Google’s Crawl Team Filed Bugs Against WordPress Plugins via @sejournal, @MattGSouthern

Google’s crawl team has been filing bugs directly against WordPress plugins that waste crawl budget at scale.

Gary Illyes, Analyst at Google, shared the details on the latest Search Off the Record podcast. His team filed an issue against WooCommerce after identifying its add-to-cart URL parameters as a top source of crawl waste. WooCommerce picked up the bug and fixed it quickly.

Not every plugin developer has been as responsive. An issue filed against a separate action-parameter plugin is still sitting unclaimed. And Google says its outreach to the developer of a commercial calendar plugin that generates infinite URL paths fell on deaf ears.

What Google Found

The details come from Google’s internal year-end crawl issue report, which Illyes reviewed during the podcast with fellow Google Search Relations team member Martin Splitt.

Action parameters accounted for roughly 25% of all crawl issues reported in 2025. Only faceted navigation ranked higher, at 50%. Together, those two categories represent about three-quarters of every crawl issue Google flagged last year.

The problem with action parameters is that each one creates what appears to be a new URL by adding text like ?add_to_cart=true. Parameters can stack, doubling or tripling the crawlable URL space on a site.

Illyes said these parameters are often injected by CMS plugins rather than built intentionally by site owners.

The WooCommerce Fix

Google’s crawl team filed a bug report against the plugin, flagging the add-to-cart parameter behavior as a source of crawl waste affecting sites at scale.

Illyes describes how they identified the issue:

“So we would try to dig into like where are these coming from and then sometimes you can identify that perhaps these action parameters are coming from WordPress plug-ins because WordPress is quite a popular CMS content management system. And then you would find that yes, these plugins are the ones that add to cart and add to wish list.”

And then what you would do if you were a Gary is to try to see if they are open source in the sense that they have a repository where you can report bugs and issues and in both of these cases the answer was yes. So we would file issues against these uh plugins.”

WooCommerce responded and shipped a fix. Illyes noted the turnaround was fast, but other plugin developers with similar issues haven’t responded. Illyes didn’t name the other plugins.

He added:

“What I really, really loved is that the good folks at Woolcommerce almost immediately picked up the issue and they solved it.”

Why This Matters

This is the same URL parameter problem Illyes warned about before and continued flagging. Google then formalized its faceted navigation guidelines into official documentation and revised its URL parameter best practices.

The data shows those warnings and documentation updates didn’t solve the problem because the same issues still dominate crawl reports.

The crawl waste is often baked into the plugin layer. That creates a real bind for websites with ecommerce plugins. Your crawl problems may not be your fault, but they’re still your responsibility to manage.

Illyes said Googlebot can’t determine whether a URL space is useful “unless it crawled a large chunk of that URL space.” By the time you notice the server strain, the damage is already happening.

Google consistently recommends robots.txt, as blocking parameter URLs proactively is more effective than waiting for symptoms.

Looking Ahead

Google filing bugs against open-source plugins could help reduce crawl waste at the source. The full podcast episode with Illyes and Splitt is available with a transcript.

Google Updates Googlebot File Size Limit Docs via @sejournal, @MattGSouthern

Google updated its Googlebot documentation to clarify information about file size limits.

The change involves moving information about default file size limits from the Googlebot page to Google’s broader crawler documentation. Google also updated the Googlebot page to be more specific about Googlebot’s own limits.

What’s New

Google’s documentation changelog describes the update as a two-part clarification.

The default file size limits that previously lived on the Googlebot page now appear in the crawler documentation. Google said the original location wasn’t the most logical place because the limits apply to all of Google’s crawlers and fetchers, not just Googlebot.

With the defaults now housed in the crawler documentation, Google updated the Googlebot page to describe Googlebot’s specific file size limits more precisely.

The crawling infrastructure docs list a 15 MB default for Google’s crawlers and fetchers, while the Googlebot page now lists 2 MB for supported file types and 64 MB for PDFs when crawling for Google Search.

The crawler overview describes a default limit across Google’s crawling infrastructure, while the Googlebot page describes Google Search–specific limits for Googlebot. Each resource referenced in the HTML, such as CSS and JavaScript, is fetched separately.

Why This Matters

This fits a pattern Google has been running since late 2025. In November, Google migrated its core crawling documentation to a standalone site, separating it from Search Central. The reasoning was that Google’s crawling infrastructure serves products beyond Search, including Shopping, News, Gemini, and AdSense.

In December, more documentation followed, including faceted navigation guidance and crawl budget optimization.

The latest update continues that reorganization. The 15 MB file size limit was first documented in 2022, when Google added it to the Googlebot help page. Mueller confirmed at the time that the limit wasn’t new. It had been in effect for years. Google was just putting it on the record.

When managing crawl budgets or troubleshooting indexing on content-heavy pages, Google’s docs now describe the limits differently depending on where you look.

The crawling infrastructure overview lists 15 MB as the default for all crawlers and fetchers. The Googlebot page lists 2 MB for HTML and supported text-based files, and 64 MB for PDFs. Google’s changelog does not explain how these figures relate to one another.

Default limits now live in the crawler overview documentation, while Googlebot-specific limits are on the Googlebot page.

Looking Ahead

Google’s documentation reorganization suggests there will likely be more updates to the crawling infrastructure site in the coming months. By separating crawler-wide defaults from product-specific documentation, Google can more easily document new crawlers and fetchers as they are introduced.

GSC Data Is 75% Incomplete via @sejournal, @Kevin_Indig

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My findings this week show Google Search Console data is about 75% incomplete, making single-source GSC decisions dangerously unreliable.

