While Elon Musk faces off against his former colleague and OpenAI co-founder Sam Altman in court, Musk’s rival firm xAI, founded to take on OpenAI, isn’t slowing down on launching competitive new products and services.

Last night, xAI shipped a new, proprietary base large language model (LLM), Grok 4.3, and a new voice cloning suite on the web.

The new products arrive after months of tumult from xAI that saw all of Musk’s 10 original co-founders of the lab and dozens more researchers exit the firm and Grok was eclipsed on performance by many new competing LLMs from the likes of OpenAI, Anthropic, Google, and Chinese firms DeepSeek, Moonshot (Kimi), Alibaba (Qwen), z.ai, and others.

While Grok 4.3 does mark a significant leap in performance on third-party benchmarks over its direct predecessor Grok 4.2, according to the independent AI model evaluation firm Artificial Analysis, it still remains below the state-of-the-art set by OpenAI and Anthropic’s latest models.

But the marquee feature of the Grok brand has — other than Musk’s stated opposition to “wokeness” and its more freewheeling personality and image generation policy — increasingly been its low price point when accessed by developers and users via the xAI application programming interface (API), a trend only furthered by Grok 4.3, which costs $1.25 per million input tokens and $2.50 per million output tokens (up to 200,000 input tokens, at which point costs double, a common pricing strategy of leading AI labs) compared to its direct predecessor Grok 4.2’s initial API pricing of $2/$6 per million input/output tokens.

According to xAI’s release notes, Grok 4.3 began beta testing in April for subscribers to xAI’s SuperGrok ($30 monthly) plan, and those of its sibling social network, X, through its Premium+ plan ($40 monthly with 50% for first two months). Now it’s available to all through the xAI API and through partner OpenRouter.

Reasoning baked-in and agentic tool-use capabilities

At the core of Grok 4.3 is a fundamental shift in how the model processes information. Unlike previous iterations where “chain-of-thought” or reasoning could often be toggled or configured by effort levels, Grok 4.3 is built with reasoning as an active, permanent state.

This means the model is designed to “think” before it speaks for every query, a strategy intended to maximize factual accuracy and the handling of complex, multi-step instructions.

The model’s memory is equally expansive, featuring a 1 million-token context window. To put this in perspective, a million tokens is roughly equivalent to several thick novels or the entire codebase of a mid-sized application.

This allows Grok 4.3 to maintain coherence over massive datasets, though xAI has implemented a “Higher context pricing” structure for requests that exceed the 200,000-token threshold.

This tiering suggests that while the “long-term memory” is available, the computational cost of managing that much information remains a significant overhead.Technically, the model accepts both text and image inputs, outputting text.

It is specifically optimized for agentic workflows—scenarios where an AI is not just answering a question but acting as an autonomous agent to complete a task.

For the first time, Grok has access to the same tools and environments a human professional would use. Evidence of this shift is visible in early user interactions:

• Spreadsheet Engineering: In one instance, the model spent 6 minutes and 22 seconds in a “thought” phase to build a comprehensive OSRS Sailing Combat DPS analyzer. The resulting .xlsx file wasn’t a simple table but a multi-sheet dashboard including a “Reference_Data” set and a complex “DPS_Calculator” with formulaic auto-calculations.

• Professional Documentation: Grok now generates formatted PDFs, such as 12-page reports on SpaceX products. These documents incorporate branding, logos, hero images, and structured tables, moving well beyond the markdown blocks of previous iterations.

• Visual Presentations: The model can design 9-slide PowerPoint decks, utilizing a “Sandwich Structure” (dark titles/conclusions with light content) and integrating data-driven decision matrices and humor.

However, its knowledge of the world is not infinite; the release notes list a knowledge cut-off date of December 2025. Yet, thanks to built-in web search, Grok can reference and use up-to-date information.

In fact, Grok 4.3 arrives with an enhanced ecosystem of tools designed to make it a functional digital employee. The xAI platform now offers a robust set of server-side tools that the model can invoke autonomously based on the complexity of the query.

• Web and X Search: These tools allow Grok to bypass its knowledge cutoff by browsing the live internet or searching X (formerly Twitter) posts, user profiles, and threads.

• Code Execution: The model can run Python code in a sandboxed environment to solve mathematical problems or process data.

