Week 44 news

• How the new ChatGPT Search compares to Perplexity and other AI-powered search tools.
• Why the tax hikes in the UK budget might speed up AI adoption.
• What Google’s latest results tell us about growth without workforce expansion.

ChatGPT Search takes on Google

The launch of ChatGPT Search this week has reignited the battle for search supremacy, echoing the dynamics of the 1990s when AltaVista, Yahoo, and others competed for users’ attention. This time, the prize isn’t just about providing links – it’s about delivering direct answers to complex questions.

Google’s response in recent months with its AI-powered summaries reflects the tension at the heart of this shift. Whilst introducing search enhanced by Gemini the company is also trying to balance innovation against protecting its $200+ billion advertising business. Other players face no such constraints. Perplexity has gained 15 million monthly users by focusing on cited, accurate responses, although is toying with implementing advertising. More options: the existing competitor Bing, You.com which provides specialised tools for coding and content creation, and Phind which serves developers with complex answers, complete the current main choices.

We tested them with a complex query on the very recent UK Autumn budget and comparative AI-linked fiscal policy in other countries. Claude analysed the results over several repetitions (as these tools tend to respond differently each query):

• Perplexity: Strong on recent data and specific figures with good citations but did notably fail to address a specific part of the query. Score: 84% (Paid version)
• ChatGPT Search: Showed good judgment by correctly noting absence of specific AI measures in the budget and provided balanced international comparison with specific figures. Score: 70% (Paid version)
• You.com: Attempted comprehensive coverage with clear structure but mixed current/speculative content and included unnecessary images while lacking clear citations. Score: 62% (Free version)
• Phind: Made assumptions about future events and lacked specific data but provided good structural analysis – shows risks of AI search going beyond verified facts. Score: 48% (Free version)
• Bing: Provided a well-structured and detailed analysis with plausible content and figures, but critically undermined its credibility by fabricating future sources and dates. Score: 46% (Paid Copilot version)

We also tested the tools on a slightly less high-brow topic, the most recent Strictly Come Dancing contestant leaderboard. ChatGPT Search won hands-down as it managed to source a web page with detailed results, something the others didn’t find. This highlights one of the main challenges with AI search… if the results found are good, the output tends to be better. But often the links retrieved feel quite random, lacking the relevance that we expect from Google.

One of the main advantages of AI search is being able to continue the conversation. Once the first query is complete these tools allow follow-up questions to be asked which can be incredibly powerful. Perplexity goes further and allows those questions to be laid out in an information page you can re-order and share.

Our final testing looked at this week’s new search or ‘grounding’ capabilities in Google Gemini 1.5 Pro. It seems that this may be useful for specific factual queries but was limited to only a few search results with little control over what they were. Whilst Google still have by far the most powerful web search capability, that is still not integrated into their Gemini products flexibly. At ExoBrain we’ll continue to use third party search to feed our ‘grounded’ AI solutions for the time being.

Takeaways: Different search tools serve different purposes. Despite the launch of ChatGPT Search, Perplexity still feels like the leader, with the ability to focus on academic, social or web source content, the power of o1-preview for advanced analysis or the user’s choice of frontier model, and its publishing tools. The overall ChatGPT experience will benefit from search with clear cited content items that are returned almost instantly. Google has strong models and the world’s best web search, but the innovators dilemma seems to be preventing it from integrating these assets to full effect.

We’d summarise our findings as follows:

• Use web search when you need verified, current facts or specific pages you know are out there – it’s about finding rather than understanding.
• Use AI search when you need to understand broad patterns or relationships across multiple current sources and continue the conversation – it’s about synthesis of current events and dynamic topics rather than 1-off high-precision facts.
• Use non-search AI when you need analysis of established knowledge or creative thinking – it’s about focused thought rather than being distracted by web info which can often be of lower quality than the concepts already in the model.

Taxing times for Labour and labour

In the UK the new Labour government’s first budget in 14 years has been dominating the headlines. Our hot take on LinkedIn this week highlighted how the combination of the largest employer NI increase in history and a minimum wage hike could create compelling incentives for AI automation, even as it raises concerns about implementation challenges.

Market reaction has been negative, with gilt yields climbing and the FTSE 100 declining by 2.5% over three days. As the cost of borrowing looks like it may increase for everyone, many technology leaders have cautioned that other measure in the budget will further discourage investment.

So how might this play out? We believe in a future global economy that will be compute rather than labour or capital constrained, and productive use of compute will be critical. Our budget assessment remains that the £100s of billions of new capital investment into the public sector over the parliament will be drawn to productivity and technology improvements, spilling into the wider economy, whilst companies in every sector will need to find significant productivity gains.

Cash-rich large enterprises will self-fund automation initiatives and may accelerate their existing AI plans, while stable mid-sized companies are more likely to pursue targeted automation of their most expensive operations. However, smaller companies and those already struggling financially may find themselves trapped in a cycle of rising labour costs without the capital to invest in solutions. In the “can’t invest” trap, companies facing margin pressure from higher labour costs and borrowing expenses may be forced to reduce output or exit markets entirely. One of the optimal approaches will be to pursue continuous incremental AI automation based on return on investment, accepting lower short-term returns to ensure long-term survival.

The current UK economic pressures bear similarities to Japan’s automation inflection point in the late 1960s. Japan’s experienced 10% annual wage increases and labour shortages and responded succeeded through and national programme of industrial automation or ‘jidoka’, a coordinated industrial policy, capital via the ‘keiretsu’ system, and government-industry alignment. The budget creates similar economic incentives for automation but sadly lacks Japan’s systematic approach to industrial transformation. Japanese companies could access long-term capital and government support to build a world-leading robotics industry, while UK firms face high borrowing costs and a more short-term focused financial system.

One further oversight in the budget is the absence of specific policies addressing data centre capacity and compute infrastructure. As businesses face mounting pressure to automate, the UK’s existing challenges with power grid capacity and planning permissions for data centres could become significant bottlenecks. While the budget allocates £500 million for digital infrastructure and has some planning changes that could benefit data centre build, this doesn’t go nearly far enough to respond to the future demands.

Takeaways: The budget’s combination of labour cost pressures and capital investment availability creates a compelling case for automation, but success will depend on company size, financial strength, and strategy. Large and mid-sized companies with stable cash flows should begin planning phased automation initiatives, while smaller companies need to identify critical processes for highly targeted AI initiatives. The lack of infrastructure policy could create bottlenecks, suggesting businesses should factor ‘compute resilience’ and costs into their automation strategies. The next 12-18 months will likely see increased merger and acquisition activity as larger companies acquire smaller firms with valuable automation potential.