Google filters 3/4 of search impressions for “privacy,” while bot inflation and AIOs corrupt what remains. (Image Credit: Kevin Indig)

1. GSC Used To Be Ground Truth

Search Console data used to be the most accurate representation of what happens in the search results. But privacy sampling, bot-inflated impressions, and AI Overview (AIO) distortion suck the reliability out of the data.

Without understanding how your data is filtered and skewed, you risk drawing the wrong conclusions from GSC data.

SEO data has been on a long path of becoming less reliable, starting with Google killing keyword referrer to excluding critical SERP Features from performance results. But three key events over the last 12 months topped it off:

  • January 2025: Google deploys “SearchGuard,” requiring JavaScript and (sophisticated) CAPTCHA for anyone looking at search results (turns out, Google uses a lot of advanced signals to differentiate humans from scrapers).
  • March 2025: Google significantly amps up the number of AI Overviews in the SERPs. We’re seeing a significant spike in impressions and drop in clicks.
  • September 2025: Google removes num=100 parameter, which SERP scrapers use to parse the search results. The impression spike normalizes, clicks stay down.

On one hand, Google took measures to clean up GSC data. On the other hand, the data still leaves us with more open questions than answers.

2. Privacy Sampling Hides 75% Of Queries

Google filters out a significant amount of impressions (and clicks) for “privacy” reasons. One year ago, Patrick Stox analyzed a large dataset and came to the conclusion that almost 50% are filtered out.

I repeated the analysis (10 sites in B2B out of the USA) across ~4 million clicks and ~450 million impressions.

Methodology:

  • Google Search Console (GSC) provides data through two API endpoints that reveal its filtering behavior. The aggregate query (no dimensions) returns total clicks and impressions, including all data. The query-level query (with “query” dimension) returns only queries meeting Google’s privacy threshold.
  • By comparing these two numbers, you can calculate the filter rate.
  • For example, if aggregate data shows 4,205 clicks but query-level data only shows 1,937 visible clicks, Google filtered 2,268 clicks (53.94%).
  • I analyzed 10 B2B SaaS sites (~4 million clicks, ~450 million impressions), comparing 30-day, 90-day, and 12-month periods against the same analysis from 12 months prior.

My conclusion:

1. Google filters out ~75% of impressions.

Image Credit: Kevin Indig
  • The filter rate on impressions is incredibly high, with three-fourths filtered for privacy.
  • 12 months ago, the rate was only 2 percentage points higher.
  • The range I observed went from 59.3% all the way up to 93.6%.
Image Credit: Kevin Indig

2. Google filters out ~38% of clicks, but ~5% less than 12 months ago.

IMage Credit: Kevin Indig
  • Click filtering is not something we talk about a lot, but it seems Google doesn’t report up to one-third of all clicks that happened.
  • 12 months ago, Google filtered out over 40% of clicks.
  • The range of filtering spans from 6.7% to 88.5%!
Image Credit: Kevin Indig

The good news is that the filter rate has gone slightly down over the last 12 months, probably as a result of fewer “bot impressions.”

The bad news: The core problem persists. Even with these improvements, 38% click-filtering and 75% impression-filtering remain catastrophically high. A 5% improvement doesn’t make single-source GSC decisions reliable when three-fourths of your impression data is missing.

3. 2025 Impressions Are Highly Inflated

Image Credit: Kevin Indig

The last 12 months show a rollercoaster of GSC data:

  • In March 2025, Google intensified the rollout of AIOs and showed 58% more for the sites I analyzed.
  • In July, impressions grew by 25.3% and by another 54.6% in August. SERP scrapers somehow found a way around SearchGuard (the protection “bot” that Google uses to prevent SERP scrapers) and caused “bot impressions” to capture AIOs.
  • In September, Google removed the num=100 parameter, which caused impressions to drop by 30.6%.
Image Credit: Kevin Indig

Fast forward to today:

  • Clicks decreased by 56.6% since March 2025.
  • Impressions normalized (down -9.2%).
  • AIOs reduced by 31.3%.

I cannot come to a causative number of reduced clicks from AIOs, but the correlation is strong: 0.608. We know AIOs reduce clicks (makes logical sense), but we don’t know exactly how much. To figure that out, I’d have to measure CTR for queries before and after an AIO shows up.

But how do you know click decline is due to an AIO and not just poor content quality or content decay?

Look for temporal correlation:

  • Track when your clicks dropped against Google’s AIO rollout timeline (March 2025 spike). Poor content quality shows gradual decline; AIO impact is sharp and query-specific.
  • Cross-reference with position data. If rankings hold steady while clicks drop, that signals AIO cannibalization. Check if the affected queries are informational (AIO-prone) vs. transactional (AIO-resistant). Your 0.608 correlation coefficient between AIO presence and click reduction supports this diagnostic approach.

4. Bot Impressions Are Rising

Image Credit: Kevin Indig

I have reason to believe that SERP scrapers are coming back. We can measure the amount of impressions likely caused by bots by filtering out GSC data by queries that contain more than 10 words and two impressions. The chance that such a long query (prompt) is used by a human twice is close to zero.

The logic of bot impressions:

  • Hypothesis: Humans rarely search for the exact same 5+ word query twice in a short window.
  • Filter: Identify queries with 10+ words that have >1 impression but zero clicks.
  • Caveat: This method may capture some legitimate zero-click queries, but provides a directional estimate of bot activity.

I compared those queries over the last 30, 90, and 180 days:

  • Queries with +10 words and +1 impression grew by 25% over the last 180 days.
  • The range of bot impressions spans from 0.2% to 6.5% (last 30 days).