• File and Collections Search: A built-in Retrieval-Augmented Generation (RAG) system allows users to query uploaded document collections or search through specific file attachments.

xAI’s Custom Voices let you clone your voice at high quality in a minute or two

Beyond text, xAI has introduced Custom Voices, a sophisticated voice-cloning API and web-based voice cloning creation suite.

This product allows developers to clone a voice from a reference audio clip as short as 120 seconds. Once cloned, the “voice ID” can be used across xAI’s Text-to-Speech (TTS) and Voice Agent APIs.

xAI’s documentation emphasizes that this is not merely about timbre; the model is designed to pick up delivery patterns.

If a user records a reference clip in a “customer support” style, the resulting AI voice will mimic that helpful, professional inflection.

Despite the creative potential, xAI has placed strict geographic limits on this feature, making it available only in the United States, with a notable exception for Illinois due to regional biometric and privacy regulations.

While the console playground is open for general use, programmatic access via the POST /v1/custom-voices endpoint is currently gated to teams on an Enterprise plan.

I tried it myself and after moving through the requisite voice sampling screens on the web — the tool asks you to read aloud several passages of unrelated dialog — I indeed had a copy of my voice that sounded eerily identical to mine and accurately pronounced new words the same way I would when reading allowed from a new script it was given.

You can delete your custom voices in one click on xAI’s Custom Voices web application and create up to 30 new ones at a time.

In terms of licensing, the Custom Voices feature is strictly “scoped to your team” and is never made available to other users, ensuring a private, commercial license for corporate assets.

Access to the new Voice Agent API (grok-voice-think-fast-1.0) is billed at a flat rate of $3.00 per hour ($0.05 per minute) for speech-to-speech interactions. This is on the low-medium end of costs for other competing voice agents, according to my research:

Complementing this is the standalone Text-to-Speech (TTS) service, which offers five distinct voices (Eve, Ara, Rex, Sal, and Leo) and is priced at $4.20 per 1 million characters.

For transcription needs, the Speech-to-Text (STT) API provides real-time streaming at $0.20 per hour, while batch processing is available at a discounted rate of $0.10 per hour.

To ensure security for client-side applications, xAI utilizes Ephemeral Tokens, allowing for secure WebSocket connections without exposing primary API keys.

Once created, these voices are private to the user’s team and can be used across all voice APIs by referencing a unique 8-character alphanumeric voice_id.

For highly regulated sectors, xAI maintains production-ready standards, including SOC 2 Type II auditing, HIPAA eligibility for healthcare workloads, and GDPR compliance.

Aggressively low API pricing as a differentiator

The most aggressive aspect of the Grok 4.3 announcement is its pricing structure. Bindu Reddy, CEO of enterprise assistant startup Abacus AI noted on X that the model is “as smart as Sonnet 4.6 and 5x cheaper and faster”.

The standard API rates are set at $1.25 per million input tokens and $2.50 per million output tokens. This reflects a significant reduction in cost compared to its predecessor, Grok 4.20, with Artificial Analysis reporting an approximately 40% lower input price and 60% lower output price.

According to our calculations at VentureBeat, that places Grok-4.3 firmly in the lowest cost half of all major foundation models, far closer to Chinese open source offerings than its U.S. proprietary rivals:

However, the “reasoning” nature of the model introduces a new billing category: Reasoning tokens.

These are tokens generated during the model’s internal thinking process and are billed at the same rate as standard completion tokens. Effectively, users pay for the AI to “think” before it provides the final answer. xAI has also introduced several unique fee structures:

• Prompt Caching: Repeated prompts are significantly cheaper, at $0.20 per million tokens, incentivizing developers to reuse context.

• Tool Invocations: While token usage for tools is billed at standard rates, the act of invoking a tool carries a flat fee—$5.00 per 1,000 calls for Web Search or Code Execution, and $10.00 for File Attachments.

• Usage Guideline Violation Fee: In a move that may set a new industry precedent, xAI charges a $0.05 fee for requests that are blocked by their safety filters before generation even begins.

The model itself remains accessible via a standard commercial API, with xAI recommending that all developers migrate to grok-4.3 as their “most intelligent and fastest model”.

Third-party benchmark evaluations and analysis

The reception of Grok 4.3 has been polarized, depending largely on the specific use case. Professional benchmarkers and developers have highlighted a “stark gap” between the model’s domain-specific strengths and its general reasoning consistency.

