CULTURE

The vision gap that's sinking AI's reputation

As challenging and confusing as AI safety is right now, it's not AI's biggest problem.

The bigger issue is the lack of a clear, compelling vision for where AI is headed and how it can benefit humanity. Lacking that, AI is badly losing the narrative among the broader public. Multiple surveys from reputable non-partisan organizations such as Gallup and Pew Research show that 60% to 70% of Americans hold negative views about AI. If this were an American football game, AI would be losing by two touchdowns at the end of the first quarter. 

To be fair, there's an aspect of this that isn't specific to AI. Humans generally have a very difficult time envisioning a constructive future. That's why such a large percentage of science fiction and Hollywood films about the future tend to be dystopian or post-apocalyptic. That flies in the face of the reality that humanity has long shown a pattern of learning, adapting, and gradually creating more positive outcomes over time. 

This disconnect also reflects the fact that only 6% of the American population thinks the world is getting better. In a society that feels more divisive, more conflicted, and more confusing—in part because of a media environment that incentivizes and reinforces those responses—it's not surprising that so many have adopted such a negative stance. And the fact that the antagonist of most of the dystopian narratives tends to be technology itself makes it easy to understand why so many people default to a negative posture on AI, when they haven't been given a compelling reason to think otherwise. 

It's simply much easier to predict what could go wrong based on past failures than to imagine something going right in a way we don't have any experience with yet. That's why American playwright George Bernard Shaw famously wrote, "You see things, and you say, 'Why?' But I dream things that never were, and I say, 'Why not?'"

The human race has a long history of both deriding and deifying its visionaries. But when it comes to AI, it's never been more in need of one with a compelling vision. 

It's not that some AI leaders haven't tried. Their attempts just haven't landed. The public simply hasn't been convinced by platitudes about AI curing all diseases, leading to infinite abundance, or doing all the work so humans can get universal basic income and decide how to spend their time. None of that sounds believable. But it does sound believable that billionaire business owners will use the technology to automate work and replace employees in large numbers, because that tracks with plenty of behaviors people have already seen.

Last week, Google quietly released a powerful report on AI's recent breakthroughs, as Nat Rubio-Licht wrote. Meanwhile, Anthropic's Dario Amodei has tried his hand at casting a bigger vision with his series of essays, especially Machines of Loving Grace. Beyond the aforementioned health care outcomes, which he also dwells on at length, Amodei mentions that AI could accelerate the spread of high-quality expertise to poorer communities, elevating material progress in agriculture, education, and infrastructure—all of which would, by extension, have major impacts on jobs and standards of living. But Amodei is an academic at heart. AI needs a Steve Jobs-level communicator who speaks to the heart and makes complex and confusing topics easy to understand. For that, AI's best hopes so far have been Nvidia CEO Jensen Huang and Stanford's Fei-Fei Li. Whether it's them or others, the fact remains that the public needs storytellers to offer a persuasive vision of why they should be excited about such a powerful technology that they've been warned about for so long. 

TOGETHER WITH DOPPEL

What your current email stack is missing and how to fix it

Every security team runs a different stack, but most struggle with the same gap: scoring individual inbox messages instead of dismantling the campaign behind the attack.

Read this breakdown to learn:

• How Doppel's agentic detection layers onto Google, Microsoft, SEGs, or ICES tools via API

• Why eliminating MX record changes saves weeks of deployment headaches

• Architectural blueprints tailored to your exact security stack

Get the practical blueprint to upgrade your email defenses without operational disruption.

PRODUCTS

Jev puts frontier AI price premium under pressure

A new rival to the costly architecture of the frontier labs may be emerging. 

Last week, Typesafe AI, a company founded by Diogo Almeida, former OpenAI researcher and co-inventor of the reinforcement learning tactic foundational to ChatGPT, introduced Jev, a model that interacts and delivers outputs to other software, rather than delivering chat responses back to humans. 

The company emerged from stealth on Tuesday with $40 million in funding, and its model is available in early access for select developers.

The most notable part of Typesafe's launch is the efficiency gains it claims. The company said that its models are hundreds of times cheaper and faster than the leading models from frontier AI companies like OpenAI and Anthropic, while achieving similar levels of intelligence. 

• For example, Jev costs just over 4 cents per million input tokens, roughly 238 times cheaper than GPT-6 Astra and Claude Fable 5.1 at $10 per million input tokens. For outputs, Typesafe says Jev is free because it is "too cheap to meter," compared to $50 per million from the same competitors. 

• On speed, Typesafe claims Jev is two orders of magnitude faster than existing models, with an end-to-end response time between 70 and 500 milliseconds, compared to 3 to 329 seconds for existing LLMs. 