Here’s what you can anticipate as a “normal” percentage of bot impressions for a typical SaaS site:

  • Based on the 10-site B2B dataset, bot impressions range from 0.2% to 6.5% over 30 days, with queries containing 10+ words and 2+ impressions but 0 clicks.
  • For SaaS specifically, expect a 1-3% baseline for bot impressions. Sites with extensive documentation, technical guides, or programmatic SEO pages trend higher (4-6%).
  • The 25% growth over 180 days suggests scrapers are adapting post-SearchGuard. Monitor your percentile position within this range more than the absolute number.

Bot impressions do not affect your actual rankings – just your reporting by inflating impression counts. The practical impact? Misallocated resources if you optimize for inflated impression queries that humans never search for.

5. The Measurement Layer Is Broken

Single-source decisions based on GSC data alone become dangerous:

  • Three-fourths of impressions are filtered.
  • Bot impressions generate up to 6.5% of data.
  • AIOs reduce clicks by over 50%.
  • User behavior is structurally changing.

Your opportunity is in the methodology: Teams that build robust measurement frameworks (sampling rate scripts, bot-share calculations, multi-source triangulation) have a competitive advantage.


Featured Image: Paulo Bobita/Search Engine Journal

Why SEO Roadmaps Break In January (And How To Build Ones That Survive The Year) via @sejournal, @cshel

SEO roadmaps have a lot in common with New Year’s resolutions: They’re created with optimism, backed by sincere intent, and abandoned far sooner than anyone wants to admit.

The difference is that most people at least make it to Valentine’s Day before quietly deciding that daily workouts or dry January were an ambitious, yet misguided, experiment. SEO roadmaps often start unraveling while Punxsutawney Phil is still deep in REM sleep.

By the third or fourth week of the year, teams are already making “temporary” adjustments. A content cadence slips here. A technical initiative gets deprioritized there. A dependency turns out to be more complicated than anticipated, etc. None of this is framed as failure, naturally, but the original plan is already being renegotiated.

This doesn’t happen because SEO teams are bad at planning. It happens because annual SEO roadmaps are still built as if search were a stable environment with predictable inputs and outcomes.

(Narrator: Search is not, and has never been, a stable environment with predictable inputs or outcomes.)

In January, just like that diet plan, the SEO roadmap looks entirely doable. By February, you’re hiding in a dark pantry with a sleeve of Thin Mints, and the roadmap is already in tatters.

Here’s why those plans break so quickly and how to replace them with a planning model that holds up once the year actually starts moving.

The January Planning Trap

Annual SEO roadmaps are appealing because they feel responsible.

  • They give leadership something concrete to approve.
  • They make resourcing look predictable.
  • They suggest that search performance can be engineered in advance.

Except SEO doesn’t operate in a static system, and most roadmaps quietly assume that it does.

By the time Q1 is halfway over, teams are already reacting instead of executing. The plan didn’t fail because it was poorly constructed. It failed because it was built on outdated assumptions about how search works now.

Three Assumptions That Break By February

1. Algorithms Behave Predictably Over A 12-Month Period

Most annual roadmaps assume that major algorithm shifts are rare, isolated events.

That’s no longer true.

Search systems are now updated continuously. Ranking behavior, SERP layouts, AI integrations, and retrieval logic evolve incrementally –  often without a single, named “update” to react to.

A roadmap that assumes stability for even one full quarter is already fragile.

If your plan depends on a fixed set of ranking conditions remaining intact until December, it’s already obsolete.

2. Technical Debt Stays Static Unless Something “Breaks”

January plans usually account for new technical work like migrations, performance improvements, structured data, internal linking projects.

What they don’t account for is technical debt accumulation.

Every CMS update, plugin change, template tweak, tracking script, and marketing experiment adds friction. Even well-maintained sites slowly degrade over time.

Most SEO roadmaps treat technical SEO as a project with an end date. In reality, it’s a system that requires continuous maintenance.

By February, that invisible debt starts to surface – crawl inefficiencies, index bloat, rendering issues, or performance regressions – none of which were in the original plan.

3. Content Velocity Produces Linear Returns

Many annual SEO plans assume that content output scales predictably:

More content = more rankings = more traffic

That relationship hasn’t been linear for a long time.

Content saturation, intent overlap, internal competition, and AI-driven summaries all flatten returns. Publishing at the same pace doesn’t guarantee the same impact quarter over quarter.

By February, teams are already seeing diminishing returns from “planned” content and scrambling to justify why performance isn’t tracking to projections.

What Modern SEO Roadmap Planning Actually Looks Like

Roadmaps don’t need to disappear, but they do need to change shape.

Instead of a rigid annual plan, resilient SEO teams operate on a quarterly diagnostic model, one that assumes volatility and builds flexibility into execution.

The goal isn’t to abandon strategy. It’s to stop pretending that January can predict December.

A resilient model includes:

  • Quarterly diagnostic checkpoints, not just quarterly goals.
  • Rolling prioritization, based on what’s actually happening in search.
  • Protected capacity for unplanned technical or algorithmic responses.
  • Outcome-based planning, not task-based planning.

This shifts SEO from “deliverables by date” to “decisions based on signals.”

The Quarterly Diagnostic Framework

Instead of locking a yearlong roadmap, break planning into repeatable quarterly cycles:

Step 1: Assess (What Changed?)

At the start of each quarter, and ideally again mid-quarter, evaluate:

  • Crawl and indexation patterns.
  • Ranking volatility across key templates.
  • Performance deltas by intent, not just keywords.
  • Content cannibalization and decay.
  • Technical regressions or new constraints.

This is not a full audit. It’s a focused diagnostic designed to surface friction early.

Step 2: Diagnose (Why Did It Change?)

This is where most roadmaps fall apart: They track metrics but skip interpretation.