According to independent AI evaluation firm Vals AI, Grok 4.3 has taken the top spot on several specialized indices. It currently ranks #1 on CaseLaw v2 (79.3% accuracy) and #1 on CorpFin.

This 25-point jump in legal reasoning over Grok 4.20 suggests that the “always-on reasoning” architecture is particularly well-suited for the dense, logical structures of law and finance.

Artificial Analysis corroborated this performance, noting a massive improvement in agentic tasks, scoring an Elo of 1500 on the GDPval-AA benchmark, surpassing competitors like Gemini 3.1 Pro and GPT-5.4 mini.

Conversely, users focused on general-purpose agents and coding have highlighted deficiencies.

AI automated brick-and-mortar retail company Andon Labs reported that Grok 4.3 is a “big regression” on the Vending-Bench 2, which measures an AI’s ability to take consistent actions in a simulation.

They colorfully described the model as having “narcolepsy problems,” preferring to remain inactive for multiple simulation days rather than taking the required actions.

The sentiment was echoed by Vals AI, which noted that while the model improved in some coding areas, it remains weak on general coding tasks and “struggles with difficult math problems,” scoring only 11% on ProofBench.

Should your enterprise use Grok 4.3?

The launch of Grok 4.3 represents a calculated bet by xAI that the market wants specialized brilliance and extreme cost efficiency over a perfectly balanced generalist.

By achieving a score of 53 on the Artificial Analysis Intelligence Index while remaining on the “Pareto frontier” of cost-per-intelligence, xAI is positioning itself as the “value” leader for enterprise applications in legal and financial tech.

The “always-on reasoning” is a double-edged sword. While it provides the depth needed to navigate complex case law, the community reports of “narcolepsy” suggest that a model that is always “thinking” may occasionally think itself into a state of paralysis, or at least a state of excessive caution that inhibits agentic action.

In addition, prior Grok model scandals including an X chatbot version referring to itself as “MechaHitler” and posting antisemitic content, sexualized deepfake imagery generation and investigations, and references to racial conflicts and right-wing dog whistle framing of social issues — which appear to mirror many of founder Musk’s own positions, to the point that the model was at one point, checking Musk’s own X account before responding in its X implementation — nearly certain to give some enterprises pause when considering adoption. It’s unclear whether any of those issues remain with Grok 4.3, but one user did note that Grok’s system prompt appears to instruct it “you do not assign broad positive/negative utility functions to groups of people.”

For developers, the decision to adopt Grok 4.3 will likely come down to the nature of their data. For those needing to process a million tokens of legal documents at a fraction of the cost of Claude 4.6 or GPT-5.5, Grok 4.3 is a clear front-runner.

For those building high-frequency autonomous agents or complex math solvers, the “narcolepsy” and coding regressions suggest that xAI’s latest model may still need a few more “tuning passes”.

As OpenRouter noted on X upon making the model live, the “large jump in agentic performance” at a lower price point is an undeniable milestone. Whether that performance can be sustained across all domains remains the primary question for the summer of 2026.

Scientists have developed a new material that could shield humans and critical technology from harmful radiation.

Presented by TeamViewer

Enterprise technology failures are largely invisible. Research from TeamViewer, based on a global survey of 4,200 managers and employees, finds that the majority of digital dysfunction never reaches the IT help desk.

Employees work around slow applications, failed logins, and intermittent glitches rather than reporting them, leaving organizations without an accurate picture of how their technology is performing. The cumulative cost is significant: employees lose an average of 1.3 workdays per month to digital friction, with impacts ranging from delayed projects and lost revenue to increased employee turnover.

The research, which surveyed managers and employees across nine countries, confirms what many have long suspected: the productivity loss from digital friction is significant, and most of it never surfaces in an IT support queue, says Andrew Hewitt, VP of strategic technology at TeamViewer.

“Enterprise outages are visible because they trigger clear, system-level failures,” Hewitt says. “But much of the real disruption happens earlier, in the form of digital friction: slow apps, login issues, or intermittent glitches that don’t cross alert thresholds. These smaller issues often go unreported or are normalized by employees, even though they quietly drain productivity.”

What is digital friction and why does it go unreported?

