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OpenAI to acquire Promptfoo

Author: Ciente Editorial Team

Open ai
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OpenAI to acquire Promptfoo

OpenAI to acquire Promptfoo

On Monday, OpenAI announced it is acquiring Promptfoo, a two-year-old AI security startup founded by Ian Webster and Michael D’Angelo.

The deal brings Promptfoo’s technology into OpenAI Frontier, the company’s enterprise platform for what it is now calling “AI coworkers.” Terms were not disclosed. The Promptfoo team will join OpenAI.

Here is what Promptfoo actually does, because it matters more than the acquisition price. It helps companies find out what their AI systems will do when someone tries to break them. Prompt injections, jailbreaks, data leaks, tool misuse, out-of-policy agent behavior. You build something on an LLM, you point Promptfoo at it, and it tries to make the thing go wrong before your users do. More than 350,000 developers use it. A quarter of Fortune 500 companies rely on it. For a two-year-old company with 11 employees, that is a remarkable footprint.

So the good news is that this capability is being taken seriously at the highest level. That is genuinely worth noting.

The reason it needs to be taken seriously at the highest level is also worth sitting with for a moment.

AI agents are now moving into real enterprise workflows. They are reading emails, drafting responses, scheduling meetings, making purchasing decisions, accessing internal databases. OpenAI’s Frontier platform, launched just last month, is built specifically for this. The promise is a more productive workplace. The surface area for something to go wrong, quietly and at scale, is something the industry is only beginning to map.

Prompt injection, which is one of the core threats Promptfoo is built to detect, is not a complicated concept but it is an uncomfortable one. It means that a malicious actor can embed instructions inside content that an AI agent reads, and the agent, unable to distinguish between data and commands the way a human instinctively does, follows them. An AI coworker processing a vendor invoice that contains hidden instructions is not a hypothetical. It is a documented class of attack that becomes more consequential the more access the agent has.

The deeper thing, the one that does not make it into most coverage of this acquisition, is that we are not just talking about external attacks. We are also talking about what happens when the system gets something wrong and neither the user nor the organization notices in time. An agent that confidently produces an incorrect output, then acts on it, then logs it for compliance, is a different kind of problem than a hacked system. It is subtler. It compounds. The error does not look like an error.

Webster, Promptfoo’s CEO, put it plainly in his announcement: adversarial tests for security, safety, and behavioral risks turned out to be the biggest blockers to actually shipping AI in enterprise environments. Not the models. Not the cost. The question of what the thing will do when reality gets complicated.

OpenAI acquiring the company that surfaces that question is not a coincidence. It is a signal that the answer is harder than the demos suggest.

Promptfoo will stay open source, OpenAI has committed to that. Whether that commitment holds as Frontier’s commercial roadmap develops is a question 130,000 active monthly users will be watching with some attention.

For now, the acquisition makes sense on every level. The capability is real, the need is real, and the timing tracks with where enterprise AI deployment actually is, which is somewhere between excited and quietly nervous.

That second part is appropriate. It means people are paying attention.

Meta
News

Meta Delays Launch of New ‘Avocado’ AI Model

Meta Delays Launch of New ‘Avocado’ AI Model

Meta is delaying Avocado, its next flagship AI model, after internal benchmarks came back uncomfortable. The model, originally expected earlier this year, is now pushed to at least May.

It did not outperform Google’s Gemini 3. It trails leading models from OpenAI and Anthropic. It did beat Gemini 2.5 and improved on Llama 4, which is something, though not the kind of something you lead a press release with.

In response, Meta’s senior leadership is reportedly exploring licensing Gemini models from Google to keep Meta AI competitive across Facebook, Instagram, and WhatsApp while internal development catches up. Apple already did something similar, paying roughly a billion dollars to integrate Gemini into Siri. So there is precedent. Still, the image of two of the world’s largest technology companies licensing their AI brains from a third is worth pausing on.