Diagnosis means asking:

  • Is this decline structural, algorithmic, or competitive?
  • Did we introduce friction, or did the ecosystem change around us?
  • Are we seeing demand shifts or retrieval shifts?

Without this layer, teams chase symptoms instead of causes.

Step 3: Fix (What Actually Matters Now?)

Only after diagnosis should priorities shift. That shift may involve pausing content production, redirecting engineering resources, or deliberately doing nothing while volatility settles. Resilient planning accepts that the “right” work in February may bear little resemblance to what was approved in January.

How To Audit Mid-Quarter Without Panicking

Mid-quarter reviews don’t mean throwing out the plan. They mean stress-testing it.

A healthy mid-quarter SEO check should answer three questions:

  1. What assumptions no longer hold?
  2. What work is no longer high-leverage?
  3. What risk is emerging that wasn’t visible before?

If the answer to any of those changes execution, that’s not failure. It’s adaptive planning.

The teams that struggle are the ones afraid to admit the plan needs to change.

The Bottom Line

The acceleration introduced by AI-driven retrieval has shortened the gap between planning and obsolescence.

January SEO roadmaps don’t fail because teams lack strategy. They fail because they assume a level of stability that search has not offered in years. If your SEO plan can’t absorb algorithmic shifts, technical debt, and nonlinear content returns, it won’t survive the year. The difference between teams that struggle and teams that adapt is simple: One plans for certainty, the other plans for reality.

The teams that win in search aren’t the ones with the most detailed January roadmap. They’re the ones that can still make good decisions in February.

More Resources:


Featured Image: Anton Vierietin/Shutterstock

WordPress Publishes AI Guidelines To Combat AI Slop via @sejournal, @martinibuster

WordPress published guidelines for using AI for coding plugins, themes, documentation, and media assets. The purpose of the guidelines, guided by five principles, is to keep WordPress contributions transparent, GPL-compatible, and human-accountable, while maintaining high quality standards for AI-assisted work.

The new guidelines lists the following five principles:

  1. “You are responsible for your contributions (AI can assist, but it isn’t a contributor).
  2. Disclose meaningful AI assistance in your PR description and/or Trac ticket comment.
  3. License compatibility matters: contributions must remain compatible with GPLv2-or-later, including AI-assisted output.
  4. Non-code assets count too (docs, screenshots, images, educational materials).
  5. Quality over volume: avoid low-signal, unverified “AI slop”; reviewers may close or reject work that doesn’t meet the bar.”

Transparency

The purpose of the transparency guidelines is to encourage contributors to disclose that AI was used and how it was used so that reviewers can be aware when evaluating the work.

License Compatibility And Tool Choice

Licensing is a big deal with WordPress because it’s designed to be a fully open source publishing platform under the GPLv2 licensing framework. Everything that’s made for WordPress, including plugins and themes, must also be open source. It’s an essential element of everything created with WordPress.

The guidelines specify that AI cannot be used if the output is not licensable under GPLv2.

It also states:

“Do not use tools whose terms forbid using their output in GPL-licensed projects or impose additional restrictions on redistribution.

Do not rely on tools to “launder” incompatible licenses. If an AI output reproduces non-free or incompatible code, it cannot be included.”

AI Slop

Of course, the guidelines address the issue of AI slop. In this case, AI slop is defined as hallucinated references (such as links or APIs that do not exist), overly complicated code where simpler solutions exist, and GitHub PRs that are generic or do not reflect actual testing or experience.

The AI Slop guidelines has recommendations of what they expect from contributors:

“Use AI to draft, then review yourself.

Submit PRs (or patches) that are small, concise and with atomic and well defined commit messages to make reviewing easier.

Run and document real tests.

Link to real Trac tickets, GitHub issues, or documentation that you have verified.”

The guidelines are clear that the WordPress contributors who are responsible for overseeing, reviewing, and deciding whether changes are accepted into a specific part of the project may close or reject contributions that they determine to be AI slop “with little added human insight.”

Takeaways

The new WordPress AI guidelines appear to be about preserving trust in the contribution process as AI becomes more common across development, documentation, and media creation. It in no way discourages the use of AI but rather encourages its use in a responsible manner.

Requiring disclosure, enforcing GPL compatibility, and giving maintainers the authority to reject low-quality submissions, the guidelines set boundaries that protect both the legal integrity of the WordPress project and the time of its reviewers.

Featured Image by Shutterstock/Ivan Moreno sl

What’s next for EV batteries in 2026

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

Demand for electric vehicles and the batteries that power them has never been hotter.

In 2025, EVs made up over a quarter of new vehicle sales globally, up from less than 5% in 2020. Some regions are seeing even higher uptake: In China, more than 50% of new vehicle sales last year were battery electric or plug-in hybrids. In Europe, more purely electric vehicles hit the roads in December than gas-powered ones. (The US is the notable exception here, dragging down the global average with a small sales decline from 2024.)

As EVs become increasingly common on the roads, the battery world is growing too. Looking ahead, we could soon see wider adoption of new chemistries, including some that deliver lower costs or higher performance. Meanwhile, the geopolitics of batteries are shifting, and so is the policy landscape. Here’s what’s coming next for EV batteries in 2026 and beyond.

A big opportunity for sodium-ion batteries

Lithium-ion batteries are the default chemistry used in EVs, personal devices, and even stationary storage systems on the grid today. But in a tough environment in some markets like the US, there’s a growing interest in cheaper alternatives. Automakers right now largely care just about batteries’ cost, regardless of performance improvements, says Kara Rodby, a technical principal at Volta Energy Technologies, a venture capital firm that focuses on energy storage technology.

Sodium-ion cells have long been held up as a potentially less expensive alternative to lithium. The batteries are limited in their energy density, so they deliver a shorter range than lithium-ion. But sodium is also more abundant, so they could be cheaper.