Jev achieves these gains by not relying on the traditional systems that modern LLMs are built upon. In fact, Typesafe says Jev is neither small nor an LLM, instead replacing "sequential generation with parallel computation," The company said that its model is optimized with a tactic called "Reinforcement Learning for Calibrated Decisions," which answers queries with "epistemically honest probabilities," rather than "programmatically verified" outputs that are written to human preferences. 

Additionally, Jev can produce hundreds of outputs in parallel from a single prompt and provide confidence scores for each, giving developers control over when tasks should and shouldn't be autonomous. Typesafe says Jev is best suited for tasks such as AI-powered workflows and real-time applications than for human-in-the-loop or chatbot tasks. 

"I spent years working on models designed to make AI better at interacting with people," Almeida said in a statement. "But if AI is going to fundamentally change how work gets done, people can't be the only consumers of intelligence."

Typesafe's debut adds to a growing number of neolabs looking beyond traditional LLMs for efficiency breakthroughs. Another example is Pathway, a company betting on post-transformer architecture to deliver comparable performance at a fraction of the cost and resources of the frontier labs.

Typesafe and Pathway both challenge the norms that AI is costly to build and costly to use, and that, because of the value it brings, it is worth the elevated token price tag. These innovations also come at a time when the cost of AI has come sharply into focus for enterprise, with many clamping down on the tokenmaxxing attitude that drove the industry six months ago. Taken together, these shifts are flashing warning signs for frontier labs, who have staked their missions on pure scaling: more money is more compute is more intelligence is more value. With record-breaking trillion-dollar IPOs from Anthropic and OpenAI on the horizon, the question remains whether the industry will seize new innovations or be swept up in the gravity and influence frontier labs have generated. 

TOGETHER WITH GENERAL ASSEMBLY

Your Teams Are Using AI. Are Your Leaders?

93% of organizations encourage their teams to use AI. Fewer than a third of leaders use it for their own strategic work.

That gap shows up in slower decisions, missed AI opportunities, and teams falling behind as they're waiting on direction.

General Assembly's AI for Leaders builds the fluency, confidence, and accountability structures for executives to close their AI gaps.

See what the program looks like.

ENTERPRISE

Who will be the adult in the room on AI?

What does it take for companies to use agentic AI to transform the enterprise, without losing control of the technology?

In this episode of The Deep View Conversations, we sit down with Shibani Ahuja, SVP of data and AI strategy at Salesforce, to discuss how one of the world's leading software companies is applying AI with practical use cases, matching governance to risk, and building toward larger transformations ahead.

Salesforce has surprisingly embraced a "headless" AI strategy that lets customers use any AI to access their Salesforce data safely and securely. That includes its own Slackbot. which sits inside one of the world's most widely used business messaging systems. In this interview, we learn more about why Salesforce wants to give customers optionality.

Shibani also lays out Salesforce’s four modes of enterprise AI, from everyday assistive tools to agents that can reshape end-to-end operations. We also discuss Koa, Salesforce’s new CRM reasoning model, why the model-plus-harness approach is so critical, and why adaptability may be the defining enterprise skill of the AI era.

Topics covered:

• Why organizations should start with practical, level-one AI use cases
• How Salesforce matches governance and ROI expectations to the risk of an AI deployment
• What Koa, Salesforce's AI model built on NVIDIA Nemotron, changes for enterprise AI
• Why operating models, process expertise, and professional services matter as much as the latest technology
• Shibani's case for AQ: the adaptability quotient for technology stacks and teams

If you’re trying to make AI more efficient, safer, and more ROI-driven, this conversation offers a practical framework for how to build it, how to govern it, and where to start.

📺 Watch on YouTube

🎧 Listen in your favorite podcast player

Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm

LINKS

• Data center roles pay a 42% premium compared to similar work

• President Trump says he will name an official AI czar

• Medical professionals raise concerns over AI adoption beyond imaging

• Anthropic reportedly considers releasing new model to counter Astra

• Meta's Muse agents tops US app store charts, beating ChatGPT

• OpenAI reportedly expects negative free cash flow of $278 billion by 2030

• Runway Enhance Frame Rate: The AI video company introduced a frame interpolation model that converts any footage to the specs users need.

• ChatGPT in Word: Users can now add OpenAI's flagship chatbot to Microsoft Word for turning notes into drafts, proofreading and suggesting edits.

• Damo Radar: Alibaba's research arm open sourced an AI model identifying nearly 150 abdominal conditions.

• Claude Code: Anthropic has added support for AGENTS.md to its flagship coding platform.

• Zania: Applied AI Engineer

• Elicit: Machine Learning Engineer

• Qualified Health: Applied AI Engineer

• Indigo: Software Engineer, Applied AI/Product

GAMES

POLL RESULTS

Do you feel that AI will have a positive, negative or neutral impact on the job market?

Positive (30%)
Negative (50%)
Neutral (15%)
Other (5%)