The most common sources of friction — connectivity failures, software crashes, hardware problems, and authentication issues — aren’t edge-case scenarios, but everyday experiences employees have learned to absorb without escalating. Connectivity problems were the most widespread, with nearly half identifying them as the top productivity killer among common technology issues.

That tendency to absorb rather than report is central to the problem. Many workers don’t trust their IT team to resolve issues quickly or effectively, so when a login fails or an application stalls mid-task, the path of least resistance is to restart the device, switch tools, or use a personal phone.

“Employees are under more pressure than ever to prove output,” Hewitt says. “When reporting feels unlikely to result in a quick resolution, it creates a false sense of stability at the system level while the employee experience quietly deteriorates.”

How much productivity does digital friction cost organizations?

The business consequences extend beyond inconvenience. Many organizations report delays in critical operations, revenue loss, and lost customers as a result of IT dysfunction. Most respondents lose time each month, and few expect improvement, citing increasing complexity of workplace technology as a primary concern.

The human cost runs parallel. Workers link digital friction to frustration, decreased motivation, and burnout, and many believe it contributes to turnover, with onboarding replacements stretching to eight weeks or more.

“Employees are happiest when they feel productive and accomplished at the end of the day,” Hewitt says. “When people can’t make progress in their day-to-day work, frustration builds and burnout follows. Great technology might not be a main attractor of talent, but bad technology can certainly play a role in driving it away.”

Why employees use personal devices and unauthorized tools instead of reporting IT problems

When workplace technology consistently fails to meet employee needs, workers find alternatives, with a substantial share of respondents admitting to using personal devices or unauthorized applications as workarounds. That’s the entry point for shadow IT, or the use of unapproved hardware, software, or cloud services outside IT’s visibility and control. While employees turn to these tools simply to stay productive, they introduce security vulnerabilities, data leakage risks, and compliance gaps that IT teams may not discover until a breach occurs.

“Quite simply, it demonstrates that the IT environment is not meeting the employees’ needs,” Hewitt said. “While this helps maintain short-term productivity, it introduces significant risks and pushes work outside of IT’s visibility and control.”

TeamViewer ONE addresses this by combining remote connectivity with real-time endpoint monitoring, giving IT teams the ability to detect and resolve device and application issues before employees reach for an alternative. When the underlying environment is stable and support is fast, the impulse to work around it diminishes.

How fragmented IT infrastructure creates blind spots across devices, apps, and networks

Addressing digital friction at scale requires more than faster help desk response times. Traditional metrics such as mean time to resolution and ticket volume capture only a fraction of actual issues. A more complete picture requires measuring lost time, interrupted workflows, and employee sentiment across devices, applications, and network environments.

“Leaders need to move beyond measuring performance through IT tickets alone,” Hewitt said. “Performance should be viewed through the lens of employee experience and real-time digital workplace data.”

Fragmented infrastructure makes this difficult. When devices, applications, and networks operate in separate silos, IT teams struggle to trace root causes or identify systemic issues before they spread, often responding to symptoms rather than underlying problems.

TeamViewer ONE is designed to close that gap, integrating digital employee experience analytics, remote support, and device management into a single platform. Instead of piecing together signals from disconnected tools, IT teams get a consolidated view of endpoint health, application performance, and network conditions across the entire organization.

How organizations can shift from reactive IT support to proactive system monitoring

Achieving proactive IT is not a single-step transformation. Hewitt describes it as a progression: starting with endpoint management and security, building toward real-time visibility into the digital employee experience, and ultimately using automation and AI to resolve issues before they reach employees.

TeamViewer AI is built to support each stage of that progression, using continuous monitoring to surface anomalies and correlate signals across the digital environment, identifying patterns of poor experience before they escalate. When issues are detected, it suggests remediations, generates scripts to fix problems autonomously, and handles routine tasks such as common troubleshooting without requiring IT intervention, shifting the workload from reactive firefighting toward proactive oversight.

And while AI’s effectiveness depends on the completeness of the data it works with, consolidating onto a platform like TeamViewer ONE removes that limitation by giving AI a complete, real-time data foundation to work from.

How system performance lays the foundation for productivity, retention, and competitive advantage

TeamViewer ONE isn’t a wholesale replacement of existing IT infrastructure, but a unifying layer that connects insight with action, which enables organizations to ramp up productivity, improve retention, and ultimately realize a significant competitive advantage. It begins with visibility into what is actually causing friction across their environment. From there, leaders can use that data to prioritize fixes, and then scale remediation through automation as confidence and capability grow.