Sodium’s growth has been cursed, however, by the very success of lithium-based batteries, says Shirley Meng, a professor of molecular engineering at the University of Chicago. A lithium-ion battery cell cost $568 per kilowatt-hour in 2013, but that cost had fallen to just $74 per kilowatt-hour by 2025—quite the moving target for cheaper alternatives to chase.

Sodium-ion batteries currently cost about $59 per kilowatt-hour on average. That’s less expensive than the average lithium-ion battery. But if you consider only lithium iron phosphate (LFP) cells, a lower-end type of lithium-ion battery that averages $52 per kilowatt-hour, sodium is still more expensive today. 

We could soon see an opening for sodium-batteries, though. Lithium prices have been ticking up in recent months, a shift that could soon slow or reverse the steady downward march of prices for lithium-based batteries. 

Sodium-ion batteries are already being used commercially, largely for stationary storage on the grid. But we’re starting to see sodium-ion cells incorporated into vehicles, too. The Chinese companies Yadea, JMEV, and HiNa Battery have all started producing sodium-ion batteries in limited numbers for EVs, including small, short-range cars and electric scooters that don’t require a battery with high energy density. CATL, a Chinese battery company that’s the world’s largest, says it recently began producing sodium-ion cells. The company plans to launch its first EV using the chemistry by the middle of this year

Today, both production and demand for sodium-ion batteries are heavily centered in China. That’s likely to continue, especially after a cutback in tax credits and other financial support for the battery and EV industries in the US. One of the biggest sodium-battery companies in the US, Natron, ceased operations last year after running into funding issues.

We could also see progress in sodium-ion research: Companies and researchers are developing new materials for components including the electrolyte and electrodes, so the cells could get more comparable to lower-end lithium-ion cells in terms of energy density, Meng says. 

Major tests for solid-state batteries

As we enter the second half of this decade, many eyes in the battery world are on big promises and claims about solid-state batteries.

These batteries could pack more energy into a smaller package by removing the liquid electrolyte, the material that ions move through when a battery is charging and discharging. With a higher energy density, they could unlock longer-range EVs.

Companies have been promising solid-state batteries for years. Toyota, for example, once planned to have them in vehicles by 2020. That timeline has been delayed several times, though the company says it’s now on track to launch the new cells in cars in 2027 or 2028.

Historically, battery makers have struggled to produce solid-state batteries at the scale needed to deliver a commercially relevant supply for EVs. There’s been progress in manufacturing techniques, though, and companies could soon actually make good on their promises, Meng says. 

Factorial Energy, a US-based company making solid-state batteries, provided cells for a Mercedes test vehicle that drove over 745 miles on a single charge in a real-world test in September. The company says it plans to bring its tech to market as soon as 2027. Quantumscape, another major solid-state player in the US, is testing its cells with automotive partners and plans to have its batteries in commercial production later this decade.  

Before we see true solid-state batteries, we could see hybrid technologies, often referred to as semi-solid-state batteries. These commonly use materials like gel electrolytes, reducing the liquid inside cells without removing it entirely. Many Chinese companies are looking to build semi-solid-state batteries before transitioning to entirely solid-state ones, says Evelina Stoikou, head of battery technologies and supply chains at BloombergNEF, an energy consultancy.

A global patchwork

The picture for the near future of the EV industry looks drastically different depending on where you’re standing.

Last year, China overtook Japan as the country with the most global auto sales. And more than one in three EVs made in 2025 had a CATL battery in it. Simply put, China is dominating the global battery industry, and that doesn’t seem likely to change anytime soon.

China’s influence outside its domestic market is growing especially quickly. CATL is expected to begin production this year at its second European site; the factory, located in Hungary, is an $8.2 billion project that will supply automakers including BMW and the Mercedes-Benz group. Canada recently signed a deal that will lower the import tax on Chinese EVs from 100% to roughly 6%, effectively opening the Canadian market for Chinese EVs.

Some countries that haven’t historically been major EV markets could become bigger players in the second half of the decade. Annual EV sales in Thailand and Vietnam, where the market was virtually nonexistent just a few years ago, broke 100,000 in 2025. Brazil, in particular, could see its new EV sales more than double in 2026 as major automakers including Volkswagen and BYD set up or ramp up production in the country. 

On the flip side, EVs are facing a real test in 2026 in the US, as this will be the first calendar year after the sunset of federal tax credits that were designed to push more drivers to purchase the vehicles. With those credits gone, growth in sales is expected to continue lagging. 

One bright spot for batteries in the US is outside the EV market altogether. Battery manufacturers are starting to produce low-cost LFP batteries in the US, largely for energy storage applications. LG opened a massive factory to make LFP batteries in mid-2025 in Michigan, and the Korean battery company SK On plans to start making LFP batteries at its facility in Georgia later this year. Those plants could help battery companies cash in on investments as the US EV market faces major headwinds. 

Even as the US lags behind, the world is electrifying transportation. By 2030, 40% of new vehicles sold around the world are projected to be electric. As we approach that milestone, expect to see more global players, a wider selection of EVs, and an even wider menu of batteries to power them. 

The Download: inside a deepfake marketplace, and EV batteries’ future

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Inside the marketplace powering bespoke AI deepfakes of real women

Civitai—an online marketplace for buying and selling AI-generated content, backed by the venture capital firm Andreessen Horowitz—is letting users buy custom instruction files for generating celebrity deepfakes. Some of these files were specifically designed to make pornographic images banned by the site, a new analysis has found.

The study, from researchers at Stanford and Indiana University, looked at people’s requests for content on the site, called “bounties.” The researchers found that between mid-2023 and the end of 2024, most bounties asked for animated content—but a significant portion were for deepfakes of real people, and 90% of these deepfake requests targeted women. Read the full story.