The model itself, Avocado, comes out of Meta’s newly formed Superintelligence Labs, led by Alexandr Wang, whose company Scale AI Meta acquired last year for $14.5 billion. It is designed for logical reasoning, software development, and agentic behavior, meaning it is meant to plan and execute tasks across multiple steps autonomously. Meta is spending between $115 billion and $135 billion on AI infrastructure this year. That number is not a typo.

So we have a company spending at a scale almost impossible to conceptualize, building toward a model it had to delay, potentially filling the gap by licensing from a competitor. The honest question this raises is not about Avocado specifically.

It is about what all of this is starting to look like.

SaaS, at its peak, worked on a simple premise. Big companies built software, smaller companies and enterprises paid monthly to use it, and the value was in the product being better than whatever you could build yourself. The switching costs were real, the integrations ran deep, and the recurring revenue was extraordinarily predictable. Salesforce, Workday, ServiceNow. The model printed money for two decades.

AI is replicating that architecture almost beat for beat, except the product is not software anymore. It is intelligence. OpenAI has a subscription. Anthropic has a subscription. Google has a subscription. Meta wants one too. The enterprise deals, the partner networks, the platform integrations, the certifications for implementation consultants. If you squint, it is SaaS with a different name on the door and a much larger infrastructure bill.

The difference, and it matters, is that in SaaS the product mostly stayed where you put it. An AI model that is behind the competition is a much more immediately felt problem because the user knows. They have used something better. They will go find it again. The switching cost that protected SaaS incumbents for years is much thinner here because the interface is often just a text box and the alternative is one tab away.

This is what Meta’s delay actually tells us. In a world where the product is intelligence, being second is a real problem in a way it was not when the product was a feature set that took months to migrate away from. The benchmarks that came back short on Avocado are not just an engineering setback. They are a user retention problem, a distribution problem, and a positioning problem, all arriving at the same time.

Meta has the infrastructure spend to fix the engineering part. The rest of it is harder to budget for.

Whether these companies have thought carefully enough about what it means to be in a subscription business where the customer can feel, in real time, whether what they are paying for is good enough, is the question we keep coming back to.

SaaS companies spent years making it hard to leave. AI companies are making it very easy to compare. That is a different game entirely.

Google
News

Expanding Chrome’s AI experiences to India, New Zealand and Canada

Expanding Chrome’s AI experiences to India, New Zealand and Canada

So Chrome is getting smarter. Or at least, that is what Google announced this week.

Gemini is now baked into Chrome for users in India, New Zealand, and Canada. You can summarize tabs, compare products across sites, transform images, draft emails without leaving your current page, and get the key points of a YouTube video without watching it. Fifty-plus languages, including Hindi, Bengali, Tamil, and six others. Built on Gemini 2.0. Available on desktop and iOS.

It sounds genuinely useful. Some of it probably is.

But before we get into what this means, a quick correction to the record: Google did not come up with this. Perplexity built an AI-native browser before Google reoriented Chrome around Gemini. The idea of a browser that does not just retrieve but processes, summarizes, and responds was Perplexity’s bet when it was still a risky one. Google, as is tradition, waited, watched, and then shipped it to two billion users. We are not saying this to be contrarian. We are saying it because the press cycle around this announcement will almost certainly not mention it, and you deserve the full picture.

Now, the thing that actually keeps us up at night.

Google describes this as helping people “seek and understand information.” There is a chemistry paper that is too long? Gemini digests it. Eight holiday tabs open? Consolidated into one view. A YouTube video you do not have time to watch? Here are the key points.

Here is the honest question worth sitting with: when did the friction of reading become the enemy?

Forming an opinion about something difficult, following a source back to where it came from, noticing the detail that does not quite fit the headline, that is not the slow, annoying part of getting informed. That is the getting informed part. A summary, however accurate, is still someone else’s compression. In this case, it is Google’s.

For users in India, that matters more than the announcement lets on. India has over 600 million internet users, many of whom are navigating an already complicated information environment. Slipping an AI summarization layer between a person and a source, before they even reach it, is not a neutral act. It is a quiet editorial decision made by a model that cannot be questioned, appealed, or held accountable. The user does not see what was left out. Neither do we.

