume of their Western counterparts, as voice is still the dominant channel for customer interaction,” he said. “Incumbent systems were built for Western markets characterized by high-end GPU infrastructure, standard English and European speech environments, and enterprise workflows common in the U.S. and Europe. That creates real gaps when enterprises need systems that handle dialects, code-switching, and informal speech patterns, and that work within their existing telephony infrastructure and their actual price points.” Put another way, while companies like ElevenLabs, Deepgram, Sierra, and Cognigy are expanding globally at a fast pace, the markets they were built for and the markets they are entering aren’t always the same thing. Startups like AethexAI are betting that the gaps — models specialized in local dialects, on-the-ground partnerships, infrastructure built for the region — represent a market opening that the giants have neither the incentive nor the architecture to close.
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In what may be one of the more questionable uses of AI to date, Amazon announced on Wednesday that it will display AI-generated images of products within its shopping app based on users’ search queries. That’s right — a retailer where people shop for real-world products thinks that displaying fake photos will “help” consumers better find what they’re looking for. Enough already. Here’s how Amazonsays in a blog postthat the feature will work. Customers may have something in mind but don’t know the right term to describe it in a way that returns useful results. (The examples Amazon gives are things like “cowl neck” for a style of shirts or “rattan” for furniture.) When someone enters a search query, they’ll be shown a variety of AI-generated product images below their autocomplete suggestions. (See above photo.) For instance, if you search for a blue gingham dress, you might see a few dress styles — short or long sleeves, varying lengths, and other differences — appear as visual options. The idea is that clicking one would direct you to search results that better match that style, powered by Amazon’s visual search capabilities. In reality, it’s somewhat bananas for a retailer to make up fake products as a way of guiding users to search results. For starters, it’s potentially misleading — customers who don’t read carefully may think they’re being directed to a page where they could find that exact dress, then be disappointed when it isn’t available. And there’s the fairly obvious question of why you’d make up product images when you have a website full of real photographs of real products — which is presumably what an online shopper actually wants to see. The feature follows a number of other attempts by Amazon to integrate AI into its retail site and shopping app, with mixed results. On the more useful end, Amazon alreadysummarizes customer reviewsvia AI, so you don’t have to read them all to get a sense of the key pros and cons of a product. More bizarrely, it last year rolled out ashort audio product summaryfeature in which AI experts describe a product’s highlights, podcast-style. Other recent AI features include AI-generated “shoppable collages” to direct people to curated pages devoted to a particular fashion style;Amazon Lens Live, which scans products in a camera’s view to find visual matches; the ability to add text to visual searches; and a Lock Screen visual search widget for iOS. Earlier this month, Amazon alsoreplaced its Rufus AI chatbot with Alexa for Shoppingto enable natural language shopping queries via voice and text.
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From Scout and Project Solara to new MAI models and GitHub Copilot upgrades, here are the biggest announcements from Build 2026.
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