—James O’Donnell

What’s next for EV batteries in 2026

Demand for electric vehicles and the batteries that power them has never been hotter.

In 2025, EVs made up over a quarter of new vehicle sales globally, up from less than 5% in 2020. Some regions are seeing even higher uptake: In China, more than 50% of new vehicle sales last year were battery electric or plug-in hybrids. In Europe, more purely electric vehicles hit the roads in December than gas-powered ones. (The US is the notable exception here, dragging down the global average with a small sales decline from 2024.)

As EVs become increasingly common on the roads, the battery world is growing too. Here’s what’s coming next for EV batteries in 2026 and beyond.

—Casey Crownhart

This story is part of MIT Technology Review’s What’s Next series, which examines industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

TR10: Base-edited baby

Kyle “KJ” Muldoon Jr. was born with a rare, potentially fatal genetic disorder that left his body unable to remove toxic ammonia from his blood. The University of Pennsylvania offered his parents an alternative to a liver transplant: gene-editing therapies.

The team set to work developing a tailored treatment using base editing—a form of CRISPR that can correct genetic “misspellings” by changing single bases, the basic units of DNA. KJ received an initial low dose when he was seven months old, and later received two higher doses. Today, KJ is doing well. At an event in October last year, his happy parents described how he was meeting all his developmental milestones.

Others have received gene-editing therapies intended to treat conditions including sickle cell disease and a predisposition to high cholesterol. But KJ was the first to receive a personalized treatment—one that was designed just for him and will probably never be used again. Read why we made it one of our 10 Breakthrough Technologies this year, and check out the rest of the list.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 A social network for AI agents is vulnerable to abuse
A misconfiguration meant anyone could take control of any agent. (404 Media)
+ Moltbook is loosely modeled on Reddit, but humans are unable to post. (FT $)

2 Google breached its own ethics rules to help an Israeli contractor
It helped a military worker to analyze drone footage, a whistleblower has claimed. (WP $)

3 Capgemini is selling its unit linked to ICE
After the French government asked it to clarify its work for the agency. (Bloomberg $) 
+ The company has signed $12.2mn in contracts under the Trump administration. (FT $)
+ Here’s how to film ICE activities as safely as possible. (Wired $)

4 China has a plan to prime its next generation of AI experts 
Thanks to its elite genius class system. (FT $)
+ The country is going all-in on AI healthcare. (Rest of World)
+ The State of AI: Is China about to win the race? (MIT Technology Review)

5 Indonesia has reversed its ban on xAI’s Grok
After it announced plans to improve its compliance with the country’s laws. (Reuters)
+ Indonesia maintains a strict stance against pornographic content. (NYT $)
+ Malaysia and the Philippines have also lifted bans on the chatbot. (TechCrunch)

6 Don’t expect to hitch a ride on a Blue Origin rocket anytime soon
Jeff Bezos’ venture won’t be taking tourists into space for at least two years. (NYT $)
+ Artemis II astronauts are due to set off for the moon soon. (IEEE Spectrum)
+ Commercial space stations are on our list of 10 Breakthrough Technologies for 2026. (MIT Technology Review)

7 America’s push for high-speed internet is under threat
There aren’t enough skilled workers to meet record demand. (WSJ $)

8 Can AI help us grieve better?
A growing cluster of companies are trying to find out. (The Atlantic $)
+ Technology that lets us “speak” to our dead relatives has arrived. Are we ready? (MIT Technology Review)

9 How to fight future insect infestations 🍄
A certain species of fungus could play a key role. (Ars Technica)
+ How do fungi communicate? (MIT Technology Review)

10 What a robot-made latte tastes like, according to a former barista
Damn fine, apparently. (The Verge)

Quote of the day

 “It feels like a wild bison rampaging around in my computer.”

—A user who signed up to AI agent Moltbot remarks on the bot’s unpredictable behavior, Rest of World reports.

One more thing

How Wi-Fi sensing became usable tech

Wi-Fi sensing is a tantalizing concept: that the same routers bringing you the internet could also detect your movements. But, as a way to monitor health, it’s mostly been eclipsed by other technologies, like ultra-wideband radar. 

Despite that, Wi-Fi sensing hasn’t gone away. Instead, it has quietly become available in millions of homes, supported by leading internet service providers, smart-home companies, and chip manufacturers.

Soon it could be invisibly monitoring our day-to-day movements for all sorts of surprising—and sometimes alarming—purposes. Read the full story

—Meg Duff

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ These intrepid Scottish bakers created the largest ever Empire biscuit (a classic shortbread cookie covered in icing) 🍪
+ My, what big tentacles you have!
+ If you’ve been feeling like you’re stuck in a rut lately, this advice could be exactly what you need to overcome it.
+ These works of psychedelic horror are guaranteed to send a shiver down your spine.

The crucial first step for designing a successful enterprise AI system

Many organizations rushed into generative AI, only to see pilots fail to deliver value. Now, companies want measurable outcomes—but how do you design for success?

At Mistral AI, we partner with global industry leaders to co-design tailored AI solutions that solve their most difficult problems. Whether it’s increasing CX productivity with Cisco, building a more intelligent car with Stellantis, or accelerating product innovation with ASML, we start with open frontier models and customize AI systems to deliver impact for each company’s unique challenges and goals.

Our methodology starts by identifying an iconic use case, the foundation for AI transformation that sets the blueprint for future AI solutions. Choosing the right use case can mean the difference between true transformation and endless tinkering and testing.

Identifying an iconic use case

Mistral AI has four criteria that we look for in a use case: strategic, urgent, impactful, and feasible.