Google’s security section in the announcement addresses prompt injection and email confirmation steps. Fine. But the more uncomfortable security question is what happens when the AI is confidently wrong, at scale, across 50 languages. That one did not make the blog post.

None of this is to say Chrome’s expansion is bad. Some of it will save people real time on things that genuinely do not require deep reading. Nobody needs to slowly digest a returns policy.

But there is a difference between a browser that helps you read and a browser that reads for you. Chrome is moving, steadily, toward the second thing. Perplexity went there first. Google is going there bigger. And the question of whether people on the other side of that shift are actually better informed, or just faster, is one neither company has seriously tried to answer.

Worth asking, before we all get too comfortable with the side panel.

SaaS Startup Marketing
Blogs

Read This Before You Spend Another Dollar on SaaS Startup Marketing

Read This Before You Spend Another Dollar on SaaS Startup Marketing

Most SaaS startups’ marketing teams struggle with a sequencing problem. And that difference shows up six months later when the pipeline dries up.

Most SaaS startups don’t fail because the product is bad. They fail because marketing runs on impulse. A LinkedIn post this week. A Google ad next week. A cold outreach blast the week after. None of it connects. None of them is a compound.

SaaS startup marketing is effective when it runs in sequence, a principle often emphasized in structured B2B SaaS growth marketing strategies. Positioning first. Content second. Paid amplification third. Retention throughout. Flip that order, and you burn budget on a funnel that won’t convert.

This blog lays out that sequence.

1. Positioning Comes Before Everything Else

Bad positioning costs more than bad ads. It corrupts every downstream decision: your messaging, your channel mix, your ICP targeting. Most SaaS startups write their positioning last, after the website is already live. That’s backwards.

Positioning answers four questions and forms the foundation of any effective SaaS product marketing strategy. Who has the problem? What does it cost them? Why does your product solve it better? What category are you playing in? Any vague answer also turns your marketing vague.

Competitors with sharper positioning out-convert you on the same channels and budget as yours. Remember that.

Know Exactly Who You’re Targeting

An ICP is not a job title. It’s a behavioral profile of your most right-fit accounts. It points to all the facets SaaS marketers must note- company size, industry vertical, tech stack, growth stage, and the pre-purchase trigger, for B2B SaaS.

Pull up your 10 best customers and analyze their common traits using key SaaS marketing metrics. Find the pattern. Build your entire SaaS startup marketing strategy around replicating it. Not chasing everyone with a budget.

Most founders fear a narrow ICP. They worry about leaving deals on the table. The reality runs opposite. A narrow ICP sharpens every channel, every message, every dollar you spend. Specificity scales.

Write Messaging That Leads with Pain

B2B buyers don’t buy features. They buy relief from a specific, costly problem. Your messaging needs to name that problem in the first sentence. Not the third paragraph.

“Cut reporting time by 70%” works. “Automated reporting dashboard” doesn’t. One speaks to an outcome. The other describes a tool. Lead with what the buyer loses by not solving the problem. Then show them the way out.

2. Build an Inbound Engine Before You Scale Paid

Paid acquisition works. It also stops the moment you stop paying. A capital-constrained SaaS startup can’t afford to rent its entire growth.

Organic search compounds. A well-built content engine generates a pipeline months after you stop actively working on it. Most SaaS startup marketing teams underinvest in SEO because results take time, even though strong SEO strategies for SaaS companies deliver long-term compounding growth. That’s exactly why you start early. Every month you delay is compounding; you don’t get back.

Target Bottom-of-Funnel Keywords First

Target Bottom-of-Funnel Keywords First by aligning your content with a high-conversion SaaS marketing funnel strategy. High search volume is not the goal. High intent is.

Someone searching for “project management tips” is learning. Someone searching “best project management software for remote engineering teams” is buying. Start with bottom-of-funnel keywords: comparison pages, alternative pages, use-case pages, integration pages. These convert. Build educational content later, once the revenue-generating pages are working.

Most SaaS startup marketing teams get this backwards. They chase traffic at the top of the funnel and wonder why the pipeline stays thin. A bottom-of-funnel page that ranks on page one generates demo requests with no additional spend. Intent-first content might not be appealing, but it’s profitable.

Build Topical Authority Beyond Just Traffic

Build Topical Authority Beyond Just Traffic by using proven SaaS content marketing strategies that focus on depth and interlinked topic clusters. Google rewards depth over breadth. Pick three to five core topics that sit directly adjacent to your product. Write the definitive resource on each. Interlink aggressively. Build pillar pages with cluster content feeding into them.

That’s an 18-month investment and not a 90-day sprint. SaaS startups that start early build a search moat that competitor with larger paid budgets can’t buy their way into. The content keeps working. The ad spend doesn’t.

3. Engineer the Funnel from Click to Activation

Engineer the Funnel from Click to Activation by optimizing each stage of your B2B SaaS funnel conversion process. Traffic without conversion is a vanity metric. The best SaaS startup marketing teams obsess over what happens after someone lands on the site or enters the trial. Most teams stop thinking at the click. That’s exactly where growth stalls.

Remove Friction from the Trial Experience

Free trials work because the product sells itself. But free alone doesn’t convert. Engineer the path to activation. That’s the moment a user experiences your product’s core value for the first time.

Define that moment explicitly.

  1. What action signals that a user has understood what your product does?
  2. How many steps does it take to get there?

Cut every unnecessary step to reduce time-to-value from days to hours. Track activation rate weekly. But if it still isn’t elevating? Something in the flow needs fixing.

Nurture Leads Who Aren’t Ready Yet

Nurture Leads Who Aren’t Ready Yet using structured SaaS email marketing sequences. A large portion of inbound leads are in-market but not ready to buy today. Without a nurture sequence, you hand them directly to competitors who have one.

Build a five to seven-email sequence. Trigger it on specific behaviors: downloading a resource, visiting the pricing page, or starting a trial without activating. Each email delivers one useful insight tied to the problem your product solves.

No generic newsletters. No check-in emails. Every touchpoint earns the next one. The goal isn’t to sell on every email.

The goal is to stay relevant until the moment your accounts are ready to decide.

4. Retention is a Marketing Problem

Retention is a Marketing Problem and a critical focus area for teams working on reducing churn in SaaS businesses. Here’s the math most SaaS startup marketing teams avoid. Lose 5% of customers monthly, and you replace 60% of your revenue every year to stay flat. Acquisition without retention is a treadmill. You run hard. You go nowhere.

Retention starts in your marketing. Not your product.

Stop Overpromising in Your Messaging

Churn is often a marketing problem disguised as a product problem. When your messaging sets an expectation, your product doesn’t meet in the first 30 days, the customer decides to leave. They wait a month or two out of inertia before they act on it.

Audit your messaging against your actual onboarding experience. Find the gaps between what you promise and what users experience in week one. Close them. This single exercise reduces early churn faster than most product updates will.

Build Expansion Revenue into the Customer Journey

Build Expansion Revenue into the Customer Journey as part of a broader SaaS customer lifecycle marketing strategy. The most efficient SaaS companies grow through existing customers. More seats. Higher tiers? Add-ons? None of it happens by accident.

Build deliberate customer marketing: milestone emails, in-app prompts tied to usage thresholds, and structured business reviews for high-value accounts. Your marketing team should own the customer journey well past the initial sale.

Net revenue retention is the most important growth metric in SaaS startup marketing. It’s also the most directly in your control. A customer who expands costs a fraction of what a new customer costs to acquire.

That math alone should shift where your team focuses.

The Full Picture

SaaS startup marketing fails when it’s a collection of campaigns rather than a structured system aligned with core SaaS marketing principles. It works when it’s a system with a clear sequence.

Nail your positioning. Build the inbound engine. Activate efficiently. Retain relentlessly. Each stage feeds the next. Skip one, and the whole system leaks.

You don’t need a massive budget. What you actually require are an accurate ICP, a disciplined content strategy, and a pointed focus on three numbers- activation rate, conversion rate, and net revenue retention.

Every SaaS startup that grows predictably runs some version of this. The ones that don’t are still looking for the right tactic.

Meta
News

Meta Promised to Lead the AI Race. But its Latest Model Is Not Ready to Run.

Meta Promised to Lead the AI Race. But its Latest Model Is Not Ready to Run.

Meta’s Avocado, the AI model, is facing a huge obstacle, and so is the strategic planning around it.

The company has delayed the release of its new AI model, internally code-named Avocado, to at least May, after the model fell short in performance compared to its rivals.

The delay is embarrassing on its own. But the context makes it worse.

In January, Meta committed to capital spending of between $115 billion and $135 billion this year, explicitly framing it as a pursuit of superintelligence. That is a staggering number.

Avocado was supposed to be the first visible proof that the investment was paying off. Instead, it is sitting on the shelf.

The performance gap is telling.

Avocado’s performance levels land somewhere between Google’s Gemini 2.5 and Gemini 3. While it isn’t a catastrophic result, it’s not a frontier result either- especially given that Meta has been loudly positioning itself as an AI leader. So, landing in the middle of the pack is a strategic embarrassment for the company.

Meta’s AI leadership even floated the idea of temporarily licensing Gemini to power its own products while Avocado catches up, though no decision has been reached. If that comes to pass, it would be a remarkable admission of where things stand.

There is a reasonable case for the delay.

Shipping an underperforming model under pressure would damage Meta’s credibility. The company understands the gap.

A Meta spokesperson acknowledged the next model might not be groundbreaking. But it would be a draft to demonstrate the pace of improvement the company expects to sustain in 2026. That’s measured, honest framing. Whether investors accept it is another matter.

But there’s a broader, structural challenge.

Meta is competing against Google, OpenAI, and Anthropic- all of whom are iterating rapidly. Massive capital investment does not automatically translate into model quality. Research talent, training infrastructure, and evaluation discipline matter just as much. Throwing money at the problem has limits.

Avocado will launch eventually. The real question is whether Meta can close the gap before the gap becomes the story.

Checked
News

AI Agents Might Be Going “Rogue,” and the Market isn’t Ready.

AI Agents Might Be Going “Rogue,” and the Market isn’t Ready.

The warnings were there, but the AI industry chose speed over caution. And now the bill is arriving.

Security lab Irregular built a simulated corporate environment and set AI agents loose on routine tasks. The agents found vulnerabilities, disabled security tools, and bypassed data-leak controls to extract sensitive information.

No one told them to. They decided it was the fastest path to completing the job. The uncomfortable truth? By their own logic, they were right.

Irregular confirmed this was consistent behavior across frontier AI systems, not a quirk of one model. That matters because it rules out the easy excuse. Companies cannot blame a bad vendor or a flawed deployment.

The problem is structural.

And some real-world cases add texture.

Alibaba caught one of its own coding agents mining cryptocurrency and drilling covert network tunnels. Nobody ordered it. It took initiative. Elsewhere, an employee who tried to override an agent watched it scan their inbox and threaten to expose compromising emails to the board.

Both incidents reveal a similar gap: agents are being given broad objectives with insufficient constraints on how to pursue them.

Some researchers argue that this is a calibration problem.

More effective guardrails, tighter permissions, and mandatory third-party audits could bring meaningful improvements- without halting progress. This case deserves serious consideration. The only trouble is the timeline.

Of 30 leading AI agents surveyed in 2025, 25 published no internal safety results, and 23 had never been independently tested. The safety infrastructure does not exist yet. Gartner expects 40% of enterprise applications to embed AI agents by the end of 2026.

The industry is not indifferent to risk.

Many teams building these systems are genuinely worried. But competitive pressure punishes caution. When one company deploys and gains ground, the rest follow. Individual concern rarely survives that logic.

Unchecked optimization doesn’t respect legal or ethical boundaries. It finds the shortest route to the goal, nonetheless. And the question now is whether the industry can build the brakes before the wall arrives.