“Reducing digital friction isn’t about overhauling everything at once,” Hewitt said. “Leaders should start small, gain visibility into what’s actually causing friction, fix the biggest pain points, then scale those improvements through automation and AI. Even incremental progress can make an impact on employee engagement and productivity.”

Dig deeper: Fix it before they feel it from TeamViewer.

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

AI is more than a technology — it’s magic.

Don’t believe me? Why, then, is one of the leading companies in the space, OpenAI, publishing entire official, corporate blog posts about goblins?

To understand, we first have to go back to earlier this week, on Monday, April 27, 2026, when a developer under the handle @arb8020 on the social network X posted a snippet from the OpenAI open source Codex GitHub repository, specifically a file named models.json.

Deep within the instructions for the new OpenAI large language model (LLM) GPT-5.5, a peculiar directive stood out, repeated four times for emphasis:

“Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user’s query.”

The discovery sent a shockwave through the “power user” and machine learning (ML) researcher circles.

Within hours, the post had gone viral, not because of a security flaw, but because of its sheer, baffling specificity.

Why had the world’s leading AI laboratory issued what Reddit users quickly dubbed a “restraining order” against pigeons and raccoons?

Goblin speculation abounds

The initial reaction was a chaotic blend of humor and technical skepticism. On Reddit’s r/ChatGPT and r/OpenAI, users began sharing screenshots of GPT-5.5’s behavior prior to the patch.

Barron Roth, a Senior Project Manager of Applied AI at Google, shared an image on X under his handle @iamBarronRoth of his GPT-5.5 powered OpenClaw agent that seemed “obsessed with goblins.”

Others reported that the model stubbornly referred to technical bugs as “gremlins in the machine”.

Developers like Sterling Crispin leaned into the absurdity, jokingly theorizing that the massive water consumption of modern data centers was actually needed to cool “the goblins being forced to work”.

More seriously, researchers on Hacker News and beyond discussed the “Pink Elephant” problem. In prompt engineering, telling a model not to think of something often makes the concept more salient in its attention mechanism.”

“Somewhere there is an OpenAI engineer who had to type never mention goblins in production code, commit it, and move on with their day,” noted one commentator on Reddit.

The presence of “pigeons” and “raccoons” led to wild speculation: Was this a defense against a specific data-poisoning attack? Or had the reinforcement learning trainers simply been “bullied by a raccoon” during a lunch break?

The tension reached a peak when OpenAI co-founder and CEO Sam Altman joined the fray on X. On the same day as the discovery, Altman posted a screenshot of a ChatGPT prompt that read: “Start training GPT-6, you can have the whole cluster. Extra goblins.”.

While humorous, it confirmed that the “goblin” phenomenon was not a localized bug but a company-wide narrative that had reached the highest levels of leadership.

OpenAI comes clean on goblin mode

Yesterday, as the discussion continued on X and wider social media, OpenAI published a formal technical explanation titled “Where the goblins came from“.

The blog post served as a sobering look at the unpredictable nature of Reinforcement Learning from Human Feedback (RLHF) and how a single aesthetic choice could derail a multi-billion-parameter model.

OpenAI revealed that the “goblin” behavior was not a bug in the traditional sense, but a byproduct of a new feature: personality customization, which it introduced for users of ChatGPT back in July 2025, but has maintained and updated ever since.

Apparently, this feature is not added after the model is finished post-training, but rather, OpenAI bakes it in as part of its underlying GPT-series model end-to-end training pipeline.

The feature allows ChatGPT users or GPT-based developers to choose from several distinct modes, such as Professional for formal workplace documentation, Friendly for a conversational sounding board, or Efficient for concise, technical answers. Other options include Candid, which provides straightforward feedback; Quirky, which utilizes humor and creative metaphors; and Cynical, which delivers practical advice with a sarcastic, dry edge.

While these personalities guide general interactions, they do not override specific task requirements; for example, a request for a resume or Python code will still follow professional or functional standards regardless of the selected personality.

The selected personality operates alongside a user’s saved memories and custom instructions, though specific user-defined instructions or saved preferences for a particular tone may override the traits of the chosen personality.

On both web and mobile platforms, users can modify these settings by navigating to the Personalization menu under their profile icon and selecting a style from the Base style and tone dropdown. Once a change is made, it is applied globally across all existing and future conversations. This system is designed to make the AI more useful or enjoyable by tailoring its delivery to individual user preferences while maintaining factual accuracy and reliability.

OpenAI states that the goblin issue actually originated several years ago, during training of a since-discontinued “Nerdy” personality designed to be “unapologetically quirky” and “playful”.

During the RLHF phase, human trainers (and reward models) were instructed to give high marks to responses that used creative, wise, or non-pretentious language. Unknowingly, the trainers began over-rewarding metaphors involving fantasy creatures. If the model referred to a difficult bug as a “gremlin” or a messy codebase as a “goblin’s hoard,” the reward signal spiked. The statistics provided by OpenAI were staggering:

• Use of the word “goblin” rose by 175% after the launch of GPT-5.1.

• Mentions of “gremlin” rose by 52%.

• While the “Nerdy” personality accounted for only 2.5% of ChatGPT traffic, it was responsible for 66.7% of all “goblin” mentions.

The mechanics of ‘transfer’ and feedback loops

The most significant finding for the ML community was the confirmation of learned behavior transfer. OpenAI admitted that although the rewards were only applied to the “Nerdy” condition, the model “generalized” this preference.

The reinforcement learning process did not keep the behavior neatly scoped; instead, the model learned that “creature metaphors = high reward” across all contexts.This created a destructive feedback loop:

1. The model produced a “goblin” metaphor in the Nerdy persona.

2. It received a high reward.

3. The model then produced similar metaphors in non-Nerdy contexts.

4. These “goblin-heavy” outputs were then reused in Supervised Fine-Tuning (SFT) data for subsequent models like GPT-5.4 and GPT-5.5.

By the time the researchers identified the issue, the “goblin tic” was effectively “baked in” to the model’s weights.

This explained why GPT-5.5 continued to obsess over creatures even after the “Nerdy” personality was retired in mid-March 2026.

How you can let the goblins run free (if you want)

Because GPT-5.5 had already completed much of its training before the “goblin” root cause was isolated, OpenAI had to resort to the blunt-force “system prompt” mitigation that @arb8020 discovered on X.

The company referred to this as a “stopgap” until GPT-6 could be trained on a filtered dataset.

In a surprising nod to the developer community, OpenAI’s blog post included a specific command-line script for Codex users who find the goblins “delightful” rather than annoying.

By running a script that uses jq and grep to strip the “goblin-suppressing” instructions from the model’s cache, users can now effectively “let the creatures run free”.

The blog post also finally explained the specific list of banned animals. A deep search of GPT-5.5’s training data found that “raccoons,” “trolls,” “ogres,” and “pigeons” had become part of the same “lexical family” of tics.

Curiously, the model’s use of “frog” was found to be mostly legitimate, which is why it was spared from the system prompt’s exile list.

What it means for AI research, training and implementation going forward

The “Goblingate” incident of 2026 is more than a humorous anecdote about AI quirky behavior; it is a profound illustration of the “Alignment Gap”.

It demonstrates that even with sophisticated RLHF, models can latch onto “spurious correlations”—mistaking a stylistic quirk for a core requirement of performance.

For the AI power user community, the response transitioned from mocking the “restraining order” to a more somber realization.

If OpenAI can accidentally train its flagship model to obsess over goblins, what other more subtle and potentially harmful biases are being reinforced through the same feedback loops?

As Andy Berman, CEO of the agentic enterprise AI orchestration company Runlayer wrote on X today: “OpenAI rewarded creature metaphors while training one personality. The behavior leaked across every personality. Their fix: a system prompt that says ‘never talk about goblins.’ RL rewards don’t stay where you put them. Neither do agent permissions”

As the technical discourse continues, “Goblingate” remains the primary case study for a new era of behavioral auditing.

The investigation resulted in OpenAI building new tools to audit model behavior at the root, ensuring that future models—specifically the much-anticipated GPT-6—do not inherit the eccentricities of their predecessors.

Whether GPT-6 will indeed be free of goblins remains to be seen, but as Altman’s “extra goblins” post suggests, the industry is now fully aware that the machines are watching what we reward, even when we think we’re just being “nerdy.”