First, the use case must be strategically valuable, addressing a core business process or a transformative new capability. It needs to be more than an optimization; it needs to be a gamechanger. The use case needs to be strategic enough to excite an organization’s C-suite and board of directors.

For example, use cases like an internal-facing HR chatbot are nice to have, but they are easy to solve and are not enabling any new innovation or opportunities. On the other end of the spectrum, imagine an externally facing banking assistant that can not only answer questions, but also help take actions like blocking a card, placing trades, and suggesting upsell/cross-sell opportunities. This is how a customer-support chatbot is turned into a strategic revenue-generating asset.

Second, the best use case to move forward with should be highly urgent and solve a business-critical problem that people care about right now. This project will take time out of people’s days—it needs to be important enough to justify that time investment. And it needs to help business users solve immediate pain points.

Third, the use case should be pragmatic and impactful. From day one, our shared goal with our customers is to deploy into a real-world production environment to enable testing the solution with real users and gather feedback. Many AI prototypes end up in the graveyard of fancy demos that are not good enough to put in front of customers, and without any scaffolding to evaluate and improve. We work with customers to ensure prototypes are stable enough to release, and that they have the necessary support and governance frameworks.

Finally, the best use case is feasible. There may be several urgent projects, but choosing one that can deliver a quick return on investment helps to maintain the momentum needed to continue and scale.

This means looking for a project that can be in production within three months—and a prototype can be live within a few weeks. It’s important to get a prototype in front of end users as fast as possible to get feedback to make sure the project is on track, and pivot as needed.

Where use cases fall short

Enterprises are complex, and the path forward is not usually obvious. To weed through all the possibilities and uncover the right first use case, Mistral AI will run workshops with our customers, hand-in-hand with subject-matter experts and end users.

Representatives from different functions will demo their processes and discuss business cases that could be candidates for a first use case—and together we agree on a winner. Here are some examples of types of projects that don’t qualify.

Moonshots: Ambitious bets that excite leadership but lack a path to quick ROI. While these projects can be strategic and urgent, they rarely meet the feasibility and impact requirements.

Future investments: Long-term plays that can wait. While these projects can be strategic and feasible, they rarely meet the urgency and impact requirements.

Tactical fixes: Firefighting projects that solve immediate pain but don’t move the needle. While these cases can be urgent and feasible, they rarely meet the strategy and impact requirements.

Quick wins: Useful for building momentum, but not transformative. While they can be impactful and feasible, they rarely meet the strategy and urgency requirements.

Blue sky ideas: These projects are gamechangers, but they need maturity to be viable. While they can be strategic and impactful, they rarely meet the urgency and feasibility requirements.

Hero projects: These are high-pressure initiatives that lack executive sponsorship or realistic timelines. While they can be urgent and impactful, they rarely meet the strategy and feasibility requirements.

Moving from use case to deployment

Once a clearly defined and strategic use case ready for development is identified, it’s time to move into the validation phase. This means doing an initial data exploration and data mapping, identifying a pilot infrastructure, and choosing a target deployment environment.

This step also involves agreeing on a draft pilot scope, identifying who will participate in the proof of concept, and setting up a governance process.

Once this is complete, it’s time to move into the building phase. Companies that partner with Mistral work with our in-house applied AI scientists who build our frontier models. We work together to design, build, and deploy the first solution.

During this phase, we focus on co-creation, so we can transfer knowledge and skills to the organizations we’re partnering with. That way, they can be self-sufficient far into the future. The output of this phase is a deployed AI solution with empowered teams capable of independent operation and innovation.

The first step is everything

After the first win, it’s imperative to use the momentum and learnings from the iconic use case to identify more high-value AI solutions to roll out. Success is when we have a scalable AI transformation blueprint with multiple high-value solutions across the organization.

But none of this could happen without successfully identifying that first iconic use case. This first step is not just about selecting a project—it’s about setting the foundation for your entire AI transformation.

It’s the difference between scattered experiments and a strategic, scalable journey toward impact. At Mistral AI, we’ve seen how this approach unlocks measurable value, aligns stakeholders, and builds momentum for what comes next.

The path to AI success starts with a single, well-chosen use case: one that is bold enough to inspire, urgent enough to demand action, and pragmatic enough to deliver.

This content was produced by Mistral AI. It was not written by MIT Technology Review’s editorial staff.

What we’ve been getting wrong about AI’s truth crisis

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

What would it take to convince you that the era of truth decay we were long warned about—where AI content dupes us, shapes our beliefs even when we catch the lie, and erodes societal trust in the process—is now here? A story I published last week pushed me over the edge. It also made me realize that the tools we were sold as a cure for this crisis are failing miserably. 

On Thursday, I reported the first confirmation that the US Department of Homeland Security, which houses immigration agencies, is using AI video generators from Google and Adobe to make content that it shares with the public. The news comes as immigration agencies have flooded social media with content to support President Trump’s mass deportation agenda—some of which appears to be made with AI (like a video about “Christmas after mass deportations”).

But I received two types of reactions from readers that may explain just as much about the epistemic crisis we’re in. 

One was from people who weren’t surprised, because on January 22 the White House had posted a digitally altered photo of a woman arrested at an ICE protest, one that made her appear hysterical and in tears. Kaelan Dorr, the White House’s deputy communications director, did not respond to questions about whether the White House altered the photo but wrote, “The memes will continue.”

The second was from readers who saw no point in reporting that DHS was using AI to edit content shared with the public, because news outlets were apparently doing the same. They pointed to the fact that the news network MS Now (formerly MSNBC) shared an image of Alex Pretti that was AI-edited and appeared to make him look more handsome, a fact that led to many viral clips this week, including one from Joe Rogan’s podcast. Fight fire with fire, in other words? A spokesperson for MS Now told Snopes that the news outlet aired the image without knowing it was edited.

There is no reason to collapse these two cases of altered content into the same category, or to read them as evidence that truth no longer matters. One involved the US government sharing a clearly altered photo with the public and declining to answer whether it was intentionally manipulated; the other involved a news outlet airing a photo it should have known was altered but taking some steps to disclose the mistake.

What these reactions reveal instead is a flaw in how we were collectively preparing for this moment. Warnings about the AI truth crisis revolved around a core thesis: that not being able to tell what is real will destroy us, so we need tools to independently verify the truth. My two grim takeaways are that these tools are failing, and that while vetting the truth remains essential, it is no longer capable on its own of producing the societal trust we were promised.

For example, there was plenty of hype in 2024 about the Content Authenticity Initiative, cofounded by Adobe and adopted by major tech companies, which would attach labels to content disclosing when it was made, by whom, and whether AI was involved. But Adobe applies automatic labels only when the content is wholly AI-generated. Otherwise the labels are opt-in on the part of the creator.

And platforms like X, where the altered arrest photo was posted, can strip content of such labels anyway (a note that the photo was altered was added by users). Platforms can also simply not choose to show the label; indeed, when Adobe launched the initiative, it noted that the Pentagon’s website for sharing official images, DVIDS, would display the labels to prove authenticity, but a review of the website today shows no such labels.

Noticing how much traction the White House’s photo got even after it was shown to be AI-altered, I was struck by the findings of a very relevant new paper published in the journal Communications Psychology. In the study, participants watched a deepfake “confession” to a crime, and the researchers found that even when they were told explicitly that the evidence was fake, participants relied on it when judging an individual’s guilt. In other words, even when people learn that the content they’re looking at is entirely fake, they remain emotionally swayed by it. 

“Transparency helps, but it isn’t enough on its own,” the disinformation expert Christopher Nehring wrote recently about the study’s findings. “We have to develop a new masterplan of what to do about deepfakes.”

AI tools to generate and edit content are getting more advanced, easier to operate, and cheaper to run—all reasons why the US government is increasingly paying to use them. We were well warned of this, but we responded by preparing for a world in which the main danger was confusion. What we’re entering instead is a world in which influence survives exposure, doubt is easily weaponized, and establishing the truth does not serve as a reset button. And the defenders of truth are already trailing way behind.

Update: This story was updated on February 2 with details about how Adobe applies its content authenticity labels.

Better Metrics for AI Search Visibility

The rise of AI-generated search and discovery is pushing merchants to measure their products’ visibility on those platforms. Many search optimizers are attempting to apply traditional metrics such as traffic from genAI and rankings in the answers. Both fall short.

Traffic. Focusing on traffic obscures the purpose of AI answers: to satisfy a need on-site, not to generate clicks.

AI-generated solutions do not typically include links to branded websites. Google’s AI Overviews, for example, sometimes links product names to organic search listings.

Thus visibility does not equate to traffic. A merchant’s products could appear in an AI answer and receive no clicks.

Screenshot of the AI Overview showing the North Face citation and link to an organic listing.

Brand names cited in Google’s AI Overviews often link to organic search listings, such as this example for North Face hiking boots.

Rankings. AI answers often include lists. Many sellers are trying to track those lists to rank at or near the top. Yet tracking such rankings is impossible.

AI answers are unpredictable. A recent study by Sparktoro found that AI platforms recommend different brands and different orders every time the same person asks the same question.

Better AI Metrics

Here are better metrics to measure AI visibility.

Product or brand positioning in LLM training data

Training data is fundamental to AI visibility because large language models default to what they know. Even when they query Google and elsewhere, LLMs often use their training data to guide the search terms.

It’s therefore essential to track what LLMs retain about your brand and competitors and, importantly, what is incorrect or outdated. Then focus on providing missing or corrected data on your site and across all owned channels.

Manual prompting in ChatGPT, Claude, and Gemini (at least) will help identify the gaps. The prompts can be:

  • “What do you know about [MY PRODUCT]?”
  • “Compare [MY PRODUCT] vs [MY COMPETITOR’S PRODUCT].”

Profound, Peec AI, and other AI visibility trackers can set up these prompts to monitor product positioning over time.

When using such visibility tools, keep in mind:

  • AI tracking tools enter prompts via LLMs’ APIs. Humans often see different results due to personalization and differences among AI models. API results are better for checking training data because LLMs likely return results from that data (versus live searches) to save resources.
  • The tools’ visibility scores depend entirely on the prompt. In the tools, separate branded prompts in a folder, as they will likely score 100%. Also, focus on non-branded prompts that reflect a product’s value proposition. Prompts irrelevant to an item’s key features will likely score 0%.

Most cited sources

LLM platforms increasingly conduct live searches when responding to prompts. They may query Google or Bing — yes, organic search drives AI visibility — or crawl other sources such as Reddit.

Citations, such as articles or videos, from those live searches influence the AI responses. But the citations vary widely because LLMs fan out across different (often unrelated) queries. So, trying to get included in every cited source is not realistic.

However, prompts often produce the same, influential sources repeatedly. These are worth exploring to include your brand or product. AI visibility trackers can collect the most cited URLs for your brand, product, or industry.

Brand mentions and branded search volume

Use Search Console or other traditional analytics tools to track:

  • Queries that contain your brand name or a version of it.
  • Number of clicks from those queries.
  • Impressions from those queries. The more AI answers include a brand name, the more humans will search for it.

In Search Console, create a filter in the “Performance” section to view data for branded queries.

Screenshot of the Search Console Performance section.

Create a filter in Search Console’s “Performance” section to view data for